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https://issues.apache.org/jira/browse/SPARK-2468?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14201832#comment-14201832
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Aaron Davidson commented on SPARK-2468:
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[~lianhuiwang] I have created
[#3155|https://github.com/apache/spark/pull/3155/files], which I will clean up
and try to get in tomorrow, which makes the preferDirectBufs config forcefully
disable direct byte buffers from both the server and client pools.
Additionally, I have added the conf "spark.shuffle.io.maxUsableCores" which
should allow you to inform the executor how many cores you're actually using,
so it will avoid allocating enough memory for all the machine's cores.
I hope that simply specifying the maxUsableCores is sufficient to actually fix
this issue for you, but the combination should give a higher chance of success.
> Netty-based block server / client module
> ----------------------------------------
>
> Key: SPARK-2468
> URL: https://issues.apache.org/jira/browse/SPARK-2468
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle, Spark Core
> Reporter: Reynold Xin
> Assignee: Reynold Xin
> Priority: Critical
> Fix For: 1.2.0
>
>
> Right now shuffle send goes through the block manager. This is inefficient
> because it requires loading a block from disk into a kernel buffer, then into
> a user space buffer, and then back to a kernel send buffer before it reaches
> the NIC. It does multiple copies of the data and context switching between
> kernel/user. It also creates unnecessary buffer in the JVM that increases GC
> Instead, we should use FileChannel.transferTo, which handles this in the
> kernel space with zero-copy. See
> http://www.ibm.com/developerworks/library/j-zerocopy/
> One potential solution is to use Netty. Spark already has a Netty based
> network module implemented (org.apache.spark.network.netty). However, it
> lacks some functionality and is turned off by default.
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