Expose streams in Parquet reader and writer APIs #14359
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| /* | ||
| * Copyright (c) 2023-2024, 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. | ||
| */ | ||
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| #include <cudf/io/detail/parquet.hpp> | ||
| #include <cudf/io/parquet.hpp> | ||
| #include <cudf/table/table.hpp> | ||
| #include <cudf/table/table_view.hpp> | ||
| #include <cudf/types.hpp> | ||
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| #include <cudf_test/base_fixture.hpp> | ||
| #include <cudf_test/column_wrapper.hpp> | ||
| #include <cudf_test/default_stream.hpp> | ||
| #include <cudf_test/iterator_utilities.hpp> | ||
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| #include <string> | ||
| #include <vector> | ||
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| // Global environment for temporary files | ||
| auto const temp_env = static_cast<cudf::test::TempDirTestEnvironment*>( | ||
| ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment)); | ||
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| class ParquetTest : public cudf::test::BaseFixture {}; | ||
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| template <typename... UniqPtrs> | ||
| std::vector<std::unique_ptr<cudf::column>> make_uniqueptrs_vector(UniqPtrs&&... uniqptrs) | ||
| { | ||
| std::vector<std::unique_ptr<cudf::column>> ptrsvec; | ||
| (ptrsvec.push_back(std::forward<UniqPtrs>(uniqptrs)), ...); | ||
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There was a problem hiding this comment. omg, a fold expression |
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| return ptrsvec; | ||
| } | ||
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| cudf::table construct_table() | ||
| { | ||
| constexpr auto num_rows = 10; | ||
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| std::vector<size_t> zeros(num_rows, 0); | ||
| std::vector<size_t> ones(num_rows, 1); | ||
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| cudf::test::fixed_width_column_wrapper<bool> col0(zeros.begin(), zeros.end()); | ||
| cudf::test::fixed_width_column_wrapper<int8_t> col1(zeros.begin(), zeros.end()); | ||
| cudf::test::fixed_width_column_wrapper<int16_t> col2(zeros.begin(), zeros.end()); | ||
| cudf::test::fixed_width_column_wrapper<int32_t> col3(zeros.begin(), zeros.end()); | ||
| cudf::test::fixed_width_column_wrapper<float> col4(zeros.begin(), zeros.end()); | ||
| cudf::test::fixed_width_column_wrapper<double> col5(zeros.begin(), zeros.end()); | ||
| cudf::test::fixed_width_column_wrapper<numeric::decimal128> col6 = [&ones, num_rows] { | ||
| auto col6_data = cudf::detail::make_counting_transform_iterator(0, [&](auto i) { | ||
| return numeric::decimal128{ones[i], numeric::scale_type{12}}; | ||
| }); | ||
| return cudf::test::fixed_width_column_wrapper<numeric::decimal128>(col6_data, | ||
| col6_data + num_rows); | ||
| }(); | ||
| cudf::test::fixed_width_column_wrapper<numeric::decimal128> col7 = [&ones, num_rows] { | ||
| auto col7_data = cudf::detail::make_counting_transform_iterator(0, [&](auto i) { | ||
| return numeric::decimal128{ones[i], numeric::scale_type{-12}}; | ||
| }); | ||
| return cudf::test::fixed_width_column_wrapper<numeric::decimal128>(col7_data, | ||
| col7_data + num_rows); | ||
| }(); | ||
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| cudf::test::lists_column_wrapper<int64_t> col8{ | ||
| {1, 1}, {1, 1, 1}, {}, {1}, {1, 1, 1, 1}, {1, 1, 1, 1, 1}, {}, {1, -1}, {}, {-1, -1}}; | ||
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| cudf::test::structs_column_wrapper col9 = [&ones] { | ||
| cudf::test::fixed_width_column_wrapper<int32_t> child_col(ones.begin(), ones.end()); | ||
| return cudf::test::structs_column_wrapper{child_col}; | ||
| }(); | ||
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| cudf::test::strings_column_wrapper col10 = [] { | ||
| std::vector<std::string> col10_data(num_rows, "rapids"); | ||
| return cudf::test::strings_column_wrapper(col10_data.begin(), col10_data.end()); | ||
| }(); | ||
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| auto colsptr = make_uniqueptrs_vector(col0.release(), | ||
| col1.release(), | ||
| col2.release(), | ||
| col3.release(), | ||
| col4.release(), | ||
| col5.release(), | ||
| col6.release(), | ||
| col7.release(), | ||
| col8.release(), | ||
| col9.release(), | ||
| col10.release()); | ||
| return cudf::table(std::move(colsptr)); | ||
| } | ||
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| TEST_F(ParquetTest, ParquetWriter) | ||
| { | ||
| auto tab = construct_table(); | ||
| auto filepath = temp_env->get_temp_filepath("MultiColumn.parquet"); | ||
| cudf::io::parquet_writer_options out_opts = | ||
| cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tab); | ||
| cudf::io::write_parquet(out_opts, cudf::test::get_default_stream()); | ||
| } | ||
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| TEST_F(ParquetTest, ParquetReader) | ||
| { | ||
| auto tab = construct_table(); | ||
| auto filepath = temp_env->get_temp_filepath("MultiColumn.parquet"); | ||
| cudf::io::parquet_writer_options out_opts = | ||
| cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tab); | ||
| cudf::io::write_parquet(out_opts, cudf::test::get_default_stream()); | ||
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| cudf::io::parquet_reader_options in_opts = | ||
| cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath}); | ||
| auto result = cudf::io::read_parquet(in_opts, cudf::test::get_default_stream()); | ||
| auto meta = cudf::io::read_parquet_metadata(cudf::io::source_info{filepath}); | ||
| } | ||
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| TEST_F(ParquetTest, ChunkedOperations) | ||
| { | ||
| auto tab = construct_table(); | ||
| auto filepath = temp_env->get_temp_filepath("MultiColumn.parquet"); | ||
| cudf::io::chunked_parquet_writer_options out_opts = | ||
| cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath}); | ||
| cudf::io::parquet_chunked_writer(out_opts, cudf::test::get_default_stream()).write(tab); | ||
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| auto reader = cudf::io::chunked_parquet_reader( | ||
| 1L << 31, | ||
| cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath}), | ||
| cudf::test::get_default_stream()); | ||
| while (reader.has_next()) { | ||
| auto chunk = reader.read_chunk(); | ||
| } | ||
| } | ||
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The PQ reader launches a separate kernel for many different encoding types; in theory we need to cover all those kernels. But, I think we can ignore this for now, since it requires carefully crafting tests that lead to specific encodings.
CC @nvdbaranec @etseidl for viz (short for wizardry)