spark3.0.1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！
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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
| 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
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：
nonSkewSizes为task非倾斜的分区
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
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：
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
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
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)
}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
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:
首先得是ShuffleQueryStageExec操作
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
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
}totalPostShuffleInputSize 先计算出总的shuffle的数据大小
maxTargetSize取max(totalPostShuffleInputSize/minNumPartitions,16）的最大值,minNumPartitions也就是spark.sql.adaptive.coalescePartitions.minPartitionNum的值
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
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
}
}SortMergeJoinExec说明适用于sort merge join
assert(left.partitionsWithSizes.length == right.partitionsWithSizes.length)保证进行join的两个task的分区数相等
分别计算进行join的task的分区中位数的大小leftMedSize和rightMedSize
分别计算进行join的task的分区的targetzise大小leftTargetSize和rightTargetSize
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
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
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
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文章名称：spark3.0.1中AQE配置的示例分析
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