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parquet_reader_options.cpp
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parquet_reader_options.cpp
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/*
* Copyright (c) 2022-2023, 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.
*/
#include <benchmarks/common/generate_input.hpp>
#include <benchmarks/fixture/benchmark_fixture.hpp>
#include <benchmarks/io/cuio_common.hpp>
#include <benchmarks/io/nvbench_helpers.hpp>
#include <cudf/detail/utilities/integer_utils.hpp>
#include <cudf/io/parquet.hpp>
#include <cudf/utilities/default_stream.hpp>
#include <nvbench/nvbench.cuh>
// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
// run on most GPUs, but large enough to allow highest throughput
constexpr std::size_t data_size = 512 << 20;
// The number of separate read calls to use when reading files in multiple chunks
// Each call reads roughly equal amounts of data
constexpr int32_t chunked_read_num_chunks = 4;
std::vector<std::string> get_top_level_col_names(cudf::io::source_info const& source)
{
auto const top_lvl_cols = cudf::io::read_parquet_metadata(source).schema().root().children();
std::vector<std::string> col_names;
std::transform(top_lvl_cols.cbegin(),
top_lvl_cols.cend(),
std::back_inserter(col_names),
[](auto const& col_meta) { return col_meta.name(); });
return col_names;
}
template <column_selection ColSelection,
row_selection RowSelection,
converts_strings ConvertsStrings,
uses_pandas_metadata UsesPandasMetadata,
cudf::type_id Timestamp>
void BM_parquet_read_options(nvbench::state& state,
nvbench::type_list<nvbench::enum_type<ColSelection>,
nvbench::enum_type<RowSelection>,
nvbench::enum_type<ConvertsStrings>,
nvbench::enum_type<UsesPandasMetadata>,
nvbench::enum_type<Timestamp>>)
{
auto const num_chunks = RowSelection == row_selection::ALL ? 1 : chunked_read_num_chunks;
auto constexpr str_to_categories = ConvertsStrings == converts_strings::YES;
auto constexpr uses_pd_metadata = UsesPandasMetadata == uses_pandas_metadata::YES;
auto const ts_type = cudf::data_type{Timestamp};
auto const data_types =
dtypes_for_column_selection(get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
static_cast<int32_t>(data_type::FLOAT),
static_cast<int32_t>(data_type::DECIMAL),
static_cast<int32_t>(data_type::TIMESTAMP),
static_cast<int32_t>(data_type::DURATION),
static_cast<int32_t>(data_type::STRING),
static_cast<int32_t>(data_type::LIST),
static_cast<int32_t>(data_type::STRUCT)}),
ColSelection);
auto const tbl = create_random_table(data_types, table_size_bytes{data_size});
auto const view = tbl->view();
cuio_source_sink_pair source_sink(io_type::HOST_BUFFER);
cudf::io::parquet_writer_options options =
cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view);
cudf::io::write_parquet(options);
auto const cols_to_read =
select_column_names(get_top_level_col_names(source_sink.make_source_info()), ColSelection);
cudf::size_type const expected_num_cols = cols_to_read.size();
cudf::io::parquet_reader_options read_options =
cudf::io::parquet_reader_options::builder(source_sink.make_source_info())
.columns(cols_to_read)
.convert_strings_to_categories(str_to_categories)
.use_pandas_metadata(uses_pd_metadata)
.timestamp_type(ts_type);
auto const num_row_groups = read_parquet_metadata(source_sink.make_source_info()).num_rowgroups();
auto const chunk_row_cnt = cudf::util::div_rounding_up_unsafe(view.num_rows(), num_chunks);
auto mem_stats_logger = cudf::memory_stats_logger();
state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
state.exec(
nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
try_drop_l3_cache();
cudf::size_type num_rows_read = 0;
timer.start();
for (int32_t chunk = 0; chunk < num_chunks; ++chunk) {
switch (RowSelection) {
case row_selection::ALL: break;
case row_selection::ROW_GROUPS: {
read_options.set_row_groups({segments_in_chunk(num_row_groups, num_chunks, chunk)});
} break;
case row_selection::NROWS:
read_options.set_skip_rows(chunk * chunk_row_cnt);
read_options.set_num_rows(chunk_row_cnt);
break;
default: CUDF_FAIL("Unsupported row selection method");
}
auto const result = cudf::io::read_parquet(read_options);
num_rows_read += result.tbl->num_rows();
CUDF_EXPECTS(result.tbl->num_columns() == expected_num_cols,
"Unexpected number of columns");
}
timer.stop();
CUDF_EXPECTS(num_rows_read == view.num_rows(), "Benchmark did not read the entire table");
});
auto const elapsed_time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
auto const data_processed = data_size * cols_to_read.size() / view.num_columns();
state.add_element_count(static_cast<double>(data_processed) / elapsed_time, "bytes_per_second");
state.add_buffer_size(
mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
}
using row_selections =
nvbench::enum_type_list<row_selection::ALL, row_selection::NROWS, row_selection::ROW_GROUPS>;
NVBENCH_BENCH_TYPES(BM_parquet_read_options,
NVBENCH_TYPE_AXES(nvbench::enum_type_list<column_selection::ALL>,
row_selections,
nvbench::enum_type_list<converts_strings::YES>,
nvbench::enum_type_list<uses_pandas_metadata::YES>,
nvbench::enum_type_list<cudf::type_id::EMPTY>))
.set_name("parquet_read_row_selection")
.set_type_axes_names({"column_selection",
"row_selection",
"str_to_categories",
"uses_pandas_metadata",
"timestamp_type"})
.set_min_samples(4);
using col_selections = nvbench::enum_type_list<column_selection::ALL,
column_selection::ALTERNATE,
column_selection::FIRST_HALF,
column_selection::SECOND_HALF>;
NVBENCH_BENCH_TYPES(BM_parquet_read_options,
NVBENCH_TYPE_AXES(col_selections,
nvbench::enum_type_list<row_selection::ALL>,
nvbench::enum_type_list<converts_strings::YES>,
nvbench::enum_type_list<uses_pandas_metadata::YES>,
nvbench::enum_type_list<cudf::type_id::EMPTY>))
.set_name("parquet_read_column_selection")
.set_type_axes_names({"column_selection",
"row_selection",
"str_to_categories",
"uses_pandas_metadata",
"timestamp_type"})
.set_min_samples(4);
NVBENCH_BENCH_TYPES(
BM_parquet_read_options,
NVBENCH_TYPE_AXES(nvbench::enum_type_list<column_selection::ALL>,
nvbench::enum_type_list<row_selection::ALL>,
nvbench::enum_type_list<converts_strings::YES, converts_strings::NO>,
nvbench::enum_type_list<uses_pandas_metadata::YES, uses_pandas_metadata::NO>,
nvbench::enum_type_list<cudf::type_id::EMPTY>))
.set_name("parquet_read_misc_options")
.set_type_axes_names({"column_selection",
"row_selection",
"str_to_categories",
"uses_pandas_metadata",
"timestamp_type"})
.set_min_samples(4);