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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2022/06/10 17:52:18 UTC
[GitHub] [arrow] iChauster commented on a diff in pull request #13366: ARROW-16809: [C++] Add Benchmarks for FilterNode
iChauster commented on code in PR #13366:
URL: https://github.com/apache/arrow/pull/13366#discussion_r894772961
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cpp/src/arrow/compute/exec/filter_benchmark.cc:
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@@ -0,0 +1,176 @@
+// 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 <condition_variable>
+#include <mutex>
+
+#include "benchmark/benchmark.h"
+
+#include "arrow/compute/cast.h"
+#include "arrow/compute/exec.h"
+#include "arrow/compute/exec/expression.h"
+#include "arrow/compute/exec/options.h"
+#include "arrow/compute/exec/task_util.h"
+#include "arrow/compute/exec/test_util.h"
+#include "arrow/dataset/partition.h"
+#include "arrow/testing/future_util.h"
+#include "arrow/testing/generator.h"
+#include "arrow/testing/gtest_util.h"
+#include "arrow/type.h"
+
+namespace arrow {
+namespace compute {
+
+static constexpr int64_t kTotalBatchSize = 1000000;
+
+static void FilterOverhead(benchmark::State& state, Expression expr) {
+ const int32_t batch_size = static_cast<int32_t>(state.range(0));
+ const int32_t num_batches = kTotalBatchSize / batch_size;
+
+ arrow::compute::BatchesWithSchema data = MakeRandomBatches(
+ schema({field("i64", int64()), field("bool", boolean())}), num_batches, batch_size);
+ ExecContext ctx(default_memory_pool(), arrow::internal::GetCpuThreadPool());
+ for (auto _ : state) {
+ state.PauseTiming();
+ ASSERT_OK_AND_ASSIGN(std::shared_ptr<arrow::compute::ExecPlan> plan,
+ ExecPlan::Make(&ctx));
+ AsyncGenerator<util::optional<ExecBatch>> sink_gen;
+ ASSERT_OK(Declaration::Sequence(
+ {
+ {"source",
+ SourceNodeOptions{data.schema,
+ data.gen(/*parallel=*/true, /*slow=*/false)},
+ "custom_source_label"},
+ {"filter",
+ FilterNodeOptions{
+ expr,
+ }},
+ {"sink", SinkNodeOptions{&sink_gen}, "custom_sink_label"},
+ })
+ .AddToPlan(plan.get()));
+ state.ResumeTiming();
+ ASSERT_FINISHES_OK(StartAndCollect(plan.get(), sink_gen));
+ }
+ state.counters["rows_per_second"] = benchmark::Counter(
+ static_cast<double>(state.iterations() * num_batches * batch_size),
+ benchmark::Counter::kIsRate);
+
+ state.counters["batches_per_second"] = benchmark::Counter(
+ static_cast<double>(state.iterations() * num_batches), benchmark::Counter::kIsRate);
+}
+
+static void FilterOverheadIsolated(benchmark::State& state, Expression expr) {
+ const int32_t batch_size = static_cast<int32_t>(state.range(0));
+ const int32_t num_batches = kTotalBatchSize / batch_size;
+
+ arrow::compute::BatchesWithSchema data = MakeRandomBatches(
+ schema({field("i64", int64()), field("bool", boolean())}), num_batches, batch_size);
+ ExecContext ctx(default_memory_pool(), arrow::internal::GetCpuThreadPool());
+ for (auto _ : state) {
+ state.PauseTiming();
+ AsyncGenerator<util::optional<ExecBatch>> sink_gen;
+
+ ASSERT_OK_AND_ASSIGN(std::shared_ptr<arrow::compute::ExecPlan> plan,
+ ExecPlan::Make(&ctx));
+ // Source and sink nodes have no effect on the benchmark.
+ // Used for dummy purposes as they are referenced in InputReceived and InputFinished.
+ ASSERT_OK_AND_ASSIGN(
+ arrow::compute::ExecNode * source_node,
+ MakeExecNode("source", plan.get(), {},
+ SourceNodeOptions{data.schema, data.gen(/*parallel=*/true,
+ /*slow=*/false)}));
+ ASSERT_OK_AND_ASSIGN(arrow::compute::ExecNode * project_node,
+ MakeExecNode("filter", plan.get(), {source_node},
+ FilterNodeOptions{
+ expr,
+ }));
+ MakeExecNode("sink", plan.get(), {project_node}, SinkNodeOptions{&sink_gen});
+
+ std::unique_ptr<arrow::compute::TaskScheduler> scheduler = TaskScheduler::Make();
+ std::condition_variable all_tasks_finished_cv;
+ std::mutex mutex;
+ int task_group_id = scheduler->RegisterTaskGroup(
+ [&](size_t thread_id, int64_t task_id) {
+ project_node->InputReceived(source_node, data.batches[task_id]);
+ return Status::OK();
+ },
+ [&](size_t thread_id) {
+ project_node->InputFinished(source_node, static_cast<int>(data.batches.size()));
+ std::unique_lock<std::mutex> lk(mutex);
+ all_tasks_finished_cv.notify_one();
+ return Status::OK();
+ });
+ scheduler->RegisterEnd();
+ ThreadIndexer thread_indexer;
+
+ state.ResumeTiming();
+ arrow::internal::ThreadPool* thread_pool = arrow::internal::GetCpuThreadPool();
+ ASSERT_OK(scheduler->StartScheduling(
+ thread_indexer(),
+ [&](std::function<Status(size_t)> task) -> Status {
+ return thread_pool->Spawn([&, task]() {
+ size_t tid = thread_indexer();
+ ARROW_DCHECK_OK(task(tid));
+ });
+ },
+ thread_pool->GetCapacity(),
+ /*use_sync_execution=*/false));
+ std::unique_lock<std::mutex> lk(mutex);
+ ASSERT_OK(scheduler->StartTaskGroup(thread_indexer(), task_group_id, num_batches));
+ all_tasks_finished_cv.wait(lk);
+ ASSERT_TRUE(project_node->finished().is_finished());
+ }
+ state.counters["rows_per_second"] = benchmark::Counter(
+ static_cast<double>(state.iterations() * num_batches * batch_size),
+ benchmark::Counter::kIsRate);
+
+ state.counters["batches_per_second"] = benchmark::Counter(
+ static_cast<double>(state.iterations() * num_batches), benchmark::Counter::kIsRate);
+}
+
+arrow::compute::Expression complex_expression =
+ less(less(field_ref("i64"), literal(20)), greater(field_ref("i64"), literal(0)));
+arrow::compute::Expression simple_expression =
+ less(call("negate", {field_ref("i64")}), literal(0));
+arrow::compute::Expression zero_copy_expression = is_valid((call(
+ "cast", {field_ref("i64")}, compute::CastOptions::Safe(timestamp(TimeUnit::NANO)))));
+arrow::compute::Expression ref_only_expression = less(field_ref("i64"), literal(0));
Review Comment:
I'm not sure how we want to express the complexity of filter expressions, so I used a similar approach to `ExpressionOverhead` / `ProjectBenchmarks`. `zero_copy` seems to perform a bit better than the others because it uses `is_valid` (I cannot do the less operation on timestamps).
Is there something I can change to make these more consistent?
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