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[SPARK-33455][SQL][TEST] Add SubExprEliminationBenchmark for benchmarking subexpression elimination

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### What changes were proposed in this pull request?

This patch adds a benchmark `SubExprEliminationBenchmark` for benchmarking subexpression elimination feature.

### Why are the changes needed?

We need a benchmark for subexpression elimination feature for change such as #30341.

### Does this PR introduce _any_ user-facing change?

No, dev only.

### How was this patch tested?

Unit test.

Closes #30379 from viirya/SPARK-33455.

Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
This commit is contained in:
Liang-Chi Hsieh 2020-11-14 19:02:36 -08:00 committed by Dongjoon Hyun
parent 156704ba0d
commit eea846b895
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5 changed files with 172 additions and 20 deletions
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15
sql/core/benchmarks/SubExprEliminationBenchmark-jdk11-results.txt Normal file
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@ -0,0 +1,15 @@
================================================================================================
Benchmark for performance of subexpression elimination
================================================================================================
Preparing data for benchmarking ...
OpenJDK 64-Bit Server VM 11.0.9+11 on Mac OS X 10.15.6
Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
from_json as subExpr: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------------------------------------
subexpressionElimination off, codegen on 26809 27731 898 0.0 268094225.4 1.0X
subexpressionElimination off, codegen off 25117 26612 1357 0.0 251166638.4 1.1X
subexpressionElimination on, codegen on 2582 2906 282 0.0 25819408.7 10.4X
subexpressionElimination on, codegen off 25635 26131 804 0.0 256346873.1 1.0X

15
sql/core/benchmarks/SubExprEliminationBenchmark-results.txt Normal file
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@ -0,0 +1,15 @@
================================================================================================
Benchmark for performance of subexpression elimination
================================================================================================
Preparing data for benchmarking ...
OpenJDK 64-Bit Server VM 1.8.0_265-b01 on Mac OS X 10.15.6
Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
from_json as subExpr: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------------------------------------
subexpressionElimination off, codegen on 24841 25365 803 0.0 248412787.5 1.0X
subexpressionElimination off, codegen off 25344 26205 941 0.0 253442656.5 1.0X
subexpressionElimination on, codegen on 2883 3019 119 0.0 28833086.8 8.6X
subexpressionElimination on, codegen off 24707 25688 903 0.0 247068775.9 1.0X

118
sql/core/src/test/scala/org/apache/spark/sql/execution/SubExprEliminationBenchmark.scala Normal file
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@ -0,0 +1,118 @@
/*
* 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.
*/
package org.apache.spark.sql.execution
import org.apache.spark.benchmark.Benchmark
import org.apache.spark.sql.execution.benchmark.SqlBasedBenchmark
import org.apache.spark.sql.functions._
import org.apache.spark.sql.internal.SQLConf
/**
* The benchmarks aims to measure performance of the queries where there are subexpression
* elimination or not.
* To run this benchmark:
* {{{
* 1. without sbt:
* bin/spark-submit --class <this class> --jars <spark core test jar>,
* <spark catalyst test jar> <spark sql test jar>
* 2. build/sbt "sql/test:runMain <this class>"
* 3. generate result:
* SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain <this class>"
* Results will be written to "benchmarks/SubExprEliminationBenchmark-results.txt".
* }}}
*/
object SubExprEliminationBenchmark extends SqlBasedBenchmark {
import spark.implicits._
def withFromJson(rowsNum: Int, numIters: Int): Unit = {
val benchmark = new Benchmark("from_json as subExpr", rowsNum, output = output)
withTempPath { path =>
prepareDataInfo(benchmark)
val numCols = 1000
val schema = writeWideRow(path.getAbsolutePath, rowsNum, numCols)
val cols = (0 until numCols).map { idx =>
from_json('value, schema).getField(s"col$idx")
}
// We only benchmark subexpression performance under codegen/non-codegen, so disabling
// json optimization.
benchmark.addCase("subexpressionElimination off, codegen on", numIters) { _ =>
withSQLConf(
SQLConf.SUBEXPRESSION_ELIMINATION_ENABLED.key -> "false",
SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "true",
SQLConf.CODEGEN_FACTORY_MODE.key -> "CODEGEN_ONLY",
SQLConf.JSON_EXPRESSION_OPTIMIZATION.key -> "false") {
val df = spark.read
.text(path.getAbsolutePath)
.select(cols: _*)
df.collect()
}
}
benchmark.addCase("subexpressionElimination off, codegen off", numIters) { _ =>
withSQLConf(
SQLConf.SUBEXPRESSION_ELIMINATION_ENABLED.key -> "false",
SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "false",
SQLConf.CODEGEN_FACTORY_MODE.key -> "NO_CODEGEN",
SQLConf.JSON_EXPRESSION_OPTIMIZATION.key -> "false") {
val df = spark.read
.text(path.getAbsolutePath)
.select(cols: _*)
df.collect()
}
}
benchmark.addCase("subexpressionElimination on, codegen on", numIters) { _ =>
withSQLConf(
SQLConf.SUBEXPRESSION_ELIMINATION_ENABLED.key -> "true",
SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "true",
SQLConf.CODEGEN_FACTORY_MODE.key -> "CODEGEN_ONLY",
SQLConf.JSON_EXPRESSION_OPTIMIZATION.key -> "false") {
val df = spark.read
.text(path.getAbsolutePath)
.select(cols: _*)
df.collect()
}
}
benchmark.addCase("subexpressionElimination on, codegen off", numIters) { _ =>
withSQLConf(
SQLConf.SUBEXPRESSION_ELIMINATION_ENABLED.key -> "true",
SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "false",
SQLConf.CODEGEN_FACTORY_MODE.key -> "NO_CODEGEN",
SQLConf.JSON_EXPRESSION_OPTIMIZATION.key -> "false") {
val df = spark.read
.text(path.getAbsolutePath)
.select(cols: _*)
df.collect()
}
}
benchmark.run()
}
}
override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
val numIters = 3
runBenchmark("Benchmark for performance of subexpression elimination") {
withFromJson(100, numIters)
}
}
}

