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https://issues.apache.org/jira/browse/SPARK-3441?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14126454#comment-14126454
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Sandy Ryza commented on SPARK-3441:
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Right. It's not much work, but there are some questions (posted on SPARK-2978)
about exactly what the semantics of such a wrapper should be. The concern was
that we would want to make groupByKey consistent with it when it supports
disk-backed keys, and didn't feel comfortable locking that behavior down right
now. Happy to add a wrapper if we can come to a decision there.
> Explain in docs that repartitionAndSortWithinPartitions enacts Hadoop style
> shuffle
> -----------------------------------------------------------------------------------
>
> Key: SPARK-3441
> URL: https://issues.apache.org/jira/browse/SPARK-3441
> Project: Spark
> Issue Type: Improvement
> Components: Documentation, Spark Core
> Reporter: Patrick Wendell
> Assignee: Sandy Ryza
>
> I think it would be good to say something like this in the doc for
> repartitionAndSortWithinPartitions and add also maybe in the doc for groupBy:
> {code}
> This can be used to enact a "Hadoop Style" shuffle along with a call to
> mapPartitions, e.g.:
> rdd.repartitionAndSortWithinPartitions(part).mapPartitions(...)
> {code}
> It might also be nice to add a version that doesn't take a partitioner and/or
> to mention this in the groupBy javadoc. I guess it depends a bit whether we
> consider this to be an API we want people to use more widely or whether we
> just consider it a narrow stable API mostly for Hive-on-Spark. If we want
> people to consider this API when porting workloads from Hadoop, then it might
> be worth documenting better.
> What do you think [~rxin] and [~matei]?
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