[jira] [Resolved] (SPARK-6808) Checkpointing after zipPartitions results in NODE_LOCAL execution

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-6808?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-6808.
---------------------------------
    Resolution: Incomplete

> Checkpointing after zipPartitions results in NODE_LOCAL execution
> -----------------------------------------------------------------
>
>                 Key: SPARK-6808
>                 URL: https://issues.apache.org/jira/browse/SPARK-6808
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 1.2.1, 1.3.0
>         Environment: EC2 Ubuntu r3.8xlarge machines
>            Reporter: Xinghao Pan
>            Priority: Minor
>              Labels: bulk-closed
>
> I'm encountering a weird issue where a simple iterative zipPartition is PROCESS_LOCAL before checkpointing, but turns NODE_LOCAL for all iterations after checkpointing. More often than not, tasks are fetching remote blocks from the network, leading to a 10x increase in runtime.
> Here's an example snippet of code:
>     var R : RDD[(Long,Int)]
>     = sc.parallelize((0 until numPartitions), numPartitions)
>       .mapPartitions(_ => new Array[(Long,Int)](10000000).map(i => (0L,0)).toSeq.iterator).cache()
>     sc.setCheckpointDir(checkpointDir)
>     var iteration = 0
>     while (iteration < 50){
>       R = R.zipPartitions(R)((x,y) => x).cache()
>       if ((iteration+1) % checkpointIter == 0) R.checkpoint()
>       R.foreachPartition(_ => {})
>       iteration += 1
>     }
> I've also tried to unpersist the old RDDs, and increased spark.locality.wait but nether helps.
> Strangely, by adding a simple identity map
> R = R.map(x => x).cache()
> after the zipPartitions appears to partially mitigate the issue.
> The problem was originally triggered when I attempted to checkpoint after doing joinVertices in GraphX, but the above example shows that the issue is in Spark core too.



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