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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2020/06/05 17:56:05 UTC
[GitHub] [arrow] wesm opened a new pull request #7358: ARROW-9045: [C++] Expand / improve Take and Filter benchmarks for enhanced baseline
wesm opened a new pull request #7358:
URL: https://github.com/apache/arrow/pull/7358
The idea of this patch is to provide a more comprehensive baseline for the optimization work I'm undertaking.
Summary:
* Benchmark take when indices are monotonic and contain no nulls. Monotonic takes perform much faster because it accesses memory consecutively rather than at random
* Test null percentages down to 0.01% (1% is even a lot of nulls, and obscures behavior between 1% and 0%).
* Benchmark indices/filter-mask with and without nulls, because there may be faster code paths for the no-nulls case
* Benchmark when values being taken/filtered are all not null
* Benchmark filtering/taking smaller strings. The benchmarks were doing strings of size 0 to 128 -- realistic workloads generally will be working with smaller strings, so I set a range instead of 0 to 32 with 16 the average
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[GitHub] [arrow] wesm commented on a change in pull request #7358: ARROW-9045: [C++] Expand / improve Take and Filter benchmarks for enhanced baseline
Posted by GitBox <gi...@apache.org>.
wesm commented on a change in pull request #7358:
URL: https://github.com/apache/arrow/pull/7358#discussion_r436103434
##########
File path: cpp/src/arrow/compute/kernels/vector_selection_benchmark.cc
##########
@@ -0,0 +1,291 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you 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 "benchmark/benchmark.h"
+
+#include "arrow/compute/api_vector.h"
+#include "arrow/compute/benchmark_util.h"
+#include "arrow/compute/kernels/test_util.h"
+#include "arrow/testing/gtest_util.h"
+#include "arrow/testing/random.h"
+
+namespace arrow {
+namespace compute {
+
+constexpr auto kSeed = 0x0ff1ce;
+
+struct FilterParams {
+ // proportion of nulls in the values array
+ const double values_null_proportion;
+
+ // proportion of true in filter
+ const double selected_proportion;
+
+ // proportion of nulls in the filter
+ const double filter_null_proportion;
+};
+
+std::vector<int64_t> g_data_sizes = {kL1Size, 1 << 20};
+
+// The benchmark state parameter references this vector of cases. Test high and
+// low selectivity filters.
+std::vector<FilterParams> g_filter_params = {
+ {0., 0.95, 0.05}, {0., 0.10, 0.05}, {0.001, 0.95, 0.05}, {0.001, 0.10, 0.05},
+ {0.01, 0.95, 0.05}, {0.01, 0.10, 0.05}, {0.1, 0.95, 0.05}, {0.1, 0.10, 0.05},
+ {0.9, 0.95, 0.05}, {0.9, 0.10, 0.05}};
+
+// RAII struct to handle some of the boilerplate in filter
+struct FilterArgs {
+ // size of memory tested (per iteration) in bytes
+ const int64_t size;
+
+ double values_null_proportion = 0.;
+ double selected_proportion = 0.;
+ double filter_null_proportion = 0.;
+
+ FilterArgs(benchmark::State& state, bool filter_has_nulls)
+ : size(state.range(0)), state_(state) {
+ auto params = g_filter_params[state.range(1)];
+ values_null_proportion = params.values_null_proportion;
+ selected_proportion = params.selected_proportion;
+ filter_null_proportion = filter_has_nulls ? params.filter_null_proportion : 0;
+ }
+
+ ~FilterArgs() {
+ state_.counters["size"] = static_cast<double>(size);
+ state_.counters["select%"] = selected_proportion * 100;
+ state_.counters["data null%"] = values_null_proportion * 100;
+ state_.counters["mask null%"] = filter_null_proportion * 100;
+ state_.SetBytesProcessed(state_.iterations() * size);
+ }
+
+ private:
+ benchmark::State& state_;
+};
+
+struct TakeBenchmark {
+ benchmark::State& state;
+ RegressionArgs args;
+ random::RandomArrayGenerator rand;
+ bool indices_have_nulls;
+ bool monotonic_indices = false;
+
+ TakeBenchmark(benchmark::State& state, bool indices_have_nulls,
+ bool monotonic_indices = false)
+ : state(state),
+ args(state),
+ rand(kSeed),
+ indices_have_nulls(indices_have_nulls),
+ monotonic_indices(false) {}
+
+ void Int64() {
+ const int64_t array_size = args.size / sizeof(int64_t);
