spark 3.0.1中AQE配置的示例分析

这篇文章主要介绍spark 3.0.1中AQE配置的示例分析，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！

AQE简介

从spark configuration,到在最早在spark 1.6版本就已经有了AQE;到了spark 2.x版本，intel大数据团队进行了相应的原型开发和实践；到了spark 3.0时代，Databricks和intel一起为社区贡献了新的AQE

spark 3.0.1中的AQE的配置

分析1

在OptimizeSkewedJoin.scala中，我们看到ADVISORY_PARTITION_SIZE_IN_BYTES，也就是spark.sql.adaptive.advisoryPartitionSizeInBytes被引用的地方, （OptimizeSkewedJoin是物理计划中的规则）

 /**   * 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)  }

其中：

1. nonSkewSizes为task非倾斜的分区

2. targetSize返回的是max(非倾斜的分区的平均值，advisorySize)，其中advisorySize为spark.sql.adaptive.advisoryPartitionSizeInBytes值，所以说 targetSize不一定是spark.sql.adaptive.advisoryPartitionSizeInBytes值

3. medianSize值为task的分区大小的中位值

分析2

在SQLConf.scala

def numShufflePartitions: Int = {    if (adaptiveExecutionEnabled && coalesceShufflePartitionsEnabled) {      getConf(COALESCE_PARTITIONS_INITIAL_PARTITION_NUM).getOrElse(defaultNumShufflePartitions)    } else {      defaultNumShufflePartitions    }  }

从spark 3.0.1开始如果开启了AQE和shuffle分区合并，则用的是spark.sql.adaptive.coalescePartitions.initialPartitionNum，这在如果有多个shuffle stage的情况下，增加分区数，可以有效的增强shuffle分区合并的效果

分析3

在CoalesceShufflePartitions.scala,CoalesceShufflePartitions是一个物理计划的规则，会执行如下操作

 if (!shuffleStages.forall(_.shuffle.canChangeNumPartitions)) {      plan    } else {      // `ShuffleQueryStageExec#mapStats` returns None when the input RDD has 0 partitions,      // we should skip it when calculating the `partitionStartIndices`.      val validMetrics = shuffleStages.flatMap(_.mapStats)      // We may have different pre-shuffle partition numbers, don't reduce shuffle partition number      // in that case. For example when we union fully aggregated data (data is arranged to a single      // partition) and a result of a SortMergeJoin (multiple partitions).      val distinctNumPreShufflePartitions =        validMetrics.map(stats => stats.bytesByPartitionId.length).distinct      if (validMetrics.nonEmpty && distinctNumPreShufflePartitions.length == 1) {        // We fall back to Spark default parallelism if the minimum number of coalesced partitions        // is not set, so to avoid perf regressions compared to no coalescing.        val minPartitionNum = conf.getConf(SQLConf.COALESCE_PARTITIONS_MIN_PARTITION_NUM)          .getOrElse(session.sparkContext.defaultParallelism)        val partitionSpecs = ShufflePartitionsUtil.coalescePartitions(          validMetrics.toArray,          advisoryTargetSize = conf.getConf(SQLConf.ADVISORY_PARTITION_SIZE_IN_BYTES),          minNumPartitions = minPartitionNum)        // This transformation adds new nodes, so we must use `transformUp` here.        val stageIds = shuffleStages.map(_.id).toSet        plan.transformUp {          // even for shuffle exchange whose input RDD has 0 partition, we should still update its          // `partitionStartIndices`, so that all the leaf shuffles in a stage have the same          // number of output partitions.          case stage: ShuffleQueryStageExec if stageIds.contains(stage.id) =>            CustomShuffleReaderExec(stage, partitionSpecs, COALESCED_SHUFFLE_READER_DESCRIPTION)        }      } else {        plan      }    }  }

也就是说：

1. 如果是用户自己指定的分区操作，如repartition操作，spark.sql.adaptive.coalescePartitions.minPartitionNum无效,且跳过分区合并优化

2. 如果多个task进行shuffle，且task有不同的分区数的话，spark.sql.adaptive.coalescePartitions.minPartitionNum无效,且跳过分区合并优化

