venkata91 commented on a change in pull request #33896:
URL: https://github.com/apache/spark/pull/33896#discussion_r741551702



##########
File path: core/src/main/scala/org/apache/spark/Dependency.scala
##########
@@ -131,7 +135,7 @@ class ShuffleDependency[K: ClassTag, V: ClassTag, C: 
ClassTag](
   def shuffleMergeId: Int = _shuffleMergeId
 
   def setMergerLocs(mergerLocs: Seq[BlockManagerId]): Unit = {
-    if (mergerLocs != null) {
+    if (mergerLocs != null && mergerLocs.nonEmpty) {

Review comment:
       Most likely difference in the code from the internal and the OSS 
version. Yes you're right, I don't think we need to check again here for 
`nonEmpty`

##########
File path: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
##########
@@ -1716,7 +1739,31 @@ private[spark] class DAGScheduler(
             if (runningStages.contains(shuffleStage) && 
shuffleStage.pendingPartitions.isEmpty) {
               if (!shuffleStage.shuffleDep.shuffleMergeFinalized &&
                 shuffleStage.shuffleDep.getMergerLocs.nonEmpty) {
-                scheduleShuffleMergeFinalize(shuffleStage)
+                // Check if a finalize task has already been scheduled. This 
is to prevent the
+                // following scenario: Stage A attempt 0 fails and gets 
retried. Stage A attempt 1
+                // succeeded, triggering the scheduling of shuffle merge 
finalization. However,
+                // tasks from Stage A attempt 0 might still be running and 
sending task completion
+                // events to DAGScheduler. This check prevents multiple 
attempts to schedule merge
+                // finalization get triggered due to this.
+                if (shuffleStage.shuffleDep.getFinalizeTask.isEmpty) {
+                  // If total shuffle size is smaller than the threshold, 
attempt to immediately
+                  // schedule shuffle merge finalization and process map stage 
completion.
+                  val totalSize = Try(mapOutputTracker
+                    
.getStatistics(shuffleStage.shuffleDep).bytesByPartitionId.sum).getOrElse(0L)

Review comment:
       This would fail when some of the tasks outputs are lost due to 
node/executor loss which basically causes `MapOutputTracker.getStatistics` 
throw exception. Thoughts?

##########
File path: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
##########
@@ -1716,7 +1739,31 @@ private[spark] class DAGScheduler(
             if (runningStages.contains(shuffleStage) && 
shuffleStage.pendingPartitions.isEmpty) {
               if (!shuffleStage.shuffleDep.shuffleMergeFinalized &&
                 shuffleStage.shuffleDep.getMergerLocs.nonEmpty) {
-                scheduleShuffleMergeFinalize(shuffleStage)
+                // Check if a finalize task has already been scheduled. This 
is to prevent the
+                // following scenario: Stage A attempt 0 fails and gets 
retried. Stage A attempt 1
+                // succeeded, triggering the scheduling of shuffle merge 
finalization. However,
+                // tasks from Stage A attempt 0 might still be running and 
sending task completion
+                // events to DAGScheduler. This check prevents multiple 
attempts to schedule merge
+                // finalization get triggered due to this.
+                if (shuffleStage.shuffleDep.getFinalizeTask.isEmpty) {
+                  // If total shuffle size is smaller than the threshold, 
attempt to immediately
+                  // schedule shuffle merge finalization and process map stage 
completion.
+                  val totalSize = Try(mapOutputTracker
+                    
.getStatistics(shuffleStage.shuffleDep).bytesByPartitionId.sum).getOrElse(0L)

Review comment:
       Other usages of `getStatistics` are with in `mapStage.isAvailable` check 
so its guaranteed to not throw exception. Unfortunately, that is not the case 
here.
   
   Do you mean we can avoid finalizing the shuffle for non-deterministic stage 
in this case?

##########
File path: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
##########
@@ -1716,7 +1739,31 @@ private[spark] class DAGScheduler(
             if (runningStages.contains(shuffleStage) && 
shuffleStage.pendingPartitions.isEmpty) {
               if (!shuffleStage.shuffleDep.shuffleMergeFinalized &&
                 shuffleStage.shuffleDep.getMergerLocs.nonEmpty) {
-                scheduleShuffleMergeFinalize(shuffleStage)
+                // Check if a finalize task has already been scheduled. This 
is to prevent the
+                // following scenario: Stage A attempt 0 fails and gets 
retried. Stage A attempt 1
+                // succeeded, triggering the scheduling of shuffle merge 
finalization. However,
+                // tasks from Stage A attempt 0 might still be running and 
sending task completion
+                // events to DAGScheduler. This check prevents multiple 
attempts to schedule merge
+                // finalization get triggered due to this.
+                if (shuffleStage.shuffleDep.getFinalizeTask.isEmpty) {
+                  // If total shuffle size is smaller than the threshold, 
attempt to immediately
+                  // schedule shuffle merge finalization and process map stage 
completion.
+                  val totalSize = Try(mapOutputTracker
+                    
.getStatistics(shuffleStage.shuffleDep).bytesByPartitionId.sum).getOrElse(0L)

Review comment:
       Other usages of `getStatistics` are with in `mapStage.isAvailable` check 
so its guaranteed to not throw exception. Unfortunately, that is not the case 
here.
   
   @mridulm Do you mean we can avoid finalizing the shuffle for 
non-deterministic stage in this case?




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