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From Koert Kuipers <ko...@tresata.com>
Subject Re: Why repartitionAndSortWithinPartitions slower than MapReducer
Date Mon, 20 Aug 2018 15:29:40 GMT
I assume you are using RDDs? What are you doing after the repartitioning +
sorting, if anything?


On Aug 20, 2018 11:22, "周浥尘" <zhouycsf@gmail.com> wrote:

In addition to my previous email,
Environment: spark 2.1.2, hadoop 2.6.0-cdh5.11, Java 1.8, CentOS 6.6

周浥尘 <zhouycsf@gmail.com> 于2018年8月20日周一 下午8:52写道:

> Hi team,
>
> I found the Spark method *repartitionAndSortWithinPartitions *spends
> twice as much time as using Mapreduce in some cases.
> I want to repartition the dataset accorading to split keys and save them
> to files in ascending. As the doc says, repartitionAndSortWithinPartitions
> “is more efficient than calling `repartition` and then sorting within each
> partition because it can push the sorting down into the shuffle machinery.”
> I thought it may be faster than MR, but actually, it is much more slower. I
> also adjust several configurations of spark, but that doesn't work.(Both
> Spark and Mapreduce run on a three-node cluster and share the same number
> of partitions.)
> Can this situation be explained or is there any approach to improve the
> performance of spark?
>
> Thanks & Regards,
> Yichen
>

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