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integration_tests/src/main/python/hive_parquet_write_test.py
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| # Copyright (c) 2024, NVIDIA CORPORATION. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| import pytest | ||
|
|
||
| from asserts import assert_gpu_and_cpu_sql_writes_are_equal_collect | ||
| from data_gen import * | ||
| from hive_write_test import _restricted_timestamp | ||
| from marks import allow_non_gpu, ignore_order | ||
| from spark_session import with_cpu_session | ||
|
|
||
| # Disable the meta conversion from Hive write to FrameData write in Spark, to test | ||
| # "GpuInsertIntoHiveTable" for Parquet write. | ||
| _write_to_hive_conf = {"spark.sql.hive.convertMetastoreParquet": False} | ||
|
|
||
| _hive_basic_gens = [ | ||
| byte_gen, short_gen, int_gen, long_gen, float_gen, double_gen, string_gen, boolean_gen, | ||
| DateGen(start=date(1590, 1, 1)), _restricted_timestamp(), | ||
| DecimalGen(precision=19, scale=1, nullable=True), | ||
| DecimalGen(precision=23, scale=5, nullable=True), | ||
| DecimalGen(precision=36, scale=3, nullable=True)] | ||
|
|
||
| _hive_basic_struct_gen = StructGen( | ||
| [['c'+str(ind), c_gen] for ind, c_gen in enumerate(_hive_basic_gens)]) | ||
|
|
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| _hive_struct_gens = [ | ||
| _hive_basic_struct_gen, | ||
| StructGen([['child0', byte_gen], ['child1', _hive_basic_struct_gen]]), | ||
| StructGen([['child0', ArrayGen(short_gen)], ['child1', double_gen]])] | ||
|
|
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| _hive_array_gens = [ArrayGen(sub_gen) for sub_gen in _hive_basic_gens] + [ | ||
| ArrayGen(ArrayGen(short_gen, max_length=10), max_length=10), | ||
| ArrayGen(ArrayGen(string_gen, max_length=10), max_length=10), | ||
| ArrayGen(StructGen([['child0', byte_gen], ['child1', string_gen], ['child2', float_gen]]))] | ||
|
|
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| _hive_map_gens = [simple_string_to_string_map_gen] + [MapGen(f(nullable=False), f()) for f in [ | ||
| BooleanGen, ByteGen, ShortGen, IntegerGen, LongGen, FloatGen, DoubleGen, | ||
| lambda nullable=True: _restricted_timestamp(nullable=nullable), | ||
| lambda nullable=True: DateGen(start=date(1590, 1, 1), nullable=nullable), | ||
| lambda nullable=True: DecimalGen(precision=19, scale=1, nullable=nullable), | ||
| lambda nullable=True: DecimalGen(precision=36, scale=5, nullable=nullable)]] | ||
|
|
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| _hive_write_gens = [_hive_basic_gens, _hive_struct_gens, _hive_array_gens, _hive_map_gens] | ||
|
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|
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| @allow_non_gpu('HiveTableScanExec', *non_utc_allow) | ||
| @ignore_order(local=True) | ||
| @pytest.mark.parametrize("is_ctas", [True, False], ids=['CTAS', 'CTTW']) | ||
| @pytest.mark.parametrize("gens", _hive_write_gens, ids=idfn) | ||
| def test_write_parquet_into_hive_table(spark_tmp_table_factory, gens, is_ctas): | ||
| # Generate hive table in Parquet format | ||
| def gen_hive_table(spark): | ||
| gen_list = [('_c' + str(i), gen) for i, gen in enumerate(gens)] | ||
| data_table = spark_tmp_table_factory.get() | ||
| gen_df(spark, gen_list).createOrReplaceTempView(data_table) | ||
| hive_table = spark_tmp_table_factory.get() | ||
| spark.sql("CREATE TABLE {} STORED AS PARQUET AS SELECT * FROM {}".format( | ||
| hive_table, data_table)) | ||
| return hive_table | ||
|
|
||
| input_table = with_cpu_session(gen_hive_table) | ||
|
|
||
| def write_to_hive_sql(spark, output_table): | ||
| if is_ctas: | ||
| # Create Table As Select | ||
| return [ | ||
| "CREATE TABLE {} STORED AS PARQUET AS SELECT * FROM {}".format( | ||
| output_table, input_table) | ||
| ] | ||
| else: | ||
| # Create Table Then Write | ||
| return [ | ||
| "CREATE TABLE {} LIKE {}".format(output_table, input_table), | ||
| "INSERT OVERWRITE TABLE {} SELECT * FROM {}".format(output_table, input_table) | ||