23
sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/SqlBasedBenchmark.scala
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@ -22,7 +22,9 @@ import org.apache.spark.internal.config.UI.UI_ENABLED
import org.apache.spark.sql.{Dataset, SparkSession}
import org.apache.spark.sql.SaveMode.Overwrite
import org.apache.spark.sql.catalyst.plans.SQLHelper
import org.apache.spark.sql.functions.lit
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types._
/**
* Common base trait to run benchmark with the Dataset and DataFrame API.
@ -66,4 +68,25 @@ trait SqlBasedBenchmark extends BenchmarkBase with SQLHelper {
ds.write.format("noop").mode(Overwrite).save()
}
}
protected def prepareDataInfo(benchmark: Benchmark): Unit = {
// scalastyle:off println
benchmark.out.println("Preparing data for benchmarking ...")
// scalastyle:on println
}
/**
* Prepares a table with wide row for benchmarking. The table will be written into
* the given path.
*/
protected def writeWideRow(path: String, rowsNum: Int, numCols: Int): StructType = {
val fields = Seq.tabulate(numCols)(i => StructField(s"col$i", IntegerType))
val schema = StructType(fields)
spark.range(rowsNum)
.select(Seq.tabulate(numCols)(i => lit(i).as(s"col$i")): _*)
.write.json(path)
schema
}
}

21
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonBenchmark.scala
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@ -39,16 +39,9 @@ import org.apache.spark.sql.types._
* Results will be written to "benchmarks/JSONBenchmark-results.txt".
* }}}
*/
object JsonBenchmark extends SqlBasedBenchmark {
import spark.implicits._
private def prepareDataInfo(benchmark: Benchmark): Unit = {
// scalastyle:off println
benchmark.out.println("Preparing data for benchmarking ...")
// scalastyle:on println
}
def schemaInferring(rowsNum: Int, numIters: Int): Unit = {
val benchmark = new Benchmark("JSON schema inferring", rowsNum, output = output)
@ -128,18 +121,6 @@ object JsonBenchmark extends SqlBasedBenchmark {
.add("z", StringType)
}
def writeWideRow(path: String, rowsNum: Int): StructType = {
val colsNum = 1000
val fields = Seq.tabulate(colsNum)(i => StructField(s"col$i", IntegerType))
val schema = StructType(fields)
spark.range(rowsNum)
.select(Seq.tabulate(colsNum)(i => lit(i).as(s"col$i")): _*)
.write.json(path)
schema
}
def countWideColumn(rowsNum: Int, numIters: Int): Unit = {
val benchmark = new Benchmark("count a wide column", rowsNum, output = output)
@ -171,7 +152,7 @@ object JsonBenchmark extends SqlBasedBenchmark {
withTempPath { path =>
prepareDataInfo(benchmark)
val schema = writeWideRow(path.getAbsolutePath, rowsNum)
val schema = writeWideRow(path.getAbsolutePath, rowsNum, 1000)
benchmark.addCase("No encoding", numIters) { _ =>
spark.read