Do you mean in-memory processing? It works fine if all partitions are small.
But when some partition don’t fit in memory, it will cause OOM.
发件人: Reynold Xin <r...@databricks.com>
日期: 2018年2月1日 星期四 下午3:14
收件人: Ruifeng Zheng <ruife...@foxmail.com>
抄送: <dev@spark.apache.org>
主题: Re: [Core][Suggestion] sortWithinPartitions and aggregateWithinPartitions
for RDD
You can just do that with mapPartitions pretty easily can’t you?
On Wed, Jan 31, 2018 at 11:08 PM Ruifeng Zheng <ruife...@foxmail.com> wrote:
HI all:
1, Dataset API supports operation “sortWithinPartitions”, but in RDD API
there is no counterpart (I know there is “repartitionAndSortWithinPartitions”,
but I don’t want to repartition the RDD), I have to convert RDD to Dataset for
this function. Would it make sense to add a “sortWithinPartitions” for RDD?
2, In “aggregateByKey”/”reduceByKey”, I want to do some special
operation (like aggregator compression) after local aggregation on each
partitions. A similar case may be: compute ‘ApproximatePercentile’ for
different keys by ”reduceByKey”, it may be helpful if
‘QuantileSummaries#compress’ is called before network communication. So I
wonder if it is useful to add a ‘aggregateWithinPartitions’ for RDD?
Regards,
Ruifeng
