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https://issues.apache.org/jira/browse/SPARK-2468?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14199911#comment-14199911
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Aaron Davidson edited comment on SPARK-2468 at 11/6/14 6:57 AM:
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This could be due to the netty transfer service allocating more off-heap byte
buffers, which perhaps is accounted for differently by YARN. [PR
#3101|https://github.com/apache/spark/pull/3101/files#diff-d2ce9b38bdc38ca9d7119f9c2cf79907R33],
which should go in tomorrow, will include a way to avoid allocating off-heap
buffers (by setting the spark config
"spark.shuffle.io.preferDirectBufs=false"), which should either solve your
problem or at least produce the more typical OutOfMemoryError.
was (Author: ilikerps):
This could be due to the netty transfer service allocating more off-heap byte
buffers, which perhaps is accounted for differently by YARN. [PR
#3101|https://github.com/apache/spark/pull/3101/files#diff-d2ce9b38bdc38ca9d7119f9c2cf79907R33],
which should go in tomorrow, will include a way to avoid allocating off-heap
buffers, which should either solve your problem or at least produce the more
typical OutOfMemoryError.
> 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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