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From "Aaron Davidson (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-4740) Netty's network throughput is about 1/2 of NIO's in spark-perf sortByKey
Date Sun, 07 Dec 2014 02:27:12 GMT

    [ https://issues.apache.org/jira/browse/SPARK-4740?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14237026#comment-14237026
] 

Aaron Davidson commented on SPARK-4740:
---------------------------------------

Could we get logs from the good/bad executors? I'm curious to which nodes each one has connected.
(Ideally the logs would be at the DEBUG level, otherwise it probably would not have enough
information.) We can also work on trying to repro on our own setup, though this looks like
we're pretty close.

> 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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