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From 周浥尘 <>
Subject Re: Why repartitionAndSortWithinPartitions slower than MapReducer
Date Mon, 20 Aug 2018 15:21:56 GMT
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 <> 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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