3. 见ShufflePartitionsUtil.coalescePartition分析

分析4

在OptimizeSkewedJoin.scala中，我们看到

/**   * A partition is considered as a skewed partition if its size is larger than the median   * partition size * ADAPTIVE_EXECUTION_SKEWED_PARTITION_FACTOR and also larger than   * ADVISORY_PARTITION_SIZE_IN_BYTES.   */  private def isSkewed(size: Long, medianSize: Long): Boolean = {    size > medianSize * conf.getConf(SQLConf.SKEW_JOIN_SKEWED_PARTITION_FACTOR) &&      size > conf.getConf(SQLConf.SKEW_JOIN_SKEWED_PARTITION_THRESHOLD)  }

1. OptimizeSkewedJoin是个物理计划的规则，会根据isSkewed来判断是否数据数据有倾斜，而且必须是满足SKEW_JOIN_SKEWED_PARTITION_FACTOR和SKEW_JOIN_SKEWED_PARTITION_THRESHOLD才会判断为数据倾斜了

2. medianSize为task的分区大小的中位值

分析5

在AdaptiveSparkPlanExec方法getFinalPhysicalPlan中调用了reOptimize方法，而reOptimize方法则会执行逻辑计划的优化操作：

private def reOptimize(logicalPlan: LogicalPlan): (SparkPlan, LogicalPlan) = {    logicalPlan.invalidateStatsCache()    val optimized = optimizer.execute(logicalPlan)    val sparkPlan = context.session.sessionState.planner.plan(ReturnAnswer(optimized)).next()    val newPlan = applyPhysicalRules(sparkPlan, preprocessingRules ++ queryStagePreparationRules)    (newPlan, optimized)  }

而optimizer 中有个DemoteBroadcastHashJoin规则：

@transient privateval optimizer = new RuleExecutor[LogicalPlan] {    // TODO add more optimization rules    override protected def batches: Seq[Batch] = Seq(      Batch("Demote BroadcastHashJoin", Once, DemoteBroadcastHashJoin(conf))    )  }

而对于DemoteBroadcastHashJoin则有对是否broadcastjoin的判断：

case class DemoteBroadcastHashJoin(conf: SQLConf) extends Rule[LogicalPlan] {  private def shouldDemote(plan: LogicalPlan): Boolean = plan match {    case LogicalQueryStage(_, stage: ShuffleQueryStageExec) if stage.resultOption.isDefined      && stage.mapStats.isDefined =>      val mapStats = stage.mapStats.get      val partitionCnt = mapStats.bytesByPartitionId.length      val nonZeroCnt = mapStats.bytesByPartitionId.count(_ > 0)      partitionCnt > 0 && nonZeroCnt > 0 &&        (nonZeroCnt * 1.0 / partitionCnt) < conf.nonEmptyPartitionRatioForBroadcastJoin    case _ => false  }  def apply(plan: LogicalPlan): LogicalPlan = plan.transformDown {    case j @ Join(left, right, _, _, hint) =>      var newHint = hint      if (!hint.leftHint.exists(_.strategy.isDefined) && shouldDemote(left)) {        newHint = newHint.copy(leftHint =          Some(hint.leftHint.getOrElse(HintInfo()).copy(strategy = Some(NO_BROADCAST_HASH))))      }      if (!hint.rightHint.exists(_.strategy.isDefined) && shouldDemote(right)) {        newHint = newHint.copy(rightHint =          Some(hint.rightHint.getOrElse(HintInfo()).copy(strategy = Some(NO_BROADCAST_HASH))))      }      if (newHint.ne(hint)) {        j.copy(hint = newHint)      } else {        j      }  }}

shouldDemote就是对是否进行broadcastjoin的判断:

1. 首先得是ShuffleQueryStageExec操作

2. 如果非空分区比列大于nonEmptyPartitionRatioForBroadcastJoin,也就是spark.sql.adaptive.nonEmptyPartitionRatioForBroadcastJoin,则不会把mergehashjoin转换为broadcastJoin

