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_r344828826
########## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedPartitions.scala ########## @@ -0,0 +1,261 @@ +/* + * 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.concurrent.duration.Duration + +import org.apache.spark.MapOutputStatistics +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.adaptiveSkewedFactor && + size > conf.adaptiveSkewedSizeThreshold + } + + 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 + } + + /** + * To equally divide n elements into m buckets, basically each bucket should have n/m elements, + * for the remaining n%m elements, add one more element to the first n%m buckets each. Returns + * a sequence with length numBuckets and each value represents the start index of each bucket. + */ + def equallyDivide(numElements: Int, numBuckets: Int): Seq[Int] = { + val elementsPerBucket = numElements / numBuckets + val remaining = numElements % numBuckets + val splitPoint = (elementsPerBucket + 1) * remaining + (0 until remaining).map(_ * (elementsPerBucket + 1)) ++ + (remaining until numBuckets).map(i => splitPoint + (i - remaining) * elementsPerBucket) + } + + /** + * We split the partition into several splits. Each split reads the data from several map outputs + * ranging from startMapId to endMapId(exclusive). This method calculates the split number and + * the startMapId for all splits. + */ + private def estimateMapIdStartIndices( + stage: QueryStageExec, + partitionId: Int, + medianSize: Long): Array[Int] = { + val metrics = getStatistics(stage) + val size = metrics.bytesByPartitionId(partitionId) + val factor = size / medianSize + val numMappers = getShuffleStage(stage). + plan.shuffleDependency.rdd.partitions.length + val numSplits = Math.min(conf.adaptiveSkewedMaxSplits, + Math.min(factor.toInt, numMappers)) + equallyDivide(numMappers, numSplits).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 = queryStage match { + case stage: ShuffleQueryStageExec => stage + case ReusedQueryStageExec(_, stage: ShuffleQueryStageExec, _) => stage + } + val metrics = shuffleStage.mapOutputStatisticsFuture + assert(metrics.isCompleted, "ShuffleQueryStageExec should already be ready") + ThreadUtils.awaitResult(metrics, Duration.Zero) + } + + /** + * Base optimization support check: the join type is supported and plan statistics is available. + * 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) + val statisticsReady: Boolean = if (shuffleStageCheck) { + getStatistics(leftStage) != null && getStatistics(rightStage) != null + } else false + + joinTypeSupported && statisticsReady + } + + private def supportSplitOnLeftPartition(joinType: JoinType) = joinType != RightOuter + + private def supportSplitOnRightPartition(joinType: JoinType) = { + joinType != LeftOuter && joinType != LeftSemi && joinType != LeftAnti + } + + 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) + logInfo(s"HandlingSkewedJoin left medSize: ($leftMedSize)" + + s" right medSize ($rightMedSize)") + logInfo(s"left bytes Max : ${leftStats.bytesByPartitionId.max}") + logInfo(s"right bytes Max : ${rightStats.bytesByPartitionId.max}") + + val skewedPartitions = mutable.HashSet[Int]() + val subJoins = mutable.ArrayBuffer[SparkPlan](smj) + 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) { + getShuffleStage(left).plan.shuffleDependency.rdd.partitions.length + } else { + leftMapIdStartIndices(i + 1) + } + val rightEndMapId = if (j == rightMapIdStartIndices.length - 1) { + getShuffleStage(right). + plan.shuffleDependency.rdd.partitions.length + } else { + rightMapIdStartIndices(j + 1) + } + // For the skewed partition, we set the id of shuffle query stage to -1. + // And skip this shuffle query stage optimization in 'ReduceNumShufflePartitions' rule. + val leftSkewedReader = + SkewedShuffleReaderExec(getShuffleStage(left).copy(id = -1), + partitionId, leftMapIdStartIndices(i), leftEndMapId) + + val rightSkewedReader = + SkewedShuffleReaderExec(getShuffleStage(right).copy(id = -1), + partitionId, rightMapIdStartIndices(j), rightEndMapId) + + subJoins += + SortMergeJoinExec(leftKeys, rightKeys, joinType, condition, + leftSkewedReader, rightSkewedReader) + } + } + } + logInfo(s"skewed partition number is ${skewedPartitions.size}") + if (skewedPartitions.size > 0) { + getShuffleStage(left).skewedPartitions = skewedPartitions + getShuffleStage(right).skewedPartitions = skewedPartitions + UnionExec(subJoins.toList) + } else { + smj + } + } + + override def apply(plan: SparkPlan): SparkPlan = { + if (!conf.adaptiveSkewedJoinEnabled) { + return plan + } + + def collectShuffleStages(plan: SparkPlan): Seq[ShuffleQueryStageExec] = plan match { + case _: LocalShuffleReaderExec => Nil + case _: SkewedShuffleReaderExec => 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 SkewedShuffleReaderExec( + child: QueryStageExec, + partitionIndex: Int, + startMapId: Int, + endMapId: Int) extends UnaryExecNode { Review comment: shall we make it a leaf node? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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