[SPARK-31134][SQL] optimize skew join after shuffle partitions are coalesced #27893
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| Original file line number | Diff line number | Diff line change |
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@@ -21,7 +21,7 @@ import scala.collection.mutable | |
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| import org.apache.commons.io.FileUtils | ||
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| import org.apache.spark.{MapOutputStatistics, MapOutputTrackerMaster, SparkEnv} | ||
| import org.apache.spark.sql.catalyst.plans._ | ||
| import org.apache.spark.sql.catalyst.rules.Rule | ||
| import org.apache.spark.sql.execution._ | ||
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@@ -83,14 +83,14 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] { | |
| /** | ||
| * The goal of skew join optimization is to make the data distribution more even. The target size | ||
| * to split skewed partitions is the average size of non-skewed partition, or the | ||
| * advisory partition size if avg size is smaller than it. | ||
| */ | ||
| private def targetSize(sizes: Seq[Long], medianSize: Long): Long = { | ||
| val advisorySize = conf.getConf(SQLConf.ADVISORY_PARTITION_SIZE_IN_BYTES) | ||
| val nonSkewSizes = sizes.filterNot(isSkewed(_, medianSize)) | ||
| // It's impossible that all the partitions are skewed, as we use median size to define skew. | ||
| assert(nonSkewSizes.nonEmpty) | ||
| math.max(advisorySize, nonSkewSizes.sum / nonSkewSizes.length) | ||
| } | ||
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| /** | ||
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@@ -102,21 +102,29 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] { | |
| } | ||
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| /** | ||
| * Splits the skewed partition based on the map size and the target partition size | ||
| * after split, and create a list of `PartialMapperPartitionSpec`. Returns None if can't split. | ||
| */ | ||
| private def createSkewPartitionSpecs( | ||
| shuffleId: Int, | ||
| reducerId: Int, | ||
| targetSize: Long): Option[Seq[PartialReducerPartitionSpec]] = { | ||
| val mapPartitionSizes = getMapSizesForReduceId(shuffleId, reducerId) | ||
| val mapStartIndices = ShufflePartitionsUtil.splitSizeListByTargetSize( | ||
| mapPartitionSizes, targetSize) | ||
| if (mapStartIndices.length > 1) { | ||
| Some(mapStartIndices.indices.map { i => | ||
| val startMapIndex = mapStartIndices(i) | ||
| val endMapIndex = if (i == mapStartIndices.length - 1) { | ||
| mapPartitionSizes.length | ||
| } else { | ||
| mapStartIndices(i + 1) | ||
| } | ||
| PartialReducerPartitionSpec(reducerId, startMapIndex, endMapIndex) | ||
| }) | ||
| } else { | ||
| None | ||
| } | ||
| } | ||
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| private def canSplitLeftSide(joinType: JoinType) = { | ||
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@@ -128,12 +136,9 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] { | |
| joinType == Inner || joinType == Cross || joinType == RightOuter | ||
| } | ||
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| private def getSizeInfo(medianSize: Long, sizes: Seq[Long]): String = { | ||
| s"median size: $medianSize, max size: ${sizes.max}, min size: ${sizes.min}, avg size: " + | ||
| sizes.sum / sizes.length | ||
| } | ||
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| /* | ||
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@@ -150,146 +155,97 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] { | |
| */ | ||
| def optimizeSkewJoin(plan: SparkPlan): SparkPlan = plan.transformUp { | ||
| case smj @ SortMergeJoinExec(_, _, joinType, _, | ||
| s1 @ SortExec(_, _, ShuffleStage(left: ShuffleStageInfo), _), | ||
| s2 @ SortExec(_, _, ShuffleStage(right: ShuffleStageInfo), _), _) | ||
| if supportedJoinTypes.contains(joinType) => | ||
| assert(left.partitionsWithSizes.length == right.partitionsWithSizes.length) | ||