3. 这在sql中先join在groupby的场景中比较容易出现

ShufflePartitionsUtil.coalescePartition分析（合并分区的核心代码）

见coalescePartition如示：

def coalescePartitions(      mapOutputStatistics: Array[MapOutputStatistics],      advisoryTargetSize: Long,      minNumPartitions: Int): Seq[ShufflePartitionSpec] = {    // If `minNumPartitions` is very large, it is possible that we need to use a value less than    // `advisoryTargetSize` as the target size of a coalesced task.    val totalPostShuffleInputSize = mapOutputStatistics.map(_.bytesByPartitionId.sum).sum    // The max at here is to make sure that when we have an empty table, we only have a single    // coalesced partition.    // There is no particular reason that we pick 16. We just need a number to prevent    // `maxTargetSize` from being set to 0.    val maxTargetSize = math.max(      math.ceil(totalPostShuffleInputSize / minNumPartitions.toDouble).toLong, 16)    val targetSize = math.min(maxTargetSize, advisoryTargetSize)    val shuffleIds = mapOutputStatistics.map(_.shuffleId).mkString(", ")    logInfo(s"For shuffle($shuffleIds), advisory target size: $advisoryTargetSize, " +      s"actual target size $targetSize.")    // Make sure these shuffles have the same number of partitions.    val distinctNumShufflePartitions =      mapOutputStatistics.map(stats => stats.bytesByPartitionId.length).distinct    // The reason that we are expecting a single value of the number of shuffle partitions    // is that when we add Exchanges, we set the number of shuffle partitions    // (i.e. map output partitions) using a static setting, which is the value of    // `spark.sql.shuffle.partitions`. Even if two input RDDs are having different    // number of partitions, they will have the same number of shuffle partitions    // (i.e. map output partitions).    assert(      distinctNumShufflePartitions.length == 1,      "There should be only one distinct value of the number of shuffle partitions " +        "among registered Exchange operators.")    val numPartitions = distinctNumShufflePartitions.head    val partitionSpecs = ArrayBuffer[CoalescedPartitionSpec]()    var latestSplitPoint = 0    var coalescedSize = 0L    var i = 0    while (i < numPartitions) {      // We calculate the total size of i-th shuffle partitions from all shuffles.      var totalSizeOfCurrentPartition = 0L      var j = 0      while (j < mapOutputStatistics.length) {        totalSizeOfCurrentPartition += mapOutputStatistics(j).bytesByPartitionId(i)        j += 1      }      // If including the `totalSizeOfCurrentPartition` would exceed the target size, then start a      // new coalesced partition.      if (i > latestSplitPoint && coalescedSize + totalSizeOfCurrentPartition > targetSize) {        partitionSpecs += CoalescedPartitionSpec(latestSplitPoint, i)        latestSplitPoint = i        // reset postShuffleInputSize.        coalescedSize = totalSizeOfCurrentPartition      } else {        coalescedSize += totalSizeOfCurrentPartition      }      i += 1    }    partitionSpecs += CoalescedPartitionSpec(latestSplitPoint, numPartitions)    partitionSpecs  }

1. totalPostShuffleInputSize 先计算出总的shuffle的数据大小

2. maxTargetSize取max(totalPostShuffleInputSize/minNumPartitions,16）的最大值,minNumPartitions也就是spark.sql.adaptive.coalescePartitions.minPartitionNum的值

3. targetSize取min(maxTargetSize,advisoryTargetSize),advisoryTargetSize也就是spark.sql.adaptive.advisoryPartitionSizeInBytes的值，所以说该值只是建议值，不一定是targetSize

4. while循环就是取相邻的分区合并，对于每个task中的每个相邻分区合并，直到不大于targetSize

OptimizeSkewedJoin.optimizeSkewJoin分析(数据倾斜优化的核心代码)

