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[SPARK-19008][SQL] Improve performance of Dataset.map by eliminating boxing/unboxing #17172

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[SPARK-19008][SQL] Improve performance of Dataset.map by eliminating boxing/unboxing #17172

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d8b5f8d
Initial commit
kiszk Mar 6, 2017
a885907
fix scala style error
kiszk Mar 6, 2017
65fa05a
Simplify scope of varibles as needed
kiszk Mar 6, 2017
1f9ece4
add new benchmark suite with results
kiszk Mar 8, 2017
dfbce2a
address review comments
kiszk Mar 8, 2017
8ee91af
support Boolean for returnType
kiszk Mar 9, 2017
89c896e
add test cases for boolean type
kiszk Mar 9, 2017
1fb2933
fix scala type failure
kiszk Mar 9, 2017
35ba2c6
Generate a call to specialized method for Dataset.filter()
kiszk Mar 10, 2017
4c88c03
update test cases for map()
kiszk Mar 10, 2017
200cec7
add benchmark suite for Dataset.filter()
kiszk Mar 10, 2017
b25b191
update benchmark result
kiszk Mar 10, 2017
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38 changes: 37 additions & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/object.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@ import org.apache.spark.sql.catalyst.encoders._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.objects.Invoke
import org.apache.spark.sql.types._
import org.apache.spark.util.Utils

object CatalystSerde {
def deserialize[T : Encoder](child: LogicalPlan): DeserializeToObject = {
Expand Down Expand Up @@ -210,13 +211,48 @@ case class TypedFilter(
def typedCondition(input: Expression): Expression = {
val (funcClass, methodName) = func match {
case m: FilterFunction[_] => classOf[FilterFunction[_]] -> "call"
case _ => classOf[Any => Boolean] -> "apply"
case _ => FunctionUtils.getFunctionOneName(BooleanType, input.dataType)
}
val funcObj = Literal.create(func, ObjectType(funcClass))
Invoke(funcObj, methodName, BooleanType, input :: Nil)
}
}

object FunctionUtils {
private def getMethodType(dt: DataType, isOutput: Boolean): Option[String] = {
dt match {
case BooleanType if isOutput => Some("Z")
case IntegerType => Some("I")
case LongType => Some("J")
case FloatType => Some("F")
case DoubleType => Some("D")
case _ => None
}
}

def getFunctionOneName(outputDT: DataType, inputDT: DataType): (Class[_], String) = {
// load "scala.Function1" using Java API to avoid requirements of type parameters
Utils.classForName("scala.Function1") -> {
// if a pair of an argument and return types is one of specific types
// whose specialized method (apply$mc..$sp) is generated by scalac,
// Catalyst generated a direct method call to the specialized method.
// The followings are references for this specialization:
// http://www.scala-lang.org/api/2.12.0/scala/Function1.html
// https://github.com/scala/scala/blob/2.11.x/src/compiler/scala/tools/nsc/transform/
// SpecializeTypes.scala
// http://www.cakesolutions.net/teamblogs/scala-dissection-functions
// http://axel22.github.io/2013/11/03/specialization-quirks.html
val inputType = getMethodType(inputDT, false)
val outputType = getMethodType(outputDT, true)
if (inputType.isDefined && outputType.isDefined) {
s"apply$$mc${outputType.get}${inputType.get}$$sp"
} else {
"apply"
}
}
}
}

/** Factory for constructing new `AppendColumn` nodes. */
object AppendColumns {
def apply[T : Encoder, U : Encoder](
Expand Down
6 changes: 4 additions & 2 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala
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Expand Up @@ -28,11 +28,13 @@ import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.codegen._
import org.apache.spark.sql.catalyst.expressions.objects.Invoke
import org.apache.spark.sql.catalyst.plans.logical.FunctionUtils
import org.apache.spark.sql.catalyst.plans.physical._
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.plans.logical.LogicalKeyedState
import org.apache.spark.sql.execution.streaming.KeyedStateImpl
import org.apache.spark.sql.types.{DataType, ObjectType, StructType}
import org.apache.spark.sql.types._
import org.apache.spark.util.Utils