+ auto values = rand.Int64(array_size, -100, 100, args.null_proportion);
+ Bench(values);
+ }
+
+ void FSLInt64() {
+ const int64_t array_size = args.size / sizeof(int64_t);
+ auto int_array = rand.Int64(array_size, -100, 100, args.null_proportion);
+ auto values = std::make_shared<FixedSizeListArray>(
+ fixed_size_list(int64(), 1), array_size, int_array, int_array->null_bitmap(),
+ int_array->null_count());
+ Bench(values);
+ }
+
+ void String() {
+ int32_t string_min_length = 0, string_max_length = 32;
+ int32_t string_mean_length = (string_max_length + string_min_length) / 2;
+ // for an array of 50% null strings, we need to generate twice as many strings
+ // to ensure that they have an average of args.size total characters
+ int64_t array_size = args.size;
+ if (args.null_proportion < 1) {
+ array_size = static_cast<int64_t>(args.size / string_mean_length /
+ (1 - args.null_proportion));
+ }
+ auto values = std::static_pointer_cast<StringArray>(rand.String(
+ array_size, string_min_length, string_max_length, args.null_proportion));
+ Bench(values);
+ }
+
+ void Bench(const std::shared_ptr<Array>& values) {
+ bool indices_null_proportion = indices_have_nulls ? args.null_proportion : 0;
+ auto indices =
+ rand.Int32(static_cast<int32_t>(values->length()), 0,
+ static_cast<int32_t>(values->length() - 1), indices_null_proportion);
+
+ if (monotonic_indices) {
+ auto arg_sorter = *SortToIndices(*indices);
+ indices = *Take(*indices, *arg_sorter);
+ }
+
+ for (auto _ : state) {
+ ABORT_NOT_OK(Take(values, indices).status());
+ }
+ }
+};
+
+struct FilterBenchmark {
+ benchmark::State& state;
+ FilterArgs args;
+ random::RandomArrayGenerator rand;
+ bool filter_has_nulls;
+
+ FilterBenchmark(benchmark::State& state, bool filter_has_nulls)
+ : state(state),
+ args(state, filter_has_nulls),
+ rand(kSeed),
+ filter_has_nulls(filter_has_nulls) {}
+
+ void Int64() {
+ const int64_t array_size = args.size / sizeof(int64_t);
+ auto values = std::static_pointer_cast<NumericArray<Int64Type>>(
+ rand.Int64(array_size, -100, 100, args.values_null_proportion));
+ Bench(values);
+ }
+
+ void FSLInt64() {
+ const int64_t array_size = args.size / sizeof(int64_t);
+ auto int_array = std::static_pointer_cast<NumericArray<Int64Type>>(
+ rand.Int64(array_size, -100, 100, args.values_null_proportion));
+ auto values = std::make_shared<FixedSizeListArray>(
+ fixed_size_list(int64(), 1), array_size, int_array, int_array->null_bitmap(),
+ int_array->null_count());
+ Bench(values);
+ }
+
+ void String() {
+ int32_t string_min_length = 0, string_max_length = 32;
+ int32_t string_mean_length = (string_max_length + string_min_length) / 2;
+ // for an array of 50% null strings, we need to generate twice as many strings
+ // to ensure that they have an average of args.size total characters
+ int64_t array_size = args.size;
+ if (args.values_null_proportion < 1) {
+ array_size = static_cast<int64_t>(args.size / string_mean_length /
+ (1 - args.values_null_proportion));
+ }
+ auto values = std::static_pointer_cast<StringArray>(rand.String(
+ array_size, string_min_length, string_max_length, args.values_null_proportion));
+ Bench(values);
+ }
+
+ void Bench(const std::shared_ptr<Array>& values) {
+ auto filter = rand.Boolean(values->length(), args.selected_proportion,
+ args.filter_null_proportion);
+ for (auto _ : state) {
+ ABORT_NOT_OK(Filter(values, filter).status());
+ }
+ }
+};
+
+static void FilterInt64FilterNoNulls(benchmark::State& state) {
+ return FilterBenchmark(state, false).Int64();
Review comment:
ah thanks, fixing
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[GitHub] [arrow] github-actions[bot] commented on pull request #7358: ARROW-9045: [C++] Expand / improve Take and Filter benchmarks for enhanced baseline
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on pull request #7358:
URL: https://github.com/apache/arrow/pull/7358#issuecomment-639672332
https://issues.apache.org/jira/browse/ARROW-9045
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[GitHub] [arrow] fsaintjacques commented on a change in pull request #7358: ARROW-9045: [C++] Expand / improve Take and Filter benchmarks for enhanced baseline
Posted by GitBox <gi...@apache.org>.
