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https://issues.apache.org/jira/browse/SPARK-2978?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14105128#comment-14105128
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Sandy Ryza commented on SPARK-2978:
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[~jerryshao], if I understand correctly, ShuffleRDD already supports what's
needed here, and satisfying that need is independent of whether we sort on the
map side. That said, I think the changes you proposed on SPARK-2926 could
definitely make this more performant, and we would likely see the same
improvements you benchmarked for sortByKey.
> Provide an MR-style shuffle transformation
> ------------------------------------------
>
> Key: SPARK-2978
> URL: https://issues.apache.org/jira/browse/SPARK-2978
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
> Reporter: Sandy Ryza
>
> For Hive on Spark joins in particular, and for running legacy MR code in
> general, I think it would be useful to provide a transformation with the
> semantics of the Hadoop MR shuffle, i.e. one that
> * groups by key: provides (Key, Iterator[Value])
> * within each partition, provides keys in sorted order
> A couple ways that could make sense to expose this:
> * Add a new operator. "groupAndSortByKey",
> "groupByKeyAndSortWithinPartition", "hadoopStyleShuffle", maybe?
> * Allow groupByKey to take an ordering param for keys within a partition
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