[jira] [Commented] (SPARK-5997) Increase partition count without performing a shuffle

["nirav patel (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-5997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16792964#comment-16792964 ] 

nirav patel commented on SPARK-5997:
------------------------------------

Adding another possible use case for this ask - I am hitting IllegalArgumentException: Size exceeds Integer.MAX_VALUE error when trying to write unpartitioned Dataframe to parquet. Error is due to shuffleblock exceed 2GB in size. Solution is to repartition the Dataframe (Dataset) . I can do it but I don't want to cause shuffle when I increase number of partitions with repartition API.

> Increase partition count without performing a shuffle
> -----------------------------------------------------
>
>                 Key: SPARK-5997
>                 URL: https://issues.apache.org/jira/browse/SPARK-5997
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>            Reporter: Andrew Ash
>            Priority: Major
>
> When decreasing partition count with rdd.repartition() or rdd.coalesce(), the user has the ability to choose whether or not to perform a shuffle.  However when increasing partition count there is no option of whether to perform a shuffle or not -- a shuffle always occurs.
> This Jira is to create a {{rdd.repartition(largeNum, shuffle=false)}} call that performs a repartition to a higher partition count without a shuffle.
> The motivating use case is to decrease the size of an individual partition enough that the .toLocalIterator has significantly reduced memory pressure on the driver, as it loads a partition at a time into the driver.



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