本篇内容介绍了“spark的动态分区裁剪下物理计划怎么实现”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
背景
本文基于delta 0.7.0 spark 3.0.1 spark 3.x引入了动态分区裁剪，在 spark 的动态分区裁剪上(Dynamic partition pruning)-逻辑计划我们提到在逻辑计划阶段会加入DynamicPruningSubquery，今天我们分析一下在物理阶段怎么对DynamicPruningSubquery进行优化以及实现的
分析
直接转到PlanDynamicPruningFilters的apply方法：
override def apply(plan: SparkPlan): SparkPlan = {
if (!sqlConf.get.dynamicPartitionPruningEnabled) {
return plan
}
plan transformAllExpressions {
case DynamicPruningSubquery(
value, buildplan, buildKeys, broadcastKeyIndex, onlyInbroadcast, exprId) =>
val sparkPlan = QueryExecution.createSparkPlan(
sparkSession, sparkSession.sessionState.planner, buildplan)
// Using `sparkPlan` is a little hacky as it is based on the assumption that this rule is
// the first to be applied (apart from `InsertAdaptiveSparkPlan`).
val canReuseExchange = sqlConf.get.exchangeReuseEnabled && buildKeys.nonEmpty &&
plan.find {
case broadcastHashJoinExec(_, _, _, BuildLeft, _, left, _) =>
left.sameResult(sparkPlan)
case broadcastHashJoinExec(_, _, _, buildright, _, _, right) =>
right.sameResult(sparkPlan)
case _ => false
}.isDefined
if (canReuseExchange) {
val mode = broadcastMode(buildKeys, buildplan)
val executedplan = QueryExecution.prepareExecutedplan(sparkSession, sparkPlan)
// plan a broadcast exchange of the build side of the join
val exchange = broadcastExchangeExec(mode, executedplan)
val name = s"dynamicpruning#${exprId.id}"
// place the broadcast adaptor for reusing the broadcast results on the probe side
val broadcastValues =
SubquerybroadcastExec(name, broadcastKeyIndex, buildKeys, exchange)
DynamicPruningExpression(InSubqueryExec(value, broadcastValues, exprId))
} else if (onlyInbroadcast) {
// it is not worthwhile to execute the query, so we fall-back to a true literal
DynamicPruningExpression(Literal.TrueLiteral)
} else {
// we need to apply an aggregate on the buildplan in order to be column pruned
val alias = Alias(buildKeys(broadcastKeyIndex), buildKeys(broadcastKeyIndex).toString)()
val aggregate = Aggregate(Seq(alias), Seq(alias), buildplan)
DynamicPruningExpression(expressions.InSubquery(
Seq(value), ListQuery(aggregate, childOutputs = aggregate.output)))
}
}
}
如果没有开启动态分区裁剪，则直接跳过
QueryExecution.createSparkPlan( sparkSession, sparkSession.sessionState.planner, buildplan)通过逻辑计划构造物理计划判断是否reuseExchange,如果spark.sql.exchange.reuse配置为true，且存在join的是broadcastHashjoin，而且计算结果和要进行过滤的物理计划的结果一样，则进行下一步,
进行物理计划执行前的准备, 得到executedplan
构建broadcastExchangeExec，broadcastValues，InSubqueryExec,DynamicPruningExpression，broadcastExchangeExec内部就是进行spark的broadcast操作 注意：这里的broadcastExchangeExec会在ReuseExchange规则中被优化, 最终会被broadcastQueryStageExec调用,从而公用同一个broacast的值
@H_502_37@
如果以上不满足，默认DynamicPruningExpression(Literal.TrueLiteral),也就是不会进行裁剪
如果不是broadcastHashjoin，但是能够加速，则按照需要过滤的key做一次聚合，之后再组成DynamicPruningExpression
至此动态裁剪的物理计划优化就分析完了
“spark的动态分区裁剪下物理计划怎么实现”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注编程之家网站，小编将为大家输出更多高质量的实用文章！
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