[ 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org