[jira] [Resolved] (SPARK-2818) Improve joinning RDDs that transformed from the same parent RDD

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-2818?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-2818.
------------------------------
    Resolution: Won't Fix

> Improve joinning RDDs that transformed from the same parent RDD
> ---------------------------------------------------------------
>
>                 Key: SPARK-2818
>                 URL: https://issues.apache.org/jira/browse/SPARK-2818
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>            Reporter: Lu Lu
>
> if the joinning RDDs are originating from a same cached RDD, the DAGScheduler will submit redundant stages to compute and cache the common parent.
> For example:
> {code}
>     val edges = sc.textFile(...).cache()
>     val bigSrc = edges.groupByKey().filter(...)
>     val reversed = edges.map(edge => (edge._2, edge._1))
>     val bigDst = reversed.groupByKey().filter(...)
>     bigSrc.join(bigDst).count
> {code}
> The final count action will trigger two stages both to compute the edges RDD. It will result to two performance problems:
> (1) if the resources are sufficient, these two stages will be running concurrently and read the same HDFS file at the same time.
> (2) if the two stages run one by one, the tasks of the latter stage can read the cached blocks of the edges RDD immediately. But it cannot achieve data-locality for these tasks because that the block location information are unavailable when submiting the stages.



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