JkSelf 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_r356386884
########## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedPartitions.scala ########## @@ -0,0 +1,301 @@ +/* + * 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 scala.concurrent.duration.Duration + +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 +import org.apache.spark.util.ThreadUtils + +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 bytesLen = stats.bytesByPartitionId.length + val bytes = stats.bytesByPartitionId.sorted + if (bytes(bytesLen / 2) > 0) bytes(bytesLen / 2) else 1 + } + + /** + * Get all the map data size for specific reduce partitionId. + */ + def getMapSizeForSpecificPartition(partitionId: Int, shuffleId: Int): Array[Long] = { + val mapOutputTracker = SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster] + mapOutputTracker.shuffleStatuses.get(shuffleId). + get.mapStatuses.map{_.getSizeForBlock(partitionId)} + } + + /** + * Split the partition into the number of mappers. Each split read data from each mapper. + */ + private def estimateMapIdStartIndices( + stage: QueryStageExec, + partitionId: Int, + medianSize: Long): Array[Int] = { + val dependency = getShuffleStage(stage).plan.shuffleDependency + val numMappers = dependency.rdd.partitions.length + (0 until numMappers).toArray + } + + private def getShuffleStage(queryStage: QueryStageExec): ShuffleQueryStageExec = { + queryStage match { + case stage: ShuffleQueryStageExec => stage + case ReusedQueryStageExec(_, stage: ShuffleQueryStageExec, _) => stage + } + } + + private def getStatistics(queryStage: QueryStageExec): MapOutputStatistics = { + val shuffleStage = getShuffleStage(queryStage) + val metrics = shuffleStage.mapOutputStatisticsFuture + assert(metrics.isCompleted, + "ShuffleQueryStageExec should already be ready when executing OptimizeSkewedPartitions rule") + ThreadUtils.awaitResult(metrics, Duration.Zero) + } + + /** + * Base optimization support check: the join type is supported. + * Note that for some join types(like left outer), whether a certain partition can be optimized + * also depends on the filed isSkewAndSupportsSplit. + */ + private def supportOptimization( + joinType: JoinType, + leftStage: QueryStageExec, + rightStage: QueryStageExec): Boolean = { + val joinTypeSupported = supportedJoinTypes.contains(joinType) + val shuffleStageCheck = ShuffleQueryStageExec.isShuffleQueryStageExec(leftStage) && + ShuffleQueryStageExec.isShuffleQueryStageExec(rightStage) + joinTypeSupported && shuffleStageCheck + } + + 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 estimatePartitionStartEndIndices( + mapOutputStatistics: MapOutputStatistics, + omittedPartitions: mutable.HashSet[Int] = mutable.HashSet.empty): Array[(Int, Int)] = { + val length = mapOutputStatistics.bytesByPartitionId.length + val partitionStartIndices = ArrayBuffer[Int]() + val partitionEndIndices = ArrayBuffer[Int]() + (0 until length).map { i => + if (!omittedPartitions.contains(i)) { + partitionStartIndices += i + partitionEndIndices += i + 1 + } + } + partitionStartIndices.zip(partitionEndIndices).toArray + } + + private def getMappersNum(stage: QueryStageExec): Int = { + getShuffleStage(stage).plan.shuffleDependency.rdd.partitions.length + } + + def handleSkewJoin(plan: SparkPlan): SparkPlan = plan.transformUp { + case smj @ SortMergeJoinExec(leftKeys, rightKeys, joinType, condition, + SortExec(_, _, left: QueryStageExec, _), + SortExec(_, _, right: QueryStageExec, _)) + if supportOptimization(joinType, left, right) => + val leftStats = getStatistics(left) + val rightStats = getStatistics(right) + val numPartitions = leftStats.bytesByPartitionId.length + + val leftMedSize = medianSize(leftStats) + val rightMedSize = medianSize(rightStats) + logDebug(s"HandlingSkewedJoin left medSize: ($leftMedSize)" + + s" right medSize ($rightMedSize)") + logDebug(s"left bytes Max : ${leftStats.bytesByPartitionId.max}") + logDebug(s"right bytes Max : ${rightStats.bytesByPartitionId.max}") + + 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 isSkewAndSupportsSplit = + (isLeftSkew && supportSplitOnLeftPartition(joinType)) || + (isRightSkew && supportSplitOnRightPartition(joinType)) + + if (isSkewAndSupportsSplit) { + skewedPartitions += partitionId + val leftMapIdStartIndices = if (isLeftSkew && supportSplitOnLeftPartition(joinType)) { + estimateMapIdStartIndices(left, partitionId, leftMedSize) + } else { + Array(0) + } + val rightMapIdStartIndices = if (isRightSkew && supportSplitOnRightPartition(joinType)) { + estimateMapIdStartIndices(right, partitionId, rightMedSize) + } else { + Array(0) + } + + for (i <- 0 until leftMapIdStartIndices.length; + j <- 0 until rightMapIdStartIndices.length) { + val leftEndMapId = if (i == leftMapIdStartIndices.length - 1) { + getMappersNum(left) + } else { + leftMapIdStartIndices(i + 1) + } + val rightEndMapId = if (j == rightMapIdStartIndices.length - 1) { + getMappersNum(right) + } else { + rightMapIdStartIndices(j + 1) + } + // TODO we may can optimize the sort merge join to broad cast join after + // we get 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.size > 0) { + val partitionIndices = estimatePartitionStartEndIndices( + getStatistics(left), skewedPartitions) + val optimizedSmj = smj.transformDown { + case sort: SortExec if (sort.child.isInstanceOf[QueryStageExec] && + ShuffleQueryStageExec.isShuffleQueryStageExec(sort.child)) => { + val partialReader = PartialShuffleReader( + sort.child.asInstanceOf[QueryStageExec], partitionIndices) + sort.copy(child = partialReader) + } + } + 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 _: SkewedShufflePartitionReader => Nil + case _: PartialShuffleReader => Nil + case _: CoalescedShuffleReaderExec => Nil + case stage: ShuffleQueryStageExec => Seq(stage) + case ReusedQueryStageExec(_, 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 + + } + } +} + +case class PartialShuffleReader( + child: QueryStageExec, partitionRanges: Array[(Int, Int)]) extends UnaryExecNode { Review comment: If so, we may re-calculated the `mapstatistics` of `QueryStage` in this class? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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