fsaintjacques commented on a change in pull request #7358:
URL: https://github.com/apache/arrow/pull/7358#discussion_r436089235
##########
File path: cpp/src/arrow/compute/kernels/vector_selection_benchmark.cc
##########
@@ -0,0 +1,291 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you 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 "benchmark/benchmark.h"
+
+#include "arrow/compute/api_vector.h"
+#include "arrow/compute/benchmark_util.h"
+#include "arrow/compute/kernels/test_util.h"
+#include "arrow/testing/gtest_util.h"
+#include "arrow/testing/random.h"
+
+namespace arrow {
+namespace compute {
+
+constexpr auto kSeed = 0x0ff1ce;
+
+struct FilterParams {
+ // proportion of nulls in the values array
+ const double values_null_proportion;
+
+ // proportion of true in filter
+ const double selected_proportion;
+
+ // proportion of nulls in the filter
+ const double filter_null_proportion;
+};
+
+std::vector<int64_t> g_data_sizes = {kL1Size, 1 << 20};
+
+// The benchmark state parameter references this vector of cases. Test high and
+// low selectivity filters.
+std::vector<FilterParams> g_filter_params = {
+ {0., 0.95, 0.05}, {0., 0.10, 0.05}, {0.001, 0.95, 0.05}, {0.001, 0.10, 0.05},
+ {0.01, 0.95, 0.05}, {0.01, 0.10, 0.05}, {0.1, 0.95, 0.05}, {0.1, 0.10, 0.05},
+ {0.9, 0.95, 0.05}, {0.9, 0.10, 0.05}};
+
+// RAII struct to handle some of the boilerplate in filter
+struct FilterArgs {
+ // size of memory tested (per iteration) in bytes
+ const int64_t size;
+
+ double values_null_proportion = 0.;
+ double selected_proportion = 0.;
+ double filter_null_proportion = 0.;
+
+ FilterArgs(benchmark::State& state, bool filter_has_nulls)
+ : size(state.range(0)), state_(state) {
+ auto params = g_filter_params[state.range(1)];
+ values_null_proportion = params.values_null_proportion;
+ selected_proportion = params.selected_proportion;
+ filter_null_proportion = filter_has_nulls ? params.filter_null_proportion : 0;
+ }
+
+ ~FilterArgs() {
+ state_.counters["size"] = static_cast<double>(size);
+ state_.counters["select%"] = selected_proportion * 100;
+ state_.counters["data null%"] = values_null_proportion * 100;
+ state_.counters["mask null%"] = filter_null_proportion * 100;
+ state_.SetBytesProcessed(state_.iterations() * size);
+ }
+
+ private:
+ benchmark::State& state_;
+};
+
+struct TakeBenchmark {
+ benchmark::State& state;
+ RegressionArgs args;
+ random::RandomArrayGenerator rand;
+ bool indices_have_nulls;
+ bool monotonic_indices = false;
+
+ TakeBenchmark(benchmark::State& state, bool indices_have_nulls,
+ bool monotonic_indices = false)
+ : state(state),
+ args(state),
+ rand(kSeed),
+ indices_have_nulls(indices_have_nulls),
+ monotonic_indices(false) {}
+
+ void Int64() {
+ const int64_t array_size = args.size / sizeof(int64_t);
+ auto values = rand.Int64(array_size, -100, 100, args.null_proportion);
+ Bench(values);
+ }
+
+ void FSLInt64() {
+ const int64_t array_size = args.size / sizeof(int64_t);
+ auto int_array = rand.Int64(array_size, -100, 100, args.null_proportion);
+ auto values = std::make_shared<FixedSizeListArray>(
+ fixed_size_list(int64(), 1), array_size, int_array, int_array->null_bitmap(),
+ int_array->null_count());
+ Bench(values);
+ }
+
+ void String() {
+ int32_t string_min_length = 0, string_max_length = 32;
+ int32_t string_mean_length = (string_max_length + string_min_length) / 2;