| ] | ||
|
|
||
| assert_gpu_and_cpu_sql_writes_are_equal_collect( | ||
| spark_tmp_table_factory, | ||
| write_to_hive_sql, | ||
| _write_to_hive_conf) | ||
|
|
||
|
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| @allow_non_gpu('HiveTableScanExec', *non_utc_allow) | ||
| @ignore_order(local=True) | ||
| @pytest.mark.parametrize("is_static", [True, False], ids=['Static_Partition', 'Dynamic_Partition']) | ||
| def test_write_parquet_into_partitioned_hive_table(spark_tmp_table_factory, is_static): | ||
| # Generate hive table in Parquet format | ||
| def gen_hive_table(spark): | ||
| # gen_list = [('_c' + str(i), gen) for i, gen in enumerate(gens)] | ||
| dates = [date(2024, 2, 28), date(2024, 2, 27), date(2024, 2, 26)] | ||
| gen_list = [('a', int_gen), | ||
| ('b', long_gen), | ||
| ('c', short_gen), | ||
| ('d', string_gen), | ||
| ('part', SetValuesGen(DateType(), dates))] | ||
| data_table = spark_tmp_table_factory.get() | ||
| gen_df(spark, gen_list).createOrReplaceTempView(data_table) | ||
| hive_table = spark_tmp_table_factory.get() | ||
| spark.sql("CREATE TABLE {} STORED AS PARQUET AS SELECT * FROM {}".format( | ||
| hive_table, data_table)) | ||
| return hive_table | ||
|
|
||
| input_table = with_cpu_session(gen_hive_table) | ||
|
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||
| def partitioned_write_to_hive_sql(spark, output_table): | ||
| sql_create_part_table = ( | ||
| "CREATE TABLE {} (a INT, b LONG, c SHORT, d STRING) " | ||
| "PARTITIONED BY (part DATE) STORED AS PARQUET" | ||
| ).format(output_table) | ||
| if is_static: | ||
| return [ | ||
| # sql_1: Create partitioned hive table | ||
| sql_create_part_table, | ||
| # sql_2: Static partition write only to partition 'par2' | ||
| "INSERT OVERWRITE TABLE {} PARTITION (part='2024-02-25') " | ||
| "SELECT a, b, c, d FROM {}".format(output_table, input_table) | ||
| ] | ||
| else: | ||
| return [ | ||
| # sql_1: Create partitioned hive table | ||
| sql_create_part_table, | ||
| # sql_2: Dynamic partition write | ||
| "INSERT OVERWRITE TABLE {} SELECT * FROM {}".format(output_table, input_table) | ||
| ] | ||
| all_confs = copy_and_update(_write_to_hive_conf, { | ||
| "hive.exec.dynamic.partition.mode": "nonstrict"}) | ||
| assert_gpu_and_cpu_sql_writes_are_equal_collect( | ||
| spark_tmp_table_factory, | ||
| partitioned_write_to_hive_sql, | ||
| all_confs) | ||
|
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||
|
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| @allow_non_gpu('HiveTableScanExec', *non_utc_allow) | ||
| @ignore_order(local=True) | ||
| @pytest.mark.parametrize("comp_type", ['UNCOMPRESSED', 'ZSTD', 'SNAPPY']) | ||
| def test_write_compressed_parquet_into_hive_table(spark_tmp_table_factory, comp_type): | ||
| # Generate hive table in Parquet format | ||
| def gen_hive_table(spark): | ||
| gens = _hive_basic_gens + _hive_struct_gens + _hive_array_gens + _hive_map_gens | ||
| gen_list = [('_c' + str(i), gen) for i, gen in enumerate(gens)] | ||
| data_table = spark_tmp_table_factory.get() | ||
| gen_df(spark, gen_list).createOrReplaceTempView(data_table) | ||
| hive_table = spark_tmp_table_factory.get() | ||
| spark.sql("CREATE TABLE {} STORED AS PARQUET AS SELECT * FROM {}".format( | ||
| hive_table, data_table)) | ||
| return hive_table | ||
|
|
||
| input_table = with_cpu_session(gen_hive_table) | ||
|
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| def write_to_hive_sql(spark, output_table): | ||
| return [ | ||
| "CREATE TABLE {} LIKE {} TBLPROPERTIES ('parquet.compression'='{}')".format( | ||
| output_table, input_table, comp_type), | ||
| "INSERT OVERWRITE TABLE {} SELECT * FROM {}".format(output_table, input_table) | ||
| ] | ||
|
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||
| assert_gpu_and_cpu_sql_writes_are_equal_collect( | ||
| spark_tmp_table_factory, | ||
| write_to_hive_sql, | ||
| _write_to_hive_conf) |
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