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/*
* 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.rdd.RDD
import org.apache.spark.serializer.Serializer
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode, LazilyGeneratedOrdering}
import org.apache.spark.sql.catalyst.plans.physical._
import org.apache.spark.sql.execution.exchange.ShuffleExchange
import org.apache.spark.util.Utils
/**
* Take the first `limit` elements and collect them to a single partition.
*
* This operator will be used when a logical `Limit` operation is the final operator in an
* logical plan, which happens when the user is collecting results back to the driver.
*/
case class CollectLimitExec(limit: Int, child: SparkPlan) extends UnaryExecNode {
override def output: Seq[Attribute] = child.output
override def outputPartitioning: Partitioning = SinglePartition
override def executeCollect(): Array[InternalRow] = child.executeTake(limit)
private val serializer: Serializer = new UnsafeRowSerializer(child.output.size)
protected override def doExecute(): RDD[InternalRow] = {
val locallyLimited = child.execute().mapPartitionsInternal(_.take(limit))
val shuffled = new ShuffledRowRDD(
ShuffleExchange.prepareShuffleDependency(
locallyLimited, child.output, SinglePartition, serializer))
shuffled.mapPartitionsInternal(_.take(limit))
}
}
/**
* Helper trait which defines methods that are shared by both
* [[LocalLimitExec]] and [[GlobalLimitExec]].
*/
trait BaseLimitExec extends UnaryExecNode with CodegenSupport {
val limit: Int
override def output: Seq[Attribute] = child.output
protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
iter.take(limit)
}
override def inputRDDs(): Seq[RDD[InternalRow]] = {
child.asInstanceOf[CodegenSupport].inputRDDs()
}
protected override def doProduce(ctx: CodegenContext): String = {
child.asInstanceOf[CodegenSupport].produce(ctx, this)
}
override def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = {
val stopEarly = ctx.freshName("stopEarly")
ctx.addMutableState("boolean", stopEarly, s"$stopEarly = false;")
ctx.addNewFunction("stopEarly", s"""
@Override
protected boolean stopEarly() {
return $stopEarly;
}
""")
val countTerm = ctx.freshName("count")
ctx.addMutableState("int", countTerm, s"$countTerm = 0;")
s"""
| if ($countTerm < $limit) {
| $countTerm += 1;
| ${consume(ctx, input)}
| } else {
| $stopEarly = true;
| }
""".stripMargin
}
}
/**
* Take the first `limit` elements of each child partition, but do not collect or shuffle them.
*/
case class LocalLimitExec(limit: Int, child: SparkPlan) extends BaseLimitExec {
override def outputOrdering: Seq[SortOrder] = child.outputOrdering
override def outputPartitioning: Partitioning = child.outputPartitioning
}
/**
* Take the first `limit` elements of the child's single output partition.
*/
case class GlobalLimitExec(limit: Int, child: SparkPlan) extends BaseLimitExec {
override def requiredChildDistribution: List[Distribution] = AllTuples :: Nil
override def outputPartitioning: Partitioning = child.outputPartitioning
override def outputOrdering: Seq[SortOrder] = child.outputOrdering
}
/**
* Take the first limit elements as defined by the sortOrder, and do projection if needed.
* This is logically equivalent to having a Limit operator after a [[SortExec]] operator,
* or having a [[ProjectExec]] operator between them.
* This could have been named TopK, but Spark's top operator does the opposite in ordering
* so we name it TakeOrdered to avoid confusion.
*/
case class TakeOrderedAndProjectExec(
limit: Int,
sortOrder: Seq[SortOrder],
projectList: Seq[NamedExpression],
child: SparkPlan) extends UnaryExecNode {
override def output: Seq[Attribute] = {
projectList.map(_.toAttribute)
}
override def executeCollect(): Array[InternalRow] = {
val ord = new LazilyGeneratedOrdering(sortOrder, child.output)
val data = child.execute().map(_.copy()).takeOrdered(limit)(ord)
if (projectList != child.output) {
val proj = UnsafeProjection.create(projectList, child.output)
data.map(r => proj(r).copy())
} else {
data
}
}
private val serializer: Serializer = new UnsafeRowSerializer(child.output.size)
protected override def doExecute(): RDD[InternalRow] = {
val ord = new LazilyGeneratedOrdering(sortOrder, child.output)
val localTopK: RDD[InternalRow] = {
child.execute().map(_.copy()).mapPartitions { iter =>
org.apache.spark.util.collection.Utils.takeOrdered(iter, limit)(ord)
}
}
val shuffled = new ShuffledRowRDD(
ShuffleExchange.prepareShuffleDependency(
localTopK, child.output, SinglePartition, serializer))
shuffled.mapPartitions { iter =>
val topK = org.apache.spark.util.collection.Utils.takeOrdered(iter.map(_.copy()), limit)(ord)
if (projectList != child.output) {
val proj = UnsafeProjection.create(projectList, child.output)
topK.map(r => proj(r))
} else {
topK
}
}
}
override def outputOrdering: Seq[SortOrder] = sortOrder
override def outputPartitioning: Partitioning = SinglePartition
override def simpleString: String = {
val orderByString = Utils.truncatedString(sortOrder, "[", ",", "]")
val outputString = Utils.truncatedString(output, "[", ",", "]")
s"TakeOrderedAndProject(limit=$limit, orderBy=$orderByString, output=$outputString)"
}
}
