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Posted to issues@spark.apache.org by "Patrick Wendell (JIRA)" <ji...@apache.org> on 2014/12/07 07:40:13 UTC

[jira] [Comment Edited] (SPARK-4740) Netty's network throughput is about 1/2 of NIO's in spark-perf sortByKey

    [ https://issues.apache.org/jira/browse/SPARK-4740?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14237075#comment-14237075 ] 

Patrick Wendell edited comment on SPARK-4740 at 12/7/14 6:39 AM:
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Is it possible that somehow the newer code was only pushed to one of the machines, and the other machines are just using the older code? 


was (Author: pwendell):
[~terrymanu] - is it possible that somehow the newer code was only pushed to one of the machines, and the other machines are just using the older code? 

> Netty's network throughput is about 1/2 of NIO's in spark-perf sortByKey
> ------------------------------------------------------------------------
>
>                 Key: SPARK-4740
>                 URL: https://issues.apache.org/jira/browse/SPARK-4740
>             Project: Spark
>          Issue Type: Improvement
>          Components: Shuffle, Spark Core
>    Affects Versions: 1.2.0
>            Reporter: Zhang, Liye
>            Assignee: Reynold Xin
>            Priority: Blocker
>         Attachments: (rxin patch better executor)TestRunner  sort-by-key - Thread dump for executor 3_files.zip, (rxin patch normal executor)TestRunner  sort-by-key - Thread dump for executor 0 _files.zip, Spark-perf Test Report 16 Cores per Executor.pdf, Spark-perf Test Report.pdf, TestRunner  sort-by-key - Thread dump for executor 1_files (Netty-48 Cores per node).zip, TestRunner  sort-by-key - Thread dump for executor 1_files (Nio-48 cores per node).zip
>
>
> When testing current spark master (1.3.0-snapshot) with spark-perf (sort-by-key, aggregate-by-key, etc), Netty based shuffle transferService takes much longer time than NIO based shuffle transferService. The network throughput of Netty is only about half of that of NIO. 
> We tested with standalone mode, and the data set we used for test is 20 billion records, and the total size is about 400GB. Spark-perf test is Running on a 4 node cluster with 10G NIC, 48 cpu cores per node and each executor memory is 64GB. The reduce tasks number is set to 1000. 



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