+ // for an array of 50% null strings, we need to generate twice as many strings
+ // to ensure that they have an average of args.size total characters
+ int64_t array_size = args.size;
+ if (args.null_proportion < 1) {
+ array_size = static_cast<int64_t>(args.size / string_mean_length /
+ (1 - args.null_proportion));
+ }
+ auto values = std::static_pointer_cast<StringArray>(rand.String(
+ array_size, string_min_length, string_max_length, args.null_proportion));
+ Bench(values);
+ }
+
+ void Bench(const std::shared_ptr<Array>& values) {
+ bool indices_null_proportion = indices_have_nulls ? args.null_proportion : 0;
+ auto indices =
+ rand.Int32(static_cast<int32_t>(values->length()), 0,
+ static_cast<int32_t>(values->length() - 1), indices_null_proportion);
+
+ if (monotonic_indices) {
+ auto arg_sorter = *SortToIndices(*indices);
+ indices = *Take(*indices, *arg_sorter);
+ }
+
+ for (auto _ : state) {
+ ABORT_NOT_OK(Take(values, indices).status());
+ }
+ }
+};
+
+struct FilterBenchmark {
+ benchmark::State& state;
+ FilterArgs args;
+ random::RandomArrayGenerator rand;
+ bool filter_has_nulls;
+
+ FilterBenchmark(benchmark::State& state, bool filter_has_nulls)
+ : state(state),
+ args(state, filter_has_nulls),
+ rand(kSeed),
+ filter_has_nulls(filter_has_nulls) {}
+
+ void Int64() {
+ const int64_t array_size = args.size / sizeof(int64_t);
+ auto values = std::static_pointer_cast<NumericArray<Int64Type>>(
+ rand.Int64(array_size, -100, 100, args.values_null_proportion));
+ Bench(values);
+ }
+
+ void FSLInt64() {
+ const int64_t array_size = args.size / sizeof(int64_t);
+ auto int_array = std::static_pointer_cast<NumericArray<Int64Type>>(
+ rand.Int64(array_size, -100, 100, args.values_null_proportion));
+ auto values = std::make_shared<FixedSizeListArray>(
+ fixed_size_list(int64(), 1), array_size, int_array, int_array->null_bitmap(),
+ int_array->null_count());
+ Bench(values);
+ }
+
+ void String() {
+ int32_t string_min_length = 0, string_max_length = 32;
+ int32_t string_mean_length = (string_max_length + string_min_length) / 2;
+ // for an array of 50% null strings, we need to generate twice as many strings
+ // to ensure that they have an average of args.size total characters
+ int64_t array_size = args.size;
+ if (args.values_null_proportion < 1) {
+ array_size = static_cast<int64_t>(args.size / string_mean_length /
+ (1 - args.values_null_proportion));
+ }
+ auto values = std::static_pointer_cast<StringArray>(rand.String(
+ array_size, string_min_length, string_max_length, args.values_null_proportion));
+ Bench(values);
+ }
+
+ void Bench(const std::shared_ptr<Array>& values) {
+ auto filter = rand.Boolean(values->length(), args.selected_proportion,
+ args.filter_null_proportion);
+ for (auto _ : state) {
+ ABORT_NOT_OK(Filter(values, filter).status());
+ }
+ }
+};
+
+static void FilterInt64FilterNoNulls(benchmark::State& state) {
+ return FilterBenchmark(state, false).Int64();
Review comment:
You might get compiler warnings for returning in a void function.
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[GitHub] [arrow] wesm closed pull request #7358: ARROW-9045: [C++] Expand / improve Take and Filter benchmarks for enhanced baseline
Posted by GitBox <gi...@apache.org>.
wesm closed pull request #7358:
URL: https://github.com/apache/arrow/pull/7358
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