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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.joins
import org.apache.spark.{SparkException, TaskContext}
import org.apache.spark.memory.MemoryMode
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.{Expression, JoinedRow, UnsafeRow}
import org.apache.spark.sql.catalyst.plans._
import org.apache.spark.sql.catalyst.plans.physical._
import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
import org.apache.spark.sql.execution.metric.SQLMetrics
/**
* Performs a hash join of two child relations by first shuffling the data using the join keys.
*/
case class ShuffledHashJoin(
leftKeys: Seq[Expression],
rightKeys: Seq[Expression],
joinType: JoinType,
buildSide: BuildSide,
condition: Option[Expression],
left: SparkPlan,
right: SparkPlan)
extends BinaryNode with HashJoin {
override private[sql] lazy val metrics = Map(
"numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
override def outputPartitioning: Partitioning = joinType match {
case Inner => PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
case LeftSemi => left.outputPartitioning
case LeftOuter => left.outputPartitioning
case RightOuter => right.outputPartitioning
case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
case x =>
throw new IllegalArgumentException(s"ShuffledHashJoin should not take $x as the JoinType")
}
override def requiredChildDistribution: Seq[Distribution] =
ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
private def buildHashedRelation(iter: Iterator[UnsafeRow]): HashedRelation = {
// try to acquire some memory for the hash table, it could trigger other operator to free some
// memory. The memory acquired here will mostly be used until the end of task.
val context = TaskContext.get()
val memoryManager = context.taskMemoryManager()
var acquired = 0L
var used = 0L
context.addTaskCompletionListener((t: TaskContext) =>
memoryManager.releaseExecutionMemory(acquired, MemoryMode.ON_HEAP, null)
)
val copiedIter = iter.map { row =>
// It's hard to guess what's exactly memory will be used, we have a rough guess here.
// TODO: use BytesToBytesMap instead of HashMap for memory efficiency
// Each pair in HashMap will have two UnsafeRows, one CompactBuffer, maybe 10+ pointers
val needed = 150 + row.getSizeInBytes
if (needed > acquired - used) {
val got = memoryManager.acquireExecutionMemory(
Math.max(memoryManager.pageSizeBytes(), needed), MemoryMode.ON_HEAP, null)
if (got < needed) {
throw new SparkException("Can't acquire enough memory to build hash map in shuffled" +
"hash join, please use sort merge join by setting " +
"spark.sql.join.preferSortMergeJoin=true")
}
acquired += got
}
used += needed
// HashedRelation requires that the UnsafeRow should be separate objects.
row.copy()
}
HashedRelation(canJoinKeyFitWithinLong, copiedIter, buildSideKeyGenerator)
}
protected override def doExecute(): RDD[InternalRow] = {
val numOutputRows = longMetric("numOutputRows")
streamedPlan.execute().zipPartitions(buildPlan.execute()) { (streamIter, buildIter) =>
val hashed = buildHashedRelation(buildIter.asInstanceOf[Iterator[UnsafeRow]])
val joinedRow = new JoinedRow
joinType match {
case Inner =>
hashJoin(streamIter, hashed, numOutputRows)
case LeftSemi =>
hashSemiJoin(streamIter, hashed, numOutputRows)
case LeftOuter =>
val keyGenerator = streamSideKeyGenerator
val resultProj = createResultProjection
streamIter.flatMap(currentRow => {
val rowKey = keyGenerator(currentRow)
joinedRow.withLeft(currentRow)
leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey), resultProj, numOutputRows)
})
case RightOuter =>
val keyGenerator = streamSideKeyGenerator
val resultProj = createResultProjection
streamIter.flatMap(currentRow => {
val rowKey = keyGenerator(currentRow)
joinedRow.withRight(currentRow)
rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow, resultProj, numOutputRows)
})
case x =>
throw new IllegalArgumentException(
s"ShuffledHashJoin should not take $x as the JoinType")
}
}
}
}
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