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[SPARK-33523][SQL][TEST] Add predicate related benchmark to SubExprEliminationBenchmark #30476

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[SPARK-33523][SQL][TEST] Add predicate related benchmark to SubExprEliminationBenchmark #30476

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7e3be61
Add predicate benchmark.
viirya Nov 22, 2020
0ca10ba
Use noop source.
viirya Nov 24, 2020
930ffe2
Refactor.
viirya Nov 24, 2020
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22 changes: 16 additions & 6 deletions sql/core/benchmarks/SubExprEliminationBenchmark-jdk11-results.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,21 @@ 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 25932 26908 916 0.0 259320042.3 1.0X
subexpressionElimination off, codegen off 26085 26159 65 0.0 260848905.0 1.0X
subexpressionElimination on, codegen on 2860 2939 72 0.0 28603312.9 9.1X
subexpressionElimination on, codegen off 2517 2617 93 0.0 25165157.7 10.3X
from_json as subExpr in Project: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
subExprElimination false, codegen: true 26447 27127 605 0.0 264467933.4 1.0X
subExprElimination false, codegen: false 25673 26035 546 0.0 256732419.1 1.0X
subExprElimination true, codegen: true 1384 1448 102 0.0 13842910.3 19.1X
subExprElimination true, codegen: false 1244 1347 123 0.0 12442389.3 21.3X

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 in Filter: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
subexpressionElimination off, codegen on 34631 35449 833 0.0 346309884.0 1.0X
subexpressionElimination off, codegen on 34480 34851 353 0.0 344798490.4 1.0X
subexpressionElimination off, codegen on 16618 16811 291 0.0 166176642.6 2.1X
subexpressionElimination off, codegen on 34316 34667 310 0.0 343157094.7 1.0X


22 changes: 16 additions & 6 deletions sql/core/benchmarks/SubExprEliminationBenchmark-results.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,21 @@ 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 26503 27622 1937 0.0 265033362.4 1.0X
subexpressionElimination off, codegen off 24920 25376 430 0.0 249196978.2 1.1X
subexpressionElimination on, codegen on 2421 2466 39 0.0 24213606.1 10.9X
subexpressionElimination on, codegen off 2360 2435 87 0.0 23604320.7 11.2X
from_json as subExpr in Project: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
subExprElimination false, codegen: true 22767 23240 424 0.0 227665316.7 1.0X
subExprElimination false, codegen: false 22869 23351 465 0.0 228693464.1 1.0X
subExprElimination true, codegen: true 1328 1340 10 0.0 13280056.2 17.1X
subExprElimination true, codegen: false 1248 1276 31 0.0 12476135.1 18.2X

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 in Filter: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
subexpressionElimination off, codegen on 37691 38846 1004 0.0 376913767.9 1.0X
subexpressionElimination off, codegen on 37852 39124 1103 0.0 378517745.5 1.0X
subexpressionElimination off, codegen on 22900 23085 202 0.0 229000242.5 1.6X
subexpressionElimination off, codegen on 38298 38598 374 0.0 382978731.3 1.0X


106 changes: 58 additions & 48 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/SubExprEliminationBenchmark.scala
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Expand Up @@ -17,6 +17,8 @@
package org.apache.spark.sql.execution

import org.apache.spark.benchmark.Benchmark
import org.apache.spark.sql.Column
import org.apache.spark.sql.catalyst.expressions.{Expression, Literal, Or}
import org.apache.spark.sql.execution.benchmark.SqlBasedBenchmark
import org.apache.spark.sql.functions._
import org.apache.spark.sql.internal.SQLConf
Expand All @@ -39,7 +41,7 @@ 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)
val benchmark = new Benchmark("from_json as subExpr in Project", rowsNum, output = output)

withTempPath { path =>
prepareDataInfo(benchmark)
Expand All @@ -50,69 +52,77 @@ object SubExprEliminationBenchmark extends SqlBasedBenchmark {
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()
Seq(
("false", "true", "CODEGEN_ONLY"),
("false", "false", "NO_CODEGEN"),
("true", "true", "CODEGEN_ONLY"),
("true", "false", "NO_CODEGEN")
).foreach { case (subExprEliminationEnabled, codegenEnabled, codegenFactory) =>
// We only benchmark subexpression performance under codegen/non-codegen, so disabling
// json optimization.
val caseName = s"subExprElimination $subExprEliminationEnabled, codegen: $codegenEnabled"
benchmark.addCase(caseName, numIters) { _ =>
withSQLConf(
SQLConf.SUBEXPRESSION_ELIMINATION_ENABLED.key -> subExprEliminationEnabled,
SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> codegenEnabled,
SQLConf.CODEGEN_FACTORY_MODE.key -> codegenFactory,
SQLConf.JSON_EXPRESSION_OPTIMIZATION.key -> "false") {
val df = spark.read
.text(path.getAbsolutePath)
.select(cols: _*)
df.write.mode("overwrite").format("noop").save()
}
}
}

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.run()
}
}

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()
}
}
def withFilter(rowsNum: Int, numIters: Int): Unit = {
val benchmark = new Benchmark("from_json as subExpr in Filter", rowsNum, output = output)

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()
withTempPath { path =>
prepareDataInfo(benchmark)
val numCols = 1000
val schema = writeWideRow(path.getAbsolutePath, rowsNum, numCols)

val predicate = (0 until numCols).map { idx =>
(from_json('value, schema).getField(s"col$idx") >= Literal(100000)).expr
}.asInstanceOf[Seq[Expression]].reduce(Or)

Seq(
("false", "true", "CODEGEN_ONLY"),
("false", "false", "NO_CODEGEN"),
("true", "true", "CODEGEN_ONLY"),
("true", "false", "NO_CODEGEN")
).foreach { case (subExprEliminationEnabled, codegenEnabled, codegenFactory) =>
// We only benchmark subexpression performance under codegen/non-codegen, so disabling
// json optimization.
val caseName = s"subExprElimination $subExprEliminationEnabled, codegen: $codegenEnabled"
benchmark.addCase("subexpressionElimination off, codegen on", numIters) { _ =>
withSQLConf(
SQLConf.SUBEXPRESSION_ELIMINATION_ENABLED.key -> subExprEliminationEnabled,
SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> codegenEnabled,
SQLConf.CODEGEN_FACTORY_MODE.key -> codegenFactory,
SQLConf.JSON_EXPRESSION_OPTIMIZATION.key -> "false") {
val df = spark.read
.text(path.getAbsolutePath)
.where(Column(predicate))
df.write.mode("overwrite").format("noop").save()
}
}
}

benchmark.run()
}
}


override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
val numIters = 3
runBenchmark("Benchmark for performance of subexpression elimination") {
withFromJson(100, numIters)
withFilter(100, numIters)
}
}
}