/**
Expand Down Expand Up @@ -219,7 +221,7 @@ case class MapElementsExec(
override def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = {
val (funcClass, methodName) = func match {
case m: MapFunction[_, _] => classOf[MapFunction[_, _]] -> "call"
case _ => classOf[Any => Any] -> "apply"
case _ => FunctionUtils.getFunctionOneName(outputObjAttr.dataType, child.output(0).dataType)
}
val funcObj = Literal.create(func, ObjectType(funcClass))
val callFunc = Invoke(funcObj, methodName, outputObjAttr.dataType, child.output)
Expand Down
122 changes: 116 additions & 6 deletions sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala
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Expand Up @@ -31,6 +31,49 @@ object DatasetBenchmark {

case class Data(l: Long, s: String)

def backToBackMapLong(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = {
import spark.implicits._

val rdd = spark.sparkContext.range(0, numRows)
val ds = spark.range(0, numRows)
val df = ds.toDF("l")
val func = (l: Long) => l + 1

val benchmark = new Benchmark("back-to-back map long", numRows)

benchmark.addCase("RDD") { iter =>
var res = rdd
var i = 0
while (i < numChains) {
res = res.map(func)
i += 1
}
res.foreach(_ => Unit)
}

benchmark.addCase("DataFrame") { iter =>
var res = df
var i = 0
while (i < numChains) {
res = res.select($"l" + 1 as "l")
i += 1
}
res.queryExecution.toRdd.foreach(_ => Unit)
}

benchmark.addCase("Dataset") { iter =>
var res = ds.as[Long]
var i = 0
while (i < numChains) {
res = res.map(func)
i += 1
}
res.queryExecution.toRdd.foreach(_ => Unit)
}

benchmark
}

def backToBackMap(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = {
import spark.implicits._

Expand Down Expand Up @@ -72,6 +115,49 @@ object DatasetBenchmark {
benchmark
}

def backToBackFilterLong(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = {
import spark.implicits._

val rdd = spark.sparkContext.range(1, numRows)
val ds = spark.range(1, numRows)
val df = ds.toDF("l")
val func = (l: Long) => l % 2L == 0L

val benchmark = new Benchmark("back-to-back filter Long", numRows)

benchmark.addCase("RDD") { iter =>
var res = rdd
var i = 0
while (i < numChains) {
res = res.filter(func)
i += 1
}
res.foreach(_ => Unit)
}

benchmark.addCase("DataFrame") { iter =>
var res = df
var i = 0
while (i < numChains) {
res = res.filter($"l" % 2L === 0L)
i += 1
}
res.queryExecution.toRdd.foreach(_ => Unit)
}

benchmark.addCase("Dataset") { iter =>
var res = ds.as[Long]
var i = 0
while (i < numChains) {
res = res.filter(func)
i += 1
}
res.queryExecution.toRdd.foreach(_ => Unit)
}

benchmark
}

def backToBackFilter(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = {
import spark.implicits._

Expand Down Expand Up @@ -165,9 +251,22 @@ object DatasetBenchmark {
val numRows = 100000000
val numChains = 10

val benchmark = backToBackMap(spark, numRows, numChains)
val benchmark2 = backToBackFilter(spark, numRows, numChains)
val benchmark3 = aggregate(spark, numRows)
val benchmark0 = backToBackMapLong(spark, numRows, numChains)
val benchmark1 = backToBackMap(spark, numRows, numChains)
val benchmark2 = backToBackFilterLong(spark, numRows, numChains)
val benchmark3 = backToBackFilter(spark, numRows, numChains)
val benchmark4 = aggregate(spark, numRows)

/*
OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic
Intel(R) Xeon(R) CPU E5-2667 v3 @ 3.20GHz
back-to-back map long: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
RDD 1883 / 1892 53.1 18.8 1.0X
DataFrame 502 / 642 199.1 5.0 3.7X
Dataset 657 / 784 152.2 6.6 2.9X
*/
benchmark0.run()

/*
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 3.10.0-327.18.2.el7.x86_64
Expand All @@ -178,7 +277,18 @@ object DatasetBenchmark {
DataFrame 2647 / 3116 37.8 26.5 1.3X
Dataset 4781 / 5155 20.9 47.8 0.7X
*/
benchmark.run()
benchmark1.run()

/*
OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux 4.4.0-47-generic
Intel(R) Xeon(R) CPU E5-2667 v3 @ 3.20GHz
back-to-back filter Long: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
RDD 846 / 1120 118.1 8.5 1.0X
DataFrame 270 / 329 370.9 2.7 3.1X
Dataset 545 / 789 183.5 5.4 1.6X
*/
benchmark2.run()

