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