zhongyu09 commented on a change in pull request #31167:
URL: https://github.com/apache/spark/pull/31167#discussion_r557002156
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveSparkPlanExec.scala
##########
@@ -190,7 +191,36 @@ case class AdaptiveSparkPlanExec(
executionId.foreach(onUpdatePlan(_, result.newStages.map(_.plan)))
// Start materialization of all new stages and fail fast if any
stages failed eagerly
- result.newStages.foreach { stage =>
+
+ // SPARK-33933: we should materialize broadcast stages first and
wait the
+ // materialization finish before materialize other stages, to avoid
waiting
+ // for broadcast tasks to be scheduled and leading to broadcast
timeout.
+ val broadcastMaterializationFutures = result.newStages
+ .filter(_.isInstanceOf[BroadcastQueryStageExec])
+ .map { stage =>
+ var future: Future[Any] = null
+ try {
+ future = stage.materialize()
+ future.onComplete { res =>
+ if (res.isSuccess) {
+ events.offer(StageSuccess(stage, res.get))
+ } else {
+ events.offer(StageFailure(stage, res.failed.get))
+ }
+ }(AdaptiveSparkPlanExec.executionContext)
+ } catch {
+ case e: Throwable =>
+ cleanUpAndThrowException(Seq(e), Some(stage.id))
+ }
+ future
+ }
+
+ // Wait for the materialization of all broadcast stages finish
+ broadcastMaterializationFutures.foreach(ThreadUtils.awaitReady(_,
Duration.Inf))
Review comment:
In deed, there will be a little waste of resources. This is the same
behavior as non-AQE. Given the lightweight of broadcast, it should not cause
too much time, few seconds in normal. I think that's acceptable.
If not wait, there's still probability that situations in #30998 will occur
and cause broadcast timeout.
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