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Spark 4: Fix parquet_test.py [databricks] #11519

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merged 2 commits into from
Oct 8, 2024
Merged

Spark 4: Fix parquet_test.py [databricks] #11519

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b712c2d
Spark 4: Fix parquet_test.py.
mythrocks Sep 26, 2024
1812ead
Also fix #11531.
mythrocks Oct 4, 2024
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62 changes: 55 additions & 7 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 @@ -485,6 +485,8 @@ def test_parquet_read_buffer_allocation_empty_blocks(spark_tmp_path, v1_enabled_
lambda spark : spark.read.parquet(data_path).filter("id < 2 or id > 990"),
conf=all_confs)


@disable_ansi_mode # https://github.com/NVIDIA/spark-rapids/issues/5114
@pytest.mark.parametrize('reader_confs', reader_opt_confs)
@pytest.mark.parametrize('v1_enabled_list', ["", "parquet"])
@pytest.mark.skipif(is_databricks_runtime(), reason="https://github.com/NVIDIA/spark-rapids/issues/7733")
Expand Down Expand Up @@ -797,6 +799,8 @@ def test_parquet_read_nano_as_longs_true(std_input_path):
'FileSourceScanExec',
conf=conf)


@disable_ansi_mode # https://github.com/NVIDIA/spark-rapids/issues/5114
def test_many_column_project():
def _create_wide_data_frame(spark, num_cols):
schema_dict = {}
Expand Down Expand Up @@ -1285,27 +1289,64 @@ def test_parquet_read_case_insensitivity(spark_tmp_path):
)


# test read INT32 as INT8/INT16/Date
@pytest.mark.parametrize('reader_confs', reader_opt_confs)
@pytest.mark.parametrize('v1_enabled_list', ["", "parquet"])
def test_parquet_int32_downcast(spark_tmp_path, reader_confs, v1_enabled_list):
def run_test_parquet_int32_downcast(spark_tmp_path,
reader_confs,
v1_enabled_list,
ansi_conf):
"""
This tests whether Parquet files with columns written as INT32 can be
read as having INT8, INT16 and DATE columns, with ANSI mode enabled/disabled.
"""
data_path = spark_tmp_path + '/PARQUET_DATA'
write_schema = [("d", date_gen), ('s', short_gen), ('b', byte_gen)]

# For test setup, write with ANSI disabled.
# Otherwise, CAST(d AS INT) will fail on Spark CPU.
with_cpu_session(
lambda spark: gen_df(spark, write_schema).selectExpr(
"cast(d as Int) as d",
"cast(s as Int) as s",
"cast(b as Int) as b").write.parquet(data_path))
"cast(b as Int) as b").write.parquet(data_path), conf=ansi_disabled_conf)

read_schema = StructType([StructField("d", DateType()),
StructField("s", ShortType()),
StructField("b", ByteType())])
conf = copy_and_update(reader_confs,
{'spark.sql.sources.useV1SourceList': v1_enabled_list})
{'spark.sql.sources.useV1SourceList': v1_enabled_list},
ansi_conf)
assert_gpu_and_cpu_are_equal_collect(
lambda spark: spark.read.schema(read_schema).parquet(data_path),
conf=conf)


@pytest.mark.parametrize('reader_confs', reader_opt_confs)
@pytest.mark.parametrize('v1_enabled_list', ["", "parquet"])
def test_parquet_int32_downcast_ansi_disabled(spark_tmp_path, reader_confs, v1_enabled_list):
"""
This tests whether Parquet files with columns written as INT32 can be
read as having INT8, INT16 and DATE columns, with ANSI mode disabled.
"""
run_test_parquet_int32_downcast(spark_tmp_path,
reader_confs,
v1_enabled_list,
ansi_disabled_conf)


def test_parquet_int32_downcast_ansi_enabled(spark_tmp_path):
"""
This is the flipside of test_parquet_int32_downcast_ansi_disabled.
This tests whether Parquet files with columns written as INT32 can be
read as having INT8, INT16 and DATE columns, now tested with ANSI
enabled.
A limited combination of test parameters is used to test ANSI enabled,
in the interest of brevity.
"""
run_test_parquet_int32_downcast(spark_tmp_path,
reader_confs=native_parquet_file_reader_conf,
v1_enabled_list="",
ansi_conf=ansi_disabled_conf)


@pytest.mark.parametrize('reader_confs', reader_opt_confs)
@pytest.mark.parametrize('v1_enabled_list', ["", "parquet"])
@pytest.mark.parametrize("types", [("byte", "short"), ("byte", "int"), ("short", "int")], ids=idfn)
Expand Down Expand Up @@ -1340,6 +1381,10 @@ def test_parquet_nested_column_missing(spark_tmp_path, reader_confs, v1_enabled_
lambda spark: spark.read.schema(read_schema).parquet(data_path),
conf=conf)

@pytest.mark.skipif(condition=is_databricks_runtime() and is_databricks_version_or_later(14,3),
reason="https://github.com/NVIDIA/spark-rapids/issues/11512")
@pytest.mark.skipif(condition=is_spark_400_or_later(),
reason="https://github.com/NVIDIA/spark-rapids/issues/11512")
def test_parquet_check_schema_compatibility(spark_tmp_path):
data_path = spark_tmp_path + '/PARQUET_DATA'
gen_list = [('int', int_gen), ('long', long_gen), ('dec32', decimal_gen_32bit)]
Expand Down Expand Up @@ -1431,13 +1476,16 @@ def test_parquet_read_encryption(spark_tmp_path, reader_confs, v1_enabled_list):
assert_spark_exception(
lambda: with_gpu_session(
lambda spark: spark.read.parquet(data_path).collect()),
error_message='Could not read footer for file')
error_message='Could not read footer') # Common message fragment between all Spark versions.
# Note that this isn't thrown explicitly by the plugin.

assert_spark_exception(
lambda: with_gpu_session(
lambda spark: spark.read.parquet(data_path).collect(), conf=conf),
error_message='The GPU does not support reading encrypted Parquet files')


@disable_ansi_mode # https://github.com/NVIDIA/spark-rapids/issues/5114
def test_parquet_read_count(spark_tmp_path):
parquet_gens = [int_gen, string_gen, double_gen]
gen_list = [('_c' + str(i), gen) for i, gen in enumerate(parquet_gens)]
Expand Down