Drop pyorc dependency and use pandas/pyarrow instead

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -70,7 +70,6 @@ dependencies:
 - ptxcompiler
 - pyarrow==12.0.1.*
 - pydata-sphinx-theme
-- pyorc
 - pytest
 - pytest-benchmark
 - pytest-cases

conda/environments/all_cuda-120_arch-x86_64.yaml

@@ -68,7 +68,6 @@ dependencies:
 - protobuf>=4.21,<5
 - pyarrow==12.0.1.*
 - pydata-sphinx-theme
-- pyorc
 - pytest
 - pytest-benchmark
 - pytest-cases

cpp/tests/io/orc_test.cpp

@@ -1299,20 +1299,16 @@ TEST_F(OrcStatisticsTest, Overflow)
 
 TEST_F(OrcStatisticsTest, HasNull)
 {
-  // This test can now be implemented with libcudf; keeping the pyorc version to keep the test
+  // This test can now be implemented with libcudf; keeping the pandas version to keep the test
   // inputs diversified
   // Method to create file:
-  // >>> import pyorc
-  // >>> output = open("./temp.orc", "wb")
-  // >>> writer = pyorc.Writer(output, pyorc.Struct(a=pyorc.BigInt(), b=pyorc.BigInt()))
-  // >>> writer.write((1, 3))
-  // >>> writer.write((2, 4))
-  // >>> writer.write((None, 5))
-  // >>> writer.close()
+  // >>> import pandas as pd
+  // >>> df = pd.DataFrame({'a':pd.Series([1, 2, None], dtype="Int64"), 'b':[3, 4, 5]})
+  // >>> df.to_orc("temp.orc")
   //
   // Contents of file:
   // >>> import pyarrow.orc as po
-  // >>> po.ORCFile('new.orc').read()
+  // >>> po.ORCFile('temp.orc').read()
   // pyarrow.Table
   // a: int64
   // b: int64

dependencies.yaml

@@ -548,7 +548,6 @@ dependencies:
           - fastavro>=0.22.9
           - hypothesis
           - mimesis>=4.1.0
-          - pyorc
           - pytest-benchmark
           - pytest-cases
           - python-snappy>=0.6.0

python/cudf/cudf/_fuzz_testing/orc.py

@@ -1,19 +1,19 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
 
 import copy
 import io
 import logging
 import random
 
 import numpy as np
-import pyorc
+import pyarrow.orc
+import pyarrow as pa
 
 import cudf
 from cudf._fuzz_testing.io import IOFuzz
 from cudf._fuzz_testing.utils import (
     ALL_POSSIBLE_VALUES,
     _generate_rand_meta,
-    pandas_to_orc,
     pyarrow_to_pandas,
 )
 from cudf.testing import dataset_generator as dg
@@ -82,12 +82,7 @@ def generate_input(self):
         logging.info(f"Shape of DataFrame generated: {table.shape}")
         self._df = df
         file_obj = io.BytesIO()
-        pandas_to_orc(
-            df,
-            file_io_obj=file_obj,
-            stripe_size=self._rand(len(df)),
-            arrow_table_schema=table.schema,
-        )
+        pa.orc.write_table(table, file_obj, stripe_size=self._rand(len(df)))
         file_obj.seek(0)
         buf = file_obj.read()
         self._current_buffer = copy.copy(buf)
@@ -109,8 +104,8 @@ def set_rand_params(self, params):
                     )
                 elif param == "stripes":
                     f = io.BytesIO(self._current_buffer)
-                    reader = pyorc.Reader(f)
-                    stripes = [i for i in range(reader.num_of_stripes)]
+                    orcFile = pa.orc.ORCFile(f)
+                    stripes = [i for i in range(orcFile.nstripes)]
                     params_dict[param] = np.random.choice(
                         [
                             None,
@@ -119,7 +114,7 @@ def set_rand_params(self, params):
                                     int,
                                     np.unique(
                                         np.random.choice(
-                                            stripes, reader.num_of_stripes
+                                            stripes, orcFile.nstripes
                                         )
                                     ),
                                 )

python/cudf/cudf/_fuzz_testing/utils.py

@@ -1,13 +1,11 @@
 # Copyright (c) 2020-2023, NVIDIA CORPORATION.
 