| val numPartitions = left.partitionsWithSizes.length | ||
| // We use the median size of the original shuffle partitions to detect skewed partitions. | ||
| val leftMedSize = medianSize(left.mapStats) | ||
| val rightMedSize = medianSize(right.mapStats) | ||
| logDebug( | ||
| s""" | ||
| |Optimizing skewed join. | ||
| |Left side partitions size info: | ||
| |${getSizeInfo(leftMedSize, left.mapStats.bytesByPartitionId)} | ||
| |Right side partitions size info: | ||
| |${getSizeInfo(rightMedSize, right.mapStats.bytesByPartitionId)} | ||
| """.stripMargin) | ||
| val canSplitLeft = canSplitLeftSide(joinType) | ||
| val canSplitRight = canSplitRightSide(joinType) | ||
| // We use the actual partition sizes (may be coalesced) to calculate target size, so that | ||
| // the final data distribution is even (coalesced partitions + split partitions). | ||
| val leftActualSizes = left.partitionsWithSizes.map(_._2) | ||
| val rightActualSizes = right.partitionsWithSizes.map(_._2) | ||
| val leftTargetSize = targetSize(leftActualSizes, leftMedSize) | ||
| val rightTargetSize = targetSize(rightActualSizes, rightMedSize) | ||
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| val leftSidePartitions = mutable.ArrayBuffer.empty[ShufflePartitionSpec] | ||
| val rightSidePartitions = mutable.ArrayBuffer.empty[ShufflePartitionSpec] | ||
| val leftSkewDesc = new SkewDesc | ||
| val rightSkewDesc = new SkewDesc | ||
| for (partitionIndex <- 0 until numPartitions) { | ||
| val isLeftSkew = isSkewed(leftActualSizes(partitionIndex), leftMedSize) && canSplitLeft | ||
| val leftPartSpec = left.partitionsWithSizes(partitionIndex)._1 | ||
| val isLeftCoalesced = leftPartSpec.startReducerIndex + 1 < leftPartSpec.endReducerIndex | ||
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| val isRightSkew = isSkewed(rightActualSizes(partitionIndex), rightMedSize) && canSplitRight | ||
| val rightPartSpec = right.partitionsWithSizes(partitionIndex)._1 | ||
| val isRightCoalesced = rightPartSpec.startReducerIndex + 1 < rightPartSpec.endReducerIndex | ||
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| // A skewed partition should never be coalesced, but skip it here just to be safe. | ||
| val leftParts = if (isLeftSkew && !isLeftCoalesced) { | ||
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Contributor
Author
There was a problem hiding this comment. @JkSelf I tried to create a common method to handle both sides, but the method takes too many parameters so I give up. Besides, it's not much duplicated code here. |
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| val reducerId = leftPartSpec.startReducerIndex | ||
| val skewSpecs = createSkewPartitionSpecs( | ||
| left.shuffleStage.shuffle.shuffleDependency.shuffleId, reducerId, leftTargetSize) | ||
| if (skewSpecs.isDefined) { | ||
| logDebug(s"Left side partition $partitionIndex is skewed, split it into " + | ||
| s"${skewSpecs.get.length} parts.") | ||
| leftSkewDesc.addPartitionSize(leftActualSizes(partitionIndex)) | ||
| } | ||
| skewSpecs.getOrElse(Seq(leftPartSpec)) | ||
| } else { | ||
| Seq(leftPartSpec) | ||
| } | ||
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| // A skewed partition should never be coalesced, but skip it here just to be safe. | ||
| val rightParts = if (isRightSkew && !isRightCoalesced) { | ||
| val reducerId = rightPartSpec.startReducerIndex | ||
| val skewSpecs = createSkewPartitionSpecs( | ||
| right.shuffleStage.shuffle.shuffleDependency.shuffleId, reducerId, rightTargetSize) | ||
| if (skewSpecs.isDefined) { | ||
| logDebug(s"Right side partition $partitionIndex is skewed, split it into " + | ||
| s"${skewSpecs.get.length} parts.") | ||
| rightSkewDesc.addPartitionSize(rightActualSizes(partitionIndex)) | ||
| } | ||
| skewSpecs.getOrElse(Seq(rightPartSpec)) | ||
| } else { | ||
| Seq(rightPartSpec) | ||
| } | ||
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| for { | ||
| leftSidePartition <- leftParts | ||
| rightSidePartition <- rightParts | ||
| } { | ||
