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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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30 changes: 28 additions & 2 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,8 @@

package org.apache.spark.sql.execution

import com.sun.org.apache.xalan.internal.xsltc.compiler.util.VoidType
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Is this a mistaken import? I don't see it used in the change and can't imagine we'd be invoking Xalan here

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Yes, that is why the first try was failed. It was unintentionally imported during my debugging.


import scala.language.existentials

import org.apache.spark.api.java.function.MapFunction
Expand All @@ -32,7 +34,7 @@ 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._


/**
Expand Down Expand Up @@ -217,9 +219,33 @@ case class MapElementsExec(
}

override def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = {
val inType = if (child.output.length == 1) child.output(0).dataType else NullType
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These two are only needed inside the case _ block right?

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Good catch. I simplified their scope.

val outType = outputObjAttr.dataType
val (funcClass, methodName) = func match {
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let's put this thing in a util so that FilterExec can also use it

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Sure. Now, can generate a call to a specialized method for Dataset.filter().

case m: MapFunction[_, _] => classOf[MapFunction[_, _]] -> "call"
case _ => classOf[Any => Any] -> "apply"
case _ =>
(inType, outType) match {
// 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.
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can you link to some official document or blogpost?

case (IntegerType, IntegerType) => classOf[Int => Int] -> "apply$mcII$sp"
case (IntegerType, LongType) => classOf[Int => Long] -> "apply$mcJI$sp"
case (IntegerType, FloatType) => classOf[Int => Float] -> "apply$mcFI$sp"
case (IntegerType, DoubleType) => classOf[Int => Double] -> "apply$mcDI$sp"
case (LongType, IntegerType) => classOf[Long => Int] -> "apply$mcIJ$sp"
case (LongType, LongType) => classOf[Long => Long] -> "apply$mcJJ$sp"
case (LongType, FloatType) => classOf[Long => Float] -> "apply$mcFJ$sp"
case (LongType, DoubleType) => classOf[Long => Double] -> "apply$mcDJ$sp"
case (FloatType, IntegerType) => classOf[Float => Int] -> "apply$mcIF$sp"
case (FloatType, LongType) => classOf[Float => Long] -> "apply$mcJF$sp"
case (FloatType, FloatType) => classOf[Float => Float] -> "apply$mcFF$sp"
case (FloatType, DoubleType) => classOf[Float => Double] -> "apply$mcDF$sp"
case (DoubleType, IntegerType) => classOf[Double => Int] -> "apply$mcID$sp"
case (DoubleType, LongType) => classOf[Double => Long] -> "apply$mcJD$sp"
case (DoubleType, FloatType) => classOf[Double => Float] -> "apply$mcFD$sp"
case (DoubleType, DoubleType) => classOf[Double => Double] -> "apply$mcDD$sp"
case _ => classOf[Any => Any] -> "apply"
}
}
val funcObj = Literal.create(func, ObjectType(funcClass))
val callFunc = Invoke(funcObj, methodName, outputObjAttr.dataType, child.output)
Expand Down
27 changes: 27 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala
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Expand Up @@ -62,6 +62,33 @@ class DatasetPrimitiveSuite extends QueryTest with SharedSQLContext {
2, 3, 4)
}

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

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

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

val dsDouble = Seq(1D, 2D, 3D).toDS()
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(e => e + 1.23D), 2.23D, 3.23D, 4.23D)
}

test("filter") {
val ds = Seq(1, 2, 3, 4).toDS()
checkDataset(
Expand Down