 import random
-from collections import OrderedDict
 
 import fastavro
 import numpy as np
 import pandas as pd
 import pyarrow as pa
-import pyorc
 
 import cudf
 from cudf.testing._utils import assert_eq
@@ -41,40 +39,6 @@
     cudf.dtype("<M8[us]"): {"type": "long", "logicalType": "timestamp-micros"},
 }
 
-PANDAS_TO_ORC_TYPES = {
-    cudf.dtype("int8"): pyorc.TinyInt(),
-    pd.Int8Dtype(): pyorc.TinyInt(),
-    pd.Int16Dtype(): pyorc.SmallInt(),
-    pd.Int32Dtype(): pyorc.Int(),
-    pd.Int64Dtype(): pyorc.BigInt(),
-    pd.Float32Dtype(): pyorc.Float(),
-    pd.Float64Dtype(): pyorc.Double(),
-    pd.BooleanDtype(): pyorc.Boolean(),
-    cudf.dtype("bool_"): pyorc.Boolean(),
-    cudf.dtype("int16"): pyorc.SmallInt(),
-    cudf.dtype("int32"): pyorc.Int(),
-    cudf.dtype("int64"): pyorc.BigInt(),
-    cudf.dtype("O"): pyorc.String(),
-    pd.StringDtype(): pyorc.String(),
-    cudf.dtype("float32"): pyorc.Float(),
-    cudf.dtype("float64"): pyorc.Double(),
-    cudf.dtype("<M8[ns]"): pyorc.Timestamp(),
-    cudf.dtype("<M8[ms]"): pyorc.Timestamp(),
-    cudf.dtype("<M8[us]"): pyorc.Timestamp(),
-}
-
-ORC_TO_PANDAS_TYPES = {
-    pyorc.TinyInt().name: pd.Int8Dtype(),
-    pyorc.Int().name: pd.Int32Dtype(),
-    pyorc.Boolean().name: pd.BooleanDtype(),
-    pyorc.SmallInt().name: pd.Int16Dtype(),
-    pyorc.BigInt().name: pd.Int64Dtype(),
-    pyorc.String().name: pd.StringDtype(),
-    pyorc.Float().name: pd.Float32Dtype(),
-    pyorc.Double().name: pd.Float64Dtype(),
-    pyorc.Timestamp().name: cudf.dtype("<M8[ns]"),
-}
-
 
 def _generate_rand_meta(obj, dtypes_list, null_frequency_override=None):
     obj._current_params = {}
@@ -213,24 +177,6 @@ def get_avro_dtype_info(dtype):
         )
 
 
-def get_orc_dtype_info(dtype):
-    if dtype in PANDAS_TO_ORC_TYPES:
-        return PANDAS_TO_ORC_TYPES[dtype]
-    else:
-        raise TypeError(
-            f"Unsupported dtype({dtype}) according to orc spec:"
-            f" https://orc.apache.org/specification/"
-        )
-
-
-def get_arrow_dtype_info_for_pyorc(dtype):
-    if isinstance(dtype, pa.StructType):
-        return get_orc_schema(df=None, arrow_table_schema=dtype)
-    else:
-        pd_dtype = cudf.dtype(dtype.to_pandas_dtype())
-        return get_orc_dtype_info(pd_dtype)
-
-
 def get_avro_schema(df):
     fields = [
         {"name": col_name, "type": get_avro_dtype_info(col_dtype)}
@@ -240,22 +186,6 @@ def get_avro_schema(df):
     return schema
 
