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https://issues.apache.org/jira/browse/SPARK-5997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15259101#comment-15259101
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Robert Ormandi edited comment on SPARK-5997 at 4/26/16 10:58 PM:
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Does it simply solve the problem if the method splits up each partition to N
new ones uniformly (N should be sufficiently small, like 2 or 3). In this way,
we will have N x originalNumberOfPartitions partitions each containing
originalNumberOfObjectsPerPartition / N objects approximately? N could be a
parameter of the method.
was (Author: rormandi):
Does it simply solve the problem if the method split up each partition to N new
ones uniformly. In this way, we will have N x originalNumberOfPartitions
partitions each containing originalNumberOfObjectsPerPartition / N objects
approximately? N could be a parameter of the method.
> 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
>
> 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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