cloud-fan commented on a change in pull request #26434: [SPARK-29544] [SQL] optimize skewed partition based on data size URL: https://github.com/apache/spark/pull/26434#discussion_r364262035
########## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedPartitions.scala ########## @@ -0,0 +1,255 @@ +/* + * 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.adaptive + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer + +import org.apache.spark.{MapOutputStatistics, MapOutputTrackerMaster, SparkEnv} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.Attribute +import org.apache.spark.sql.catalyst.plans._ +import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.execution._ +import org.apache.spark.sql.execution.joins.SortMergeJoinExec +import org.apache.spark.sql.internal.SQLConf + +case class OptimizeSkewedPartitions(conf: SQLConf) extends Rule[SparkPlan] { + + private val supportedJoinTypes = + Inner :: Cross :: LeftSemi :: LeftAnti :: LeftOuter :: RightOuter :: Nil + + /** + * A partition is considered as a skewed partition if its size is larger than the median + * partition size * spark.sql.adaptive.skewedPartitionFactor and also larger than + * spark.sql.adaptive.skewedPartitionSizeThreshold. + */ + private def isSkewed( + stats: MapOutputStatistics, + partitionId: Int, + medianSize: Long): Boolean = { + val size = stats.bytesByPartitionId(partitionId) + size > medianSize * conf.getConf(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_FACTOR) && + size > conf.getConf(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_SIZE_THRESHOLD) + } + + private def medianSize(stats: MapOutputStatistics): Long = { + val numPartitions = stats.bytesByPartitionId.length + val bytes = stats.bytesByPartitionId.sorted + if (bytes(numPartitions / 2) > 0) bytes(numPartitions / 2) else 1 + } + + /** + * Get the map size of the specific reduce shuffle Id. + */ + private def getMapSizesForReduceId(shuffleId: Int, partitionId: Int): Array[Long] = { + val mapOutputTracker = SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster] + mapOutputTracker.shuffleStatuses(shuffleId).mapStatuses.map{_.getSizeForBlock(partitionId)} + } + + /** + * Split the skewed partition based on the map size and the max split number. + */ + private def getMapStartIndices(stage: ShuffleQueryStageExec, partitionId: Int): Array[Int] = { + val shuffleId = stage.shuffle.shuffleDependency.shuffleHandle.shuffleId + val mapPartitionSizes = getMapSizesForReduceId(shuffleId, partitionId) + val maxSplits = math.min(conf.getConf( + SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_MAX_SPLITS), mapPartitionSizes.length) + val avgPartitionSize = mapPartitionSizes.sum / maxSplits + val advisoryPartitionSize = math.max(avgPartitionSize, + conf.getConf(SQLConf.SHUFFLE_TARGET_POSTSHUFFLE_INPUT_SIZE)) + val partitionIndices = mapPartitionSizes.indices + val partitionStartIndices = ArrayBuffer[Int]() + var postMapPartitionSize = mapPartitionSizes(0) + partitionStartIndices += 0 + partitionIndices.drop(1).foreach { nextPartitionIndex => + val nextMapPartitionSize = mapPartitionSizes(nextPartitionIndex) + if (postMapPartitionSize + nextMapPartitionSize > advisoryPartitionSize) { + partitionStartIndices += nextPartitionIndex + postMapPartitionSize = nextMapPartitionSize + } else { + postMapPartitionSize += nextMapPartitionSize + } + } + + if (partitionStartIndices.size > maxSplits) { + partitionStartIndices.take(maxSplits).toArray + } else partitionStartIndices.toArray + } + + private def getStatistics(stage: ShuffleQueryStageExec): MapOutputStatistics = { + assert(stage.resultOption.isDefined, "ShuffleQueryStageExec should" + + " already be ready when executing OptimizeSkewedPartitions rule") + stage.resultOption.get.asInstanceOf[MapOutputStatistics] + } + + private def supportSplitOnLeftPartition(joinType: JoinType) = { + joinType == Inner || joinType == Cross || joinType == LeftSemi || + joinType == LeftAnti || joinType == LeftOuter + } + + private def supportSplitOnRightPartition(joinType: JoinType) = { + joinType == Inner || joinType == Cross || joinType == RightOuter + } + + private def getNumMappers(stage: ShuffleQueryStageExec): Int = { + stage.shuffle.shuffleDependency.rdd.partitions.length + } + + def handleSkewJoin(plan: SparkPlan): SparkPlan = plan.transformUp { + case smj @ SortMergeJoinExec(leftKeys, rightKeys, joinType, condition, + SortExec(_, _, left: ShuffleQueryStageExec, _), + SortExec(_, _, right: ShuffleQueryStageExec, _)) + if supportedJoinTypes.contains(joinType) => + val leftStats = getStatistics(left) + val rightStats = getStatistics(right) + val numPartitions = leftStats.bytesByPartitionId.length + + val leftMedSize = medianSize(leftStats) + val rightMedSize = medianSize(rightStats) + val leftSizeInfo = s"median size: $leftMedSize, max size: ${leftStats.bytesByPartitionId.max}" + val rightSizeInfo = s"median size: $rightMedSize," + + s" max size: ${rightStats.bytesByPartitionId.max}" + logDebug( + s""" + |Try to optimize skewed join. + |Left side partition size: $leftSizeInfo + |Right side partition size: $rightSizeInfo + """.stripMargin) + + val skewedPartitions = mutable.HashSet[Int]() + val subJoins = mutable.ArrayBuffer[SparkPlan]() + for (partitionId <- 0 until numPartitions) { + val isLeftSkew = isSkewed(leftStats, partitionId, leftMedSize) + val isRightSkew = isSkewed(rightStats, partitionId, rightMedSize) + val leftMapIdStartIndices = if (isLeftSkew && supportSplitOnLeftPartition(joinType)) { + getMapStartIndices(left, partitionId) + } else { + Array(0) + } + val rightMapIdStartIndices = if (isRightSkew && supportSplitOnRightPartition(joinType)) { + getMapStartIndices(right, partitionId) + } else { + Array(0) + } + + if (leftMapIdStartIndices.length > 1 || rightMapIdStartIndices.length > 1) { + skewedPartitions += partitionId + for (i <- 0 until leftMapIdStartIndices.length; + j <- 0 until rightMapIdStartIndices.length) { + val leftEndMapId = if (i == leftMapIdStartIndices.length - 1) { + getNumMappers(left) + } else { + leftMapIdStartIndices(i + 1) + } + val rightEndMapId = if (j == rightMapIdStartIndices.length - 1) { + getNumMappers(right) + } else { + rightMapIdStartIndices(j + 1) + } + // TODO: we may can optimize the sort merge join to broad cast join after + // obtaining the raw data size of per partition, + val leftSkewedReader = SkewedShufflePartitionReader( + left, partitionId, leftMapIdStartIndices(i), leftEndMapId) + + val rightSkewedReader = SkewedShufflePartitionReader(right, partitionId, + rightMapIdStartIndices(j), rightEndMapId) + subJoins += SortMergeJoinExec(leftKeys, rightKeys, joinType, condition, + leftSkewedReader, rightSkewedReader) + } + } + } + logDebug(s"number of skewed partitions is ${skewedPartitions.size}") + if (skewedPartitions.nonEmpty) { + val optimizedSmj = smj.transformDown { + case sort @ SortExec(_, _, shuffleStage: ShuffleQueryStageExec, _) => + val shuffleStage = sort.child.asInstanceOf[ShuffleQueryStageExec] + val newStage = shuffleStage.copy( + excludedPartitions = skewedPartitions.toSet) + newStage.resultOption = shuffleStage.resultOption + sort.copy(child = newStage) + } + subJoins += optimizedSmj + UnionExec(subJoins) + } else { + smj + } + } + + override def apply(plan: SparkPlan): SparkPlan = { + if (!conf.getConf(SQLConf.ADAPTIVE_EXECUTION_SKEWED_JOIN_ENABLED)) { + return plan + } + + def collectShuffleStages(plan: SparkPlan): Seq[ShuffleQueryStageExec] = plan match { + case _: LocalShuffleReaderExec => Nil + case _: CoalescedShuffleReaderExec => Nil + case stage: ShuffleQueryStageExec => Seq(stage) + case _ => plan.children.flatMap(collectShuffleStages) + } + + val shuffleStages = collectShuffleStages(plan) + + if (shuffleStages.length == 2) { + // Currently we only support handling skewed join for 2 table join. + handleSkewJoin(plan) + } else { + plan + + } + } +} + +/** + * A wrapper of shuffle query stage, which submits one reduce task to read a single + * shuffle partition 'partitionIndex' produced by the mappers in range [startMapId, endMapId). + * This is used to handle the skewed partitions. + * + * @param child It's usually `ShuffleQueryStageExec`, but can be the shuffle exchange + * node during canonicalization. + * @param partitionIndex The pre shuffle partition index. + * @param startMapId The start map id. + * @param endMapId The end map id. + */ +case class SkewedShufflePartitionReader( + child: QueryStageExec, + partitionIndex: Int, + startMapId: Int, Review comment: nit: mapIndex ---------------------------------------------------------------- This is an automated message from 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