Github user dibbhatt commented on a diff in the pull request:
https://github.com/apache/spark/pull/6990#discussion_r33749161
--- Diff: core/src/main/scala/org/apache/spark/storage/BlockManager.scala
---
@@ -833,8 +833,10 @@ private[spark] class BlockManager(
logDebug("Put block %s locally took %s".format(blockId,
Utils.getUsedTimeMs(startTimeMs)))
// Either we're storing bytes and we asynchronously started
replication, or we're storing
- // values and need to serialize and replicate them now:
- if (putLevel.replication > 1) {
+ // values and need to serialize and replicate them now.
+ // Should not replicate the block if its StorageLevel is
StorageLevel.NONE or
+ // putting it to local is failed.
+ if (!putBlockInfo.isFailed && putLevel.replication > 1) {
--- End diff --
The problem here is , if local memory got filled up and block store failed,
blocks still get replicated to remote and used up memory but same blocks never
used in Streaming jobs... Even though those blocks will eventually evicted ,
but this fix will optimize the memory. I understand your concern about RDD
partition which can still use the remote replica for speedup even local store
failed.
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