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Fix Parquet test_round_trip to avoid CPU write exception #3203

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Aug 12, 2021
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Fix Parquet test_round_trip to avoid CPU write exception #3203

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51 changes: 36 additions & 15 deletions integration_tests/src/main/python/parquet_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,25 @@ def read_parquet_sql(data_path):
decimal_gens = [DecimalGen(), DecimalGen(precision=7, scale=3), DecimalGen(precision=10, scale=10),
DecimalGen(precision=9, scale=0), DecimalGen(precision=18, scale=15)]

rebase_write_corrected_conf = {
'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED',
'spark.sql.legacy.parquet.int96RebaseModeInWrite': 'CORRECTED'
}

rebase_write_legacy_conf = {
'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'LEGACY',
'spark.sql.legacy.parquet.int96RebaseModeInWrite': 'LEGACY'
}

# Like the standard map_gens_sample but with timestamps limited
parquet_map_gens = [MapGen(f(nullable=False), f()) for f in [
BooleanGen, ByteGen, ShortGen, IntegerGen, LongGen, FloatGen, DoubleGen, DateGen,
lambda nullable=True: TimestampGen(start=datetime(1900, 1, 1, tzinfo=timezone.utc), nullable=nullable)]] +\
[simple_string_to_string_map_gen,
MapGen(StringGen(pattern='key_[0-9]', nullable=False), ArrayGen(string_gen), max_length=10),
MapGen(RepeatSeqGen(IntegerGen(nullable=False), 10), long_gen, max_length=10),
MapGen(StringGen(pattern='key_[0-9]', nullable=False), simple_string_to_string_map_gen)]

parquet_gens_list = [[byte_gen, short_gen, int_gen, long_gen, float_gen, double_gen,
string_gen, boolean_gen, date_gen,
TimestampGen(start=datetime(1900, 1, 1, tzinfo=timezone.utc)), ArrayGen(byte_gen),
Expand All @@ -40,7 +59,7 @@ def read_parquet_sql(data_path):
ArrayGen(ArrayGen(byte_gen)),
StructGen([['child0', ArrayGen(byte_gen)], ['child1', byte_gen], ['child2', float_gen], ['child3', DecimalGen()]]),
ArrayGen(StructGen([['child0', string_gen], ['child1', double_gen], ['child2', int_gen]]))] +
map_gens_sample + decimal_gens,
parquet_map_gens + decimal_gens,
pytest.param([timestamp_gen], marks=pytest.mark.xfail(reason='https://github.com/NVIDIA/spark-rapids/issues/132'))]

