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From "Robert Schmidtke (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAPREDUCE-6923) Optimize MapReduce Shuffle I/O for small partitions
Date Thu, 10 Aug 2017 06:06:00 GMT

    [ https://issues.apache.org/jira/browse/MAPREDUCE-6923?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16121109#comment-16121109
] 

Robert Schmidtke commented on MAPREDUCE-6923:
---------------------------------------------

Hi Ravi,

{quote}
When {{shuffleBufferSize <= trans}}, then behavior is exactly the same as old code.
{quote}
Yes.

{quote}
if {{readSize == trans}} (i.e. the {{fