jerrypeng commented on code in PR #59523:
URL: https://github.com/apache/spark/pull/59523#discussion_r4408315688
##########
core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala:
##########
@@ -1516,6 +1515,29 @@ private[spark] class DAGScheduler(
checkPipelinedStageCreation(finalRDD, partitions, jobId)
}
+ /** Reject persistence that could bypass a native pipelined shuffle reader.
*/
+ private def checkPipelinedCacheSupported(finalRDD: RDD[_]): Unit = {
+ val persisted = new HashSet[RDD[_]]
+ traverseRDDGraph(finalRDD) { (rdd, enqueue) =>
+ if (rdd.getStorageLevel != StorageLevel.NONE ||
+
rdd.checkpointData.exists(_.isInstanceOf[LocalRDDCheckpointData[_]])) {
+ persisted += rdd
+ }
+ rdd.dependencies.foreach(dep => enqueue(dep.rdd))
Review Comment:
Agreed. The proposed change stops cache collection at regular shuffle
boundaries while continuing through pipelined and narrow dependencies. The
preceding shape validation already excludes pipelined reads below those
boundaries, including shared-lineage cases. I kept the check separate so it
still runs before admission and with slot checking disabled. The
materialized-prefix regression verifies that cache collection adds no traversal
of the prefix.
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