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https://issues.apache.org/jira/browse/SPARK-14290?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15234666#comment-15234666
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Apache Spark commented on SPARK-14290:
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User 'liyezhang556520' has created a pull request for this issue:
https://github.com/apache/spark/pull/12296
> Fully utilize the network bandwidth for Netty RPC by avoid significant
> underlying memory copy
> ---------------------------------------------------------------------------------------------
>
> Key: SPARK-14290
> URL: https://issues.apache.org/jira/browse/SPARK-14290
> Project: Spark
> Issue Type: Improvement
> Components: Input/Output, Spark Core
> Affects Versions: 1.2.0, 1.3.0, 1.4.0, 1.5.0, 1.6.0, 2.0.0
> Reporter: Zhang, Liye
> Assignee: Zhang, Liye
> Fix For: 2.0.0
>
>
> When netty transfer data that is not from *FileRegion*, data will be
> transfered as *ByteBuf*, If the data is large, there will occur significant
> performance issue because there is memory copy underlying in
> *sun.nio.ch.IOUtil.write*, the CPU is 100% used, and network is very low. We
> can check it by comparing *NIO* and *Netty* for
> *spark.shuffle.blockTransferService* in spark 1.4. NIO network bandwidth is
> much better than Netty.
> How to reproduce:
> {code}
> sc.parallelize(Array(1,2,3),3).mapPartitions(a=>Array(new Array[Double](1024
> * 1024 * 50)).iterator).reduce((a,b)=> a).length
> {code}
> The root cause can referred
> [here|http://stackoverflow.com/questions/34493320/how-does-buffer-size-affect-nio-channel-performance].
>
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