diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/object.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/object.scala index 617239f56cdd3..7f4462e583607 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/object.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/object.scala @@ -28,6 +28,7 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.expressions.objects.Invoke import org.apache.spark.sql.streaming.OutputMode import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils object CatalystSerde { def deserialize[T : Encoder](child: LogicalPlan): DeserializeToObject = { @@ -211,13 +212,48 @@ 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]( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala index 199ba5ce6969b..fdd1bcc94be25 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala @@ -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 /** @@ -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) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala index 66d94d6016050..1a0672b8876da 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala @@ -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($"l" + 1 as "l") + i += 1 + } + res.queryExecution.toRdd.foreach(_ => Unit) + } + + benchmark.addCase("Dataset") { iter => + var res = ds.as[Long] + var i = 0 + while (i < numChains) { + res = res.map(func) + i += 1 + } + res.queryExecution.toRdd.foreach(_ => Unit) + } + + benchmark + } + def backToBackMap(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = { import spark.implicits._ @@ -72,6 +115,49 @@ object DatasetBenchmark { benchmark } + def backToBackFilterLong(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = { + import spark.implicits._ + + val rdd = spark.sparkContext.range(1, numRows) + val ds = spark.range(1, numRows) + val df = ds.toDF("l") + val func = (l: Long) => l % 2L == 0L + + val benchmark = new Benchmark("back-to-back filter Long", numRows) + + benchmark.addCase("RDD") { iter => + var res = rdd + var i = 0 + while (i < numChains) { + res = res.filter(func) + i += 1 + } + res.foreach(_ => Unit) + } + + benchmark.addCase("DataFrame") { iter => + var res = df + var i = 0 + while (i < numChains) { + res = res.filter($"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._ @@ -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 @@ -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 @@ -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 @@ -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() } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala index 6b50cb3e48c76..82b707537e45f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala @@ -62,6 +62,40 @@ 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( @@ -69,6 +103,23 @@ class DatasetPrimitiveSuite extends QueryTest with SharedSQLContext { 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