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Perinkulam I Ganesh commented on SPARK-5997: -------------------------------------------- Hi .. New to Spark .. maybe speaking out of ignorance .. won't the following function accomplish the above ? The approach is simple.. If the number of partition requested is higher ...collapse the current number of partitions into a single partition with no shuffle. Now, with only one partition,repartition with shuffle or no-shuffle will behave identically. So use this single partition with shuffle and split the partition into higher partition. def repartitionv2(numPartitions: Int, shuffle: Boolean)(implicit ord: Ordering[T] = null) : RDD[T] = withScope { if (shuffle) { coalesce(numPartitions, shuffle) } else { var cnt = getPartitions.size; if (numPartitions > cnt) { var temp = coalesce(1, false) temp.coalesce(numPartitions, true) } else { coalesce(numPartitions, shuffle) } } } It seems to work ... thanks - P. I. > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org