见optimizeSkewJoin如示：

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      // Use the median size of the actual (coalesced) partition sizes to detect skewed partitions.      val leftMedSize = medianSize(left.partitionsWithSizes.map(_._2))      val rightMedSize = medianSize(right.partitionsWithSizes.map(_._2))      logDebug(        s"""          |Optimizing skewed join.          |Left side partitions size info:          |${getSizeInfo(leftMedSize, left.partitionsWithSizes.map(_._2))}          |Right side partitions size info:          |${getSizeInfo(rightMedSize, right.partitionsWithSizes.map(_._2))}        """.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)      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        val isRightSkew = isSkewed(rightActualSizes(partitionIndex), rightMedSize) && canSplitRight        val rightPartSpec = right.partitionsWithSizes(partitionIndex)._1        val isRightCoalesced = rightPartSpec.startReducerIndex + 1 < rightPartSpec.endReducerIndex        // A skewed partition should never be coalesced, but skip it here just to be safe.        val leftParts = if (isLeftSkew && !isLeftCoalesced) {          val reducerId = leftPartSpec.startReducerIndex          val skewSpecs = createSkewPartitionSpecs(            left.mapStats.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)        }        // 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.mapStats.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)        }        for {          leftSidePartition <- leftParts          rightSidePartition <- rightParts        } {          leftSidePartitions += leftSidePartition          rightSidePartitions += rightSidePartition        }      }      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      }  }

1. SortMergeJoinExec说明适用于sort merge join

2. assert(left.partitionsWithSizes.length == right.partitionsWithSizes.length)保证进行join的两个task的分区数相等

3. 分别计算进行join的task的分区中位数的大小leftMedSize和rightMedSize

4. 分别计算进行join的task的分区的targetzise大小leftTargetSize和rightTargetSize

5. 循环判断两个task的每个分区的是否存在倾斜，如果倾斜且满足没有进行过shuffle分区合并，则进行倾斜分区处理，否则不处理

6. createSkewPartitionSpecs方法为： 1.获取每个join的task的对应分区的数据大小 2.根据targetSize分成多个slice

7. 如果存在数据倾斜，则构造包装成CustomShuffleReaderExec，进行后续任务的运行，最最终调用ShuffledRowRDD的compute方法 匹配case PartialMapperPartitionSpec进行数据的读取，其中还会自动开启"spark.sql.adaptive.fetchShuffleBlocksInBatch"批量fetch减少io

OptimizeSkewedJoin/CoalesceShufflePartitions 在哪里被调用

如：AdaptiveSparkPlanExec

@transient privateval queryStageOptimizerRules: Seq[Rule[SparkPlan]] = Seq(    ReuseAdaptiveSubquery(conf, context.subqueryCache),    CoalesceShufflePartitions(context.session),    // The following two rules need to make use of 'CustomShuffleReaderExec.partitionSpecs'    // added by `CoalesceShufflePartitions`. So they must be executed after it.    OptimizeSkewedJoin(conf),    OptimizeLocalShuffleReader(conf)  )

可见在AdaptiveSparkPlanExec中被调用 ，且CoalesceShufflePartitions先于OptimizeSkewedJoin, 而AdaptiveSparkPlanExec在InsertAdaptiveSparkPlan中被调用 ,而InsertAdaptiveSparkPlan在QueryExecution中被调用

而在InsertAdaptiveSparkPlan.shouldApplyAQE方法和supportAdaptive中我们看到

private def shouldApplyAQE(plan: SparkPlan, isSubquery: Boolean): Boolean = {    conf.getConf(SQLConf.ADAPTIVE_EXECUTION_FORCE_APPLY) || isSubquery || {      plan.find {        case _: Exchange => true        case p if !p.requiredChildDistribution.forall(_ == UnspecifiedDistribution) => true        case p => p.expressions.exists(_.find {          case _: SubqueryExpression => true          case _ => false        }.isDefined)      }.isDefined    }  }private def supportAdaptive(plan: SparkPlan): Boolean = {    // TODO migrate dynamic-partition-pruning onto adaptive execution.    sanityCheck(plan) &&      !plan.logicalLink.exists(_.isStreaming) &&      !plan.expressions.exists(_.find(_.isInstanceOf[DynamicPruningSubquery]).isDefined) &&    plan.children.forall(supportAdaptive)  }

如果不满足以上条件也是不会开启AQE的，如果要强制开启，也可以配置spark.sql.adaptive.forceApply 为true（文档中提示是内部配置）

注意：

在spark 3.0.1中已经废弃了如下的配置：

spark.sql.adaptive.skewedPartitionMaxSplits    spark.sql.adaptive.skewedPartitionRowCountThreshold    spark.sql.adaptive.skewedPartitionSizeThreshold

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