/*
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 3.10.0-327.18.2.el7.x86_64
Expand All @@ -189,7 +299,7 @@ object DatasetBenchmark {
DataFrame 59 / 72 1695.4 0.6 22.8X
Dataset 2777 / 2805 36.0 27.8 0.5X
*/
benchmark2.run()
benchmark3.run()

/*
Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.12.1
Expand All @@ -201,6 +311,6 @@ object DatasetBenchmark {
Dataset sum using Aggregator 4656 / 4758 21.5 46.6 0.4X
Dataset complex Aggregator 6636 / 7039 15.1 66.4 0.3X
*/
benchmark3.run()
benchmark4.run()
}
}
51 changes: 51 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -62,13 +62,64 @@ class DatasetPrimitiveSuite extends QueryTest with SharedSQLContext {
2, 3, 4)
}

test("mapPrimitive") {
val dsInt = Seq(1, 2, 3).toDS()
checkDataset(dsInt.map(_ > 1), false, true, true)
checkDataset(dsInt.map(_ + 1), 2, 3, 4)
checkDataset(dsInt.map(_ + 8589934592L), 8589934593L, 8589934594L, 8589934595L)
checkDataset(dsInt.map(_ + 1.1F), 2.1F, 3.1F, 4.1F)
checkDataset(dsInt.map(_ + 1.23D), 2.23D, 3.23D, 4.23D)

val dsLong = Seq(1L, 2L, 3L).toDS()
checkDataset(dsLong.map(_ > 1), false, true, true)
checkDataset(dsLong.map(e => (e + 1).toInt), 2, 3, 4)
checkDataset(dsLong.map(_ + 8589934592L), 8589934593L, 8589934594L, 8589934595L)
checkDataset(dsLong.map(_ + 1.1F), 2.1F, 3.1F, 4.1F)
checkDataset(dsLong.map(_ + 1.23D), 2.23D, 3.23D, 4.23D)

val dsFloat = Seq(1F, 2F, 3F).toDS()
checkDataset(dsFloat.map(_ > 1), false, true, true)
checkDataset(dsFloat.map(e => (e + 1).toInt), 2, 3, 4)
checkDataset(dsFloat.map(e => (e + 123456L).toLong), 123457L, 123458L, 123459L)
checkDataset(dsFloat.map(_ + 1.1F), 2.1F, 3.1F, 4.1F)
checkDataset(dsFloat.map(_ + 1.23D), 2.23D, 3.23D, 4.23D)

val dsDouble = Seq(1D, 2D, 3D).toDS()
checkDataset(dsDouble.map(_ > 1), false, true, true)
checkDataset(dsDouble.map(e => (e + 1).toInt), 2, 3, 4)
checkDataset(dsDouble.map(e => (e + 8589934592L).toLong),
8589934593L, 8589934594L, 8589934595L)
checkDataset(dsDouble.map(e => (e + 1.1F).toFloat), 2.1F, 3.1F, 4.1F)
checkDataset(dsDouble.map(_ + 1.23D), 2.23D, 3.23D, 4.23D)

val dsBoolean = Seq(true, false).toDS()
checkDataset(dsBoolean.map(e => !e), false, true)
}

test("filter") {
val ds = Seq(1, 2, 3, 4).toDS()
checkDataset(
ds.filter(_ % 2 == 0),
2, 4)
}

test("filterPrimitive") {
val dsInt = Seq(1, 2, 3).toDS()
checkDataset(dsInt.filter(_ > 1), 2, 3)

val dsLong = Seq(1L, 2L, 3L).toDS()
checkDataset(dsLong.filter(_ > 1), 2L, 3L)

val dsFloat = Seq(1F, 2F, 3F).toDS()
checkDataset(dsFloat.filter(_ > 1), 2F, 3F)

val dsDouble = Seq(1D, 2D, 3D).toDS()
checkDataset(dsDouble.filter(_ > 1), 2D, 3D)

val dsBoolean = Seq(true, false).toDS()
checkDataset(dsBoolean.filter(e => !e), false)
}

test("foreach") {
val ds = Seq(1, 2, 3).toDS()
val acc = sparkContext.longAccumulator
Expand Down