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https://issues.apache.org/jira/browse/SPARK-8591?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Tathagata Das resolved SPARK-8591.
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Resolution: Won't Fix
> Block failed to unroll to memory should not be replicated for MEMORY_ONLY_2
> StorageLevel
> ----------------------------------------------------------------------------------------
>
> Key: SPARK-8591
> URL: https://issues.apache.org/jira/browse/SPARK-8591
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.4.0
> Reporter: Dibyendu Bhattacharya
>
> Block which failed to unroll to memory and returned iterator and size 0,
> should not be replicated to peer node as putBlockStatus comes as
> StorageLevel.NONE and BlockStatus is not reported to Master.
> Primary issue here is , for StorageLevel MEMORY_ONLY_2 , if BlockManager
> failed to unroll the block to memory and store to local is failed,
> BlockManager still replicate the same block to Remote peer. For Spark
> Streaming case , the Receivers get the PutResult from local BlockManager and
> if block failed to store locally , ReceivedBlockHandler throws the
> SparkException back to Receiver even though the block successfully replicated
> in Remote peer by BlockManager. This leads to wastage of memory at remote
> peer as that block can never be used in Streaming jobs. In case of Receiver
> failed to store the block, it can retry and for every failed retry ( to store
> to local) may leads to adding another unused block to remote and this may
> leads to many unwanted blocks in case of high volume receivers does multiple
> retry.
> The fix here proposed is to stop replicating the block if store to local has
> failed. This fix will prevent the scenario mentioned above and also will not
> impact the RDD Partition replications ( during Cache or Persists) as RDD
> CacheManager perform unrolling to memory first before attempting to store in
> local memory, and this can never happen that block unroll is successful but
> store to local memory has failed.
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