| leftSidePartitions += leftSidePartition | ||
| rightSidePartitions += rightSidePartition | ||
| } | ||
| } | ||
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| logDebug("number of skewed partitions: " + | ||
| s"left ${leftSkewDesc.numPartitions}, right ${rightSkewDesc.numPartitions}") | ||
| if (leftSkewDesc.numPartitions > 0 || rightSkewDesc.numPartitions > 0) { | ||
| val newLeft = CustomShuffleReaderExec( | ||
| left.shuffleStage, leftSidePartitions, leftSkewDesc.toString) | ||
| val newRight = CustomShuffleReaderExec( | ||
| right.shuffleStage, rightSidePartitions, rightSkewDesc.toString) | ||
| smj.copy( | ||
| left = s1.copy(child = newLeft), right = s2.copy(child = newRight), isSkewJoin = true) | ||
| } else { | ||
| smj | ||
| } | ||
| } | ||
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| override def apply(plan: SparkPlan): SparkPlan = { | ||
| if (!conf.getConf(SQLConf.SKEW_JOIN_ENABLED)) { | ||
| return plan | ||
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@@ -328,6 +284,48 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] { | |
| } | ||
| } | ||
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| private object ShuffleStage { | ||
| def unapply(plan: SparkPlan): Option[ShuffleStageInfo] = plan match { | ||
| case s: ShuffleQueryStageExec => | ||
| val mapStats = getMapStats(s) | ||
| val sizes = mapStats.bytesByPartitionId | ||
| val partitions = sizes.zipWithIndex.map { | ||
| case (size, i) => CoalescedPartitionSpec(i, i + 1) -> size | ||
| } | ||
| Some(ShuffleStageInfo(s, mapStats, partitions)) | ||
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| case CustomShuffleReaderExec(s: ShuffleQueryStageExec, partitionSpecs, _) => | ||
| val mapStats = getMapStats(s) | ||
| val sizes = mapStats.bytesByPartitionId | ||
| val partitions = partitionSpecs.map { | ||
| case spec @ CoalescedPartitionSpec(start, end) => | ||
| var sum = 0L | ||
|
Contributor
There was a problem hiding this comment. nit: sizes.slice(start, end).sum?
Contributor
Author
There was a problem hiding this comment.
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| var i = start | ||
| while (i < end) { | ||
| sum += sizes(i) | ||
| i += 1 | ||
| } | ||
| spec -> sum | ||
| case other => throw new IllegalArgumentException( | ||
| s"Expect CoalescedPartitionSpec but got $other") | ||
| } | ||
| Some(ShuffleStageInfo(s, mapStats, partitions)) | ||
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| case _ => None | ||
| } | ||
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| private def getMapStats(stage: ShuffleQueryStageExec): MapOutputStatistics = { | ||
| assert(stage.resultOption.isDefined, "ShuffleQueryStageExec should" + | ||
| " already be ready when executing OptimizeSkewedPartitions rule") | ||
| stage.resultOption.get.asInstanceOf[MapOutputStatistics] | ||
| } | ||
| } | ||
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| private case class ShuffleStageInfo( | ||
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| shuffleStage: ShuffleQueryStageExec, | ||
| mapStats: MapOutputStatistics, | ||
| partitionsWithSizes: Seq[(CoalescedPartitionSpec, Long)]) | ||
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| private class SkewDesc { | ||
| private[this] var numSkewedPartitions: Int = 0 | ||
| private[this] var totalSize: Long = 0 | ||
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Say we have original map output: 100, 10, 2000, and the coalesce target is 100. So, after
CoalesceShufflePartitions, we shall haveCoalescedPartitionSpec(0, 1)andCoalescedPartitionSpec(1, 3). Then, we start to applyOptimizeSkewedJoinwhereCoalescedPartitionSpec(1, 3)is obviously skewed but can be missed. Right?There was a problem hiding this comment.
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I don't think the coalesce rule will coalesce
10and2000, can you double check?There was a problem hiding this comment.
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Oh yeah, I checked, you're right!