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Spark SQL如何选择join策略

大数据学习与分享 人气:0

前言

众所周知，Catalyst Optimizer是Spark SQL的核心，它主要负责将SQL语句转换成最终的物理执行计划，在一定程度上决定了SQL执行的性能。

Catalyst在由Optimized Logical Plan生成Physical Plan的过程中，会根据：

abstract class SparkStrategies extends QueryPlanner[SparkPlan]

 

中的JoinSelection通过一些规则按照顺序进行模式匹配，从而确定join的最终执行策略，并且策略的选择会按照执行效率由高到低的优先级排列。

在了解join策略选择之前，首先看几个先决条件：

1. build table的选择

Hash Join的第一步就是根据两表之中较小的那一个构建哈希表，这个小表就叫做build table，大表则称为probe table，因为需要拿小表形成的哈希表来"探测"它。源码如下：

/* 左表作为build table的条件，join类型需满足：
   1. InnerLike：实现目前包括inner join和cross join
   2. RightOuter：right outer join
*/      
private def canBuildLeft(joinType: JoinType): Boolean = joinType match {
  case _: InnerLike | RightOuter => true
  case _ => false
}

/* 右表作为build table的条件，join类型需满足（第1种是在业务开发中写的SQL主要适配的）：
   1. InnerLike、LeftOuter（left outer join）、LeftSemi（left semi join）、LeftAnti（left anti join）
   2. ExistenceJoin：only used in the end of optimizer and physical plans, we will not generate SQL for this join type
*/
private def canBuildRight(joinType: JoinType): Boolean = joinType match {
  case _: InnerLike | LeftOuter | LeftSemi | LeftAnti | _: ExistenceJoin => true
  case _ => false
}

 


2. 满足什么条件的表才能被广播

如果一个表的大小小于或等于参数spark.sql.autoBroadcastJoinThreshold（默认10M）配置的值，那么就可以广播该表。源码如下：

private def canBroadcastBySizes(joinType: JoinType, left: LogicalPlan, right: LogicalPlan)
  : Boolean = {
  val buildLeft = canBuildLeft(joinType) && canBroadcast(left)
  val buildRight = canBuildRight(joinType) && canBroadcast(right)
  buildLeft || buildRight
}

private def canBroadcast(plan: LogicalPlan): Boolean = {
  plan.stats.sizeInBytes >= 0 && plan.stats.sizeInBytes <= conf.autoBroadcastJoinThreshold
}

private def broadcastSideBySizes(joinType: JoinType, left: LogicalPlan, right: LogicalPlan)
  : BuildSide = {
  val buildLeft = canBuildLeft(joinType) && canBroadcast(left)
  val buildRight = canBuildRight(joinType) && canBroadcast(right)
  
  // 最终会调用broadcastSide
  broadcastSide(buildLeft, buildRight, left, right)
}

 


除了通过上述表的大小满足一定条件之外，我们也可以通过直接在Spark SQL中显示使用hint方式（/*+ BROADCAST(small_table) */），直接指定要广播的表，源码如下：

private def canBroadcastByHints(joinType: JoinType, left: LogicalPlan, right: LogicalPlan)
  : Boolean = {
  val buildLeft = canBuildLeft(joinType) && left.stats.hints.broadcast
  val buildRight = canBuildRight(joinType) && right.stats.hints.broadcast
  buildLeft || buildRight
}

private def broadcastSideByHints(joinType: JoinType, left: LogicalPlan, right: LogicalPlan)
  : BuildSide = {
  val buildLeft = canBuildLeft(joinType) && left.stats.hints.broadcast
  val buildRight = canBuildRight(joinType) && right.stats.hints.broadcast
  
  // 最终会调用broadcastSide
  broadcastSide(buildLeft, buildRight, left, right)
}

 


无论是通过表大小进行广播还是根据是否指定hint进行表广播，最终都会调用broadcastSide，来决定应该广播哪个表：

private def broadcastSide(
     canBuildLeft: Boolean,
     canBuildRight: Boolean,
     left: LogicalPlan,
     right: LogicalPlan): BuildSide = {

   def smallerSide =
     if (right.stats.sizeInBytes <= left.stats.sizeInBytes) BuildRight else BuildLeft

  if (canBuildRight && canBuildLeft) {
    // 如果左表和右表都能作为build table，则将根据表的统计信息，确定physical size较小的表作为build table（即使两个表都被指定了hint）
    smallerSide
  } else if (canBuildRight) {
     // 上述条件不满足，优先判断右表是否满足build条件，满足则广播右表。否则，接着判断左表是否满足build条件
    BuildRight
  } else if (canBuildLeft) {
    BuildLeft
  } else {
    // 如果左表和右表都不能作为build table，则将根据表的统计信息，确定physical size较小的表作为build table。目前主要用于broadcast nested loop join
    smallerSide
  }
}

 


从上述源码可知，即使用户指定了广播hint，实际执行时，不一定按照hint的表进行广播。

3. 是否可构造本地HashMap

应用于Shuffle Hash Join中，源码如下：

// 逻辑计划的单个分区足够小到构建一个hash表
// 注意：要求分区数是固定的。如果分区数是动态的，还需满足其他条件
private def canBuildLocalHashMap(plan: LogicalPlan): Boolean = {
  // 逻辑计划的physical size小于spark.sql.autoBroadcastJoinThreshold * spark.sql.shuffle.partitions（默认200）时，即可构造本地HashMap
  plan.stats.sizeInBytes < conf.autoBroadcastJoinThreshold * conf.numShufflePartitions
}