# test with original parquet file reader, the multi-file parallel reader for cloud, and coalesce file reader for
Expand All @@ -60,7 +79,7 @@ def test_read_round_trip(spark_tmp_path, parquet_gens, read_func, reader_confs,
data_path = spark_tmp_path + '/PARQUET_DATA'
with_cpu_session(
lambda spark : gen_df(spark, gen_list).write.parquet(data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list, 'spark.sql.legacy.parquet.datetimeRebaseModeInRead': 'CORRECTED'})
# once https://github.com/NVIDIA/spark-rapids/issues/1126 is in we can remove spark.sql.legacy.parquet.datetimeRebaseModeInRead config which is a workaround
Expand Down Expand Up @@ -122,7 +141,7 @@ def test_pred_push_round_trip(spark_tmp_path, parquet_gen, read_func, v1_enabled
s0 = gen_scalar(parquet_gen, force_no_nulls=True)
with_cpu_session(
lambda spark : gen_df(spark, gen_list).orderBy('a').write.parquet(data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
rf = read_func(data_path)
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list})
Expand All @@ -147,6 +166,7 @@ def test_ts_read_round_trip_nested(gen, spark_tmp_path, ts_write, ts_rebase, v1_
with_cpu_session(
lambda spark : unary_op_df(spark, gen).write.parquet(data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': ts_rebase,
'spark.sql.legacy.parquet.int96RebaseModeInWrite': ts_rebase,
'spark.sql.parquet.outputTimestampType': ts_write})
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list})
Expand All @@ -166,6 +186,7 @@ def test_ts_read_round_trip(gen, spark_tmp_path, ts_write, ts_rebase, v1_enabled
with_cpu_session(
lambda spark : unary_op_df(spark, gen).write.parquet(data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': ts_rebase,
'spark.sql.legacy.parquet.int96RebaseModeInWrite': ts_rebase,
'spark.sql.parquet.outputTimestampType': ts_write})
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list})
Expand Down Expand Up @@ -230,7 +251,7 @@ def test_read_round_trip_legacy(spark_tmp_path, parquet_gens, v1_enabled_list, r
data_path = spark_tmp_path + '/PARQUET_DATA'
with_cpu_session(
lambda spark : gen_df(spark, gen_list).write.parquet(data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'LEGACY'})
conf=rebase_write_legacy_conf)
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list})
assert_gpu_and_cpu_are_equal_collect(
Expand All @@ -249,15 +270,15 @@ def test_simple_partitioned_read(spark_tmp_path, v1_enabled_list, reader_confs):
first_data_path = spark_tmp_path + '/PARQUET_DATA/key=0/key2=20'
with_cpu_session(
lambda spark : gen_df(spark, gen_list).write.parquet(first_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'LEGACY'})
conf=rebase_write_legacy_conf)
second_data_path = spark_tmp_path + '/PARQUET_DATA/key=1/key2=21'
with_cpu_session(
lambda spark : gen_df(spark, gen_list).write.parquet(second_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
third_data_path = spark_tmp_path + '/PARQUET_DATA/key=2/key2=22'
with_cpu_session(
lambda spark : gen_df(spark, gen_list).write.parquet(third_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
data_path = spark_tmp_path + '/PARQUET_DATA'
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list})
Expand All @@ -274,11 +295,11 @@ def test_partitioned_read_just_partitions(spark_tmp_path, v1_enabled_list, reade
first_data_path = spark_tmp_path + '/PARQUET_DATA/key=0'
with_cpu_session(
lambda spark : gen_df(spark, gen_list).write.parquet(first_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'LEGACY'})
conf=rebase_write_legacy_conf)
second_data_path = spark_tmp_path + '/PARQUET_DATA/key=1'
with_cpu_session(
lambda spark : gen_df(spark, gen_list).write.parquet(second_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
data_path = spark_tmp_path + '/PARQUET_DATA'
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list})
Expand Down Expand Up @@ -323,12 +344,12 @@ def test_read_merge_schema(spark_tmp_path, v1_enabled_list, reader_confs):
first_data_path = spark_tmp_path + '/PARQUET_DATA/key=0'
with_cpu_session(
lambda spark : gen_df(spark, first_gen_list).write.parquet(first_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'LEGACY'})
conf=rebase_write_legacy_conf)
second_gen_list = [(('_c' if i % 2 == 0 else '_b') + str(i), gen) for i, gen in enumerate(parquet_gens)]
second_data_path = spark_tmp_path + '/PARQUET_DATA/key=1'
with_cpu_session(
lambda spark : gen_df(spark, second_gen_list).write.parquet(second_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
data_path = spark_tmp_path + '/PARQUET_DATA'
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list})
Expand All @@ -348,12 +369,12 @@ def test_read_merge_schema_from_conf(spark_tmp_path, v1_enabled_list, reader_con
first_data_path = spark_tmp_path + '/PARQUET_DATA/key=0'
with_cpu_session(
lambda spark : gen_df(spark, first_gen_list).write.parquet(first_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'LEGACY'})
conf=rebase_write_legacy_conf)
second_gen_list = [(('_c' if i % 2 == 0 else '_b') + str(i), gen) for i, gen in enumerate(parquet_gens)]
second_data_path = spark_tmp_path + '/PARQUET_DATA/key=1'
with_cpu_session(
lambda spark : gen_df(spark, second_gen_list).write.parquet(second_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list,
'spark.sql.parquet.mergeSchema': "true"})
Expand Down Expand Up @@ -416,7 +437,7 @@ def test_small_file_memory(spark_tmp_path, v1_enabled_list):
first_data_path = spark_tmp_path + '/PARQUET_DATA'
with_cpu_session(
lambda spark : gen_df(spark, gen_list).repartition(2000).write.parquet(first_data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
data_path = spark_tmp_path + '/PARQUET_DATA'
assert_gpu_and_cpu_are_equal_collect(
lambda spark : spark.read.parquet(data_path),
Expand Down Expand Up @@ -448,7 +469,7 @@ def test_nested_pruning(spark_tmp_path, data_gen, read_schema, reader_confs, v1_
data_path = spark_tmp_path + '/PARQUET_DATA'
with_cpu_session(
lambda spark : gen_df(spark, data_gen).write.parquet(data_path),
conf={'spark.sql.legacy.parquet.datetimeRebaseModeInWrite': 'CORRECTED'})
conf=rebase_write_corrected_conf)
all_confs = reader_confs.copy()
all_confs.update({'spark.sql.sources.useV1SourceList': v1_enabled_list,
'spark.sql.optimizer.nestedSchemaPruning.enabled': nested_enabled,
Expand Down