【spark系列3】spark 3.0.1 AQE(Adaptive Query Exection)分析

原创
2020/12/01 15:46
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AQE简介

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

配置项 默认值 官方说明 分析
spark.sql.adaptive.enabled false 是否开启自适应查询 此处设置为true开启
spark.sql.adaptive.coalescePartitions.enabled true 是否合并临近的shuffle分区(根据'spark.sql.adaptive.advisoryPartitionSizeInBytes'的阈值来合并) 此处默认为true开启,分析见: 分析1
spark.sql.adaptive.coalescePartitions.initialPartitionNum (none) shuffle合并分区之前的初始分区数,默认为spark.sql.shuffle.partitions的值 分析见:分析2
spark.sql.adaptive.coalescePartitions.minPartitionNum (none) shuffle 分区合并后的最小分区数,默认为spark集群的默认并行度 分析见: 分析3
spark.sql.adaptive.advisoryPartitionSizeInBytes 64MB 建议的shuffle分区的大小,在合并分区和处理join数据倾斜的时候用到 分析见:分析3
spark.sql.adaptive.skewJoin.enabled true 是否开启join中数据倾斜的自适应处理
spark.sql.adaptive.skewJoin.skewedPartitionFactor 5 数据倾斜判断因子,必须同时满足skewedPartitionFactor和skewedPartitionThresholdInBytes 分析见:分析4
spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes 256MB 数据倾斜判断阈值,必须同时满足skewedPartitionFactor和skewedPartitionThresholdInBytes 分析见:分析4
spark.sql.adaptive.logLevel debug 配置自适应执行的计划改变日志 调整为info级别,便于观察自适应计划的改变
spark.sql.adaptive.nonEmptyPartitionRatioForBroadcastJoin 0.2 转为broadcastJoin的非空分区比例阈值,>=该值,将不会转换为broadcastjoin 分析见:分析5

分析1

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

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

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

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 private val 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)

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)

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 private val 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   

本文部分参考: https://mp.weixin.qq.com/s?__biz=MzA5MTc0NTMwNQ==&mid=2650718363&idx=1&sn=d20fffebafdd2bed6939eaeb39f5e6e3
https://mp.weixin.qq.com/s/RvFpXWpV8APcGTHhftS6NQ

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