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 <zhouy...@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 >