 

我们知道，SparkSQL目前主要实现了3种join：Broadcast Hash Join、ShuffledHashJoin、Sort Merge Join。那么Catalyst在处理SQL语句时，是依据什么规则进行join策略选择的呢？

1. Broadcast Hash Join

主要根据hint和size进行判断是否满足条件。

// broadcast hints were specified
case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
   if canBroadcastByHints(joinType, left, right) =>
   val buildSide = broadcastSideByHints(joinType, left, right)
   Seq(joins.BroadcastHashJoinExec(
     leftKeys, rightKeys, joinType, buildSide, condition, planLater(left), planLater(right)))

// broadcast hints were not specified, so need to infer it from size and configuration.
case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
   if canBroadcastBySizes(joinType, left, right) =>
   val buildSide = broadcastSideBySizes(joinType, left, right)
   Seq(joins.BroadcastHashJoinExec(
     leftKeys, rightKeys, joinType, buildSide, condition, planLater(left), planLater(right)))

 


2. Shuffle Hash Join

选择Shuffle Hash Join需要同时满足以下条件：   

  1. spark.sql.join.preferSortMergeJoin为false，即Shuffle Hash Join优先于Sort Merge Join

  2. 右表或左表是否能够作为build table  

  3. 是否能构建本地HashMap  

  4. 以右表为例，它的逻辑计划大小要远小于左表大小（默认3倍）

上述条件优先检查右表。

case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
    if !conf.preferSortMergeJoin && canBuildRight(joinType) && canBuildLocalHashMap(right)
      && muchSmaller(right, left) ||
      !RowOrdering.isOrderable(leftKeys) =>
   Seq(joins.ShuffledHashJoinExec(
     leftKeys, rightKeys, joinType, BuildRight, condition, planLater(left), planLater(right)))

case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
     if !conf.preferSortMergeJoin && canBuildLeft(joinType) && uildLocalHashMap(left)
       && muchSmaller(left, right) ||
       !RowOrdering.isOrderable(leftKeys) =>
    Seq(joins.ShuffledHashJoinExec(
      leftKeys, rightKeys, joinType, BuildLeft, condition, planLater(left), planLater(right)))
      
private def muchSmaller(a: LogicalPlan, b: LogicalPlan): Boolean = {
  a.stats.sizeInBytes * 3 <= b.stats.sizeInBytes
}

 


如果不满足上述条件，但是如果参与join的表的key无法被排序，即无法使用Sort Merge Join，最终也会选择Shuffle Hash Join。

‍
!RowOrdering.isOrderable(leftKeys)

def isOrderable(exprs: Seq[Expression]): Boolean = exprs.forall(e => isOrderable(e.dataType))

 

3. Sort Merge Join

如果上面两种join策略（Broadcast Hash Join和Shuffle Hash Join）都不符合条件，并且参与join的key是可排序的，就会选择Sort Merge Join。

case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
   if RowOrdering.isOrderable(leftKeys) =>
   joins.SortMergeJoinExec(
     leftKeys, rightKeys, joinType, condition, planLater(left), planLater(right)) :: Nil

 

4. Without joining keys

Broadcast Hash Join、Shuffle Hash Join和Sort Merge Join都属于经典的ExtractEquiJoinKeys（等值连接条件）。

对于非ExtractEquiJoinKeys，则会优先检查表是否可以被广播（hint或者size）。如果可以，则会使用BroadcastNestedLoopJoin（简称BNLJ），熟悉Nested Loop Join则不难理解BNLJ，主要却别在于BNLJ加上了广播表。

源码如下：

// Pick BroadcastNestedLoopJoin if one side could be broadcast
case j @ logical.Join(left, right, joinType, condition)
    if canBroadcastByHints(joinType, left, right) =>
  val buildSide = broadcastSideByHints(joinType, left, right)
  joins.BroadcastNestedLoopJoinExec(
    planLater(left), planLater(right), buildSide, joinType, condition) :: Nil

case j @ logical.Join(left, right, joinType, condition)
    if canBroadcastBySizes(joinType, left, right) =>
  val buildSide = broadcastSideBySizes(joinType, left, right)
  joins.BroadcastNestedLoopJoinExec(
    planLater(left), planLater(right), buildSide, joinType, condition) :: Nil

 

如果表不能被广播，又细分为两种情况： 

  1. 若join类型InnerLike（关于InnerLike上面已有介绍）对量表直接进行笛卡尔积处理若

  2. 上述情况都不满足，最终方案是选择两个表中physical size较小的表进行广播，join策略仍为BNLJ

源码如下：

// Pick CartesianProduct for InnerJoin
case logical.Join(left, right, _: InnerLike, condition) =>
  joins.CartesianProductExec(planLater(left), planLater(right), condition) :: Nil

case logical.Join(left, right, joinType, condition) =>
  val buildSide = broadcastSide(
    left.stats.hints.broadcast, right.stats.hints.broadcast, left, right)
  // This join could be very slow or OOM
  joins.BroadcastNestedLoopJoinExec(
    planLater(left), planLater(right), buildSide, joinType, condition) :: Nil

 


很显然，无论SQL语句最终的join策略选择笛卡尔积还是BNLJ，效率都很低，这一点在实际应用中，要尽量避免。

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