 
-def get_orc_schema(df, arrow_table_schema=None):
-    if arrow_table_schema is None:
-        ordered_dict = OrderedDict(
-            (col_name, get_orc_dtype_info(col_dtype))
-            for col_name, col_dtype in df.dtypes.items()
-        )
-    else:
-        ordered_dict = OrderedDict(
-            (field.name, get_arrow_dtype_info_for_pyorc(field.type))
-            for field in arrow_table_schema
-        )
-
-    schema = pyorc.Struct(**ordered_dict)
-    return schema
-
-
 def convert_nulls_to_none(records, df):
     columns_with_nulls = {col for col in df.columns if df[col].isnull().any()}
     scalar_columns_convert = [
@@ -296,99 +226,19 @@ def pandas_to_avro(df, file_name=None, file_io_obj=None):
         fastavro.writer(file_io_obj, avro_schema, records)
 
 
-def _preprocess_to_orc_tuple(df, arrow_table_schema):
-    def _null_to_None(value):
-        if value is pd.NA or value is pd.NaT:
-            return None
-        else:
-            return value
-
-    def sanitize(value, struct_type):
-        if value is None:
-            return None
-
-        values_list = []
-        for name, sub_type in struct_type.fields.items():
-            if isinstance(sub_type, cudf.StructDtype):
-                values_list.append(sanitize(value[name], sub_type))
-            else:
-                values_list.append(value[name])
-        return tuple(values_list)
-
-    has_nulls_or_nullable_dtype = any(
-        (col := df[colname]).dtype in pandas_dtypes_to_np_dtypes
-        or col.isnull().any()
-        for colname in df.columns
-    )
-    pdf = df.copy(deep=True)
-    for field in arrow_table_schema:
-        if isinstance(field.type, pa.StructType):
-            pdf[field.name] = pdf[field.name].apply(
-                sanitize, args=(cudf.StructDtype.from_arrow(field.type),)
-            )
-        else:
-            pdf[field.name] = pdf[field.name]
-
-    tuple_list = [
-        tuple(map(_null_to_None, tup)) if has_nulls_or_nullable_dtype else tup
-        for tup in pdf.itertuples(index=False, name=None)
-    ]
-
-    return tuple_list, pdf, df
-
-
-def pandas_to_orc(
-    df,
-    file_name=None,
-    file_io_obj=None,
-    stripe_size=67108864,
-    arrow_table_schema=None,
-):
-    schema = get_orc_schema(df, arrow_table_schema=arrow_table_schema)
-
-    tuple_list, pdf, df = _preprocess_to_orc_tuple(
-        df, arrow_table_schema=arrow_table_schema
-    )
-
-    if file_name is not None:
-        with open(file_name, "wb") as data:
-            with pyorc.Writer(data, schema, stripe_size=stripe_size) as writer:
-                writer.writerows(tuple_list)
-    elif file_io_obj is not None:
-        with pyorc.Writer(
-            file_io_obj, schema, stripe_size=stripe_size
-        ) as writer:
-            writer.writerows(tuple_list)
-
-
 def orc_to_pandas(file_name=None, file_io_obj=None, stripes=None):
     if file_name is not None:
         f = open(file_name, "rb")
     elif file_io_obj is not None:
         f = file_io_obj
 
-    reader = pyorc.Reader(f)
-
-    dtypes = {
-        col: ORC_TO_PANDAS_TYPES[pyorc_type.name]
-        for col, pyorc_type in reader.schema.fields.items()
-    }
-
     if stripes is None:
-        df = pd.DataFrame.from_records(
-            reader, columns=reader.schema.fields.keys()
-        )
+        df = pd.read_orc(f)
     else:
-        records = [
-            record for i in stripes for record in list(reader.read_stripe(i))
-        ]
-        df = pd.DataFrame.from_records(
-            records, columns=reader.schema.fields.keys()
-        )
-
-    # Need to type-cast to extracted `dtypes` from pyorc schema because
-    # a fully empty/ full <NA> can result in incorrect dtype by pandas.
-    df = df.astype(dtypes)
+        orc_file = pa.orc.ORCFile(f)
+        records = [orc_file.read_stripe(i) for i in stripes]
+        pa_table = pa.Table.from_batches(records)
+        df = pa_table.to_pandas()
 
     return df