[GitHub] [spark] agrawaldevesh commented on a change in pull request #28818: [WIP][SPARK-31198][CORE] Use graceful decommissioning as part of dynamic scaling

[GitBox <gi...@apache.org>]

agrawaldevesh commented on a change in pull request #28818:
URL: https://github.com/apache/spark/pull/28818#discussion_r439775571



##########
File path: core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
##########
@@ -497,6 +453,70 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
 
   protected def minRegisteredRatio: Double = _minRegisteredRatio
 
+  /**
+   * Request that the cluster manager decommission the specified executors.
+   * Default implementation delegates to kill, scheduler must override
+   * if it supports graceful decommissioning.

Review comment:
       Is this comment valid ? I don't see this method being overridden.

##########
File path: core/src/main/scala/org/apache/spark/scheduler/dynalloc/ExecutorMonitor.scala
##########
@@ -333,11 +335,19 @@ private[spark] class ExecutorMonitor(
   }
 
   override def onBlockUpdated(event: SparkListenerBlockUpdated): Unit = {
-    if (!event.blockUpdatedInfo.blockId.isInstanceOf[RDDBlockId]) {
-      return
-    }
     val exec = ensureExecutorIsTracked(event.blockUpdatedInfo.blockManagerId.executorId,
       UNKNOWN_RESOURCE_PROFILE_ID)
+
+    // Check if it is a shuffle file, or RDD to pick the correct codepath for update
+    if (event.blockUpdatedInfo.blockId.isInstanceOf[ShuffleDataBlockId] && shuffleTrackingEnabled) {
+      event.blockUpdatedInfo.blockId match {
+        case ShuffleDataBlockId(shuffleId, _, _) => exec.addShuffle(shuffleId)
+        case _ => // For now we only update on data blocks
+      }

Review comment:
       Is this change related to the purpose of 'Using decommissioning for dynamic allocation' ? I didn't fully understand why this is done wrt to just the decommissioning logic. Perhaps this is about https://issues.apache.org/jira/browse/SPARK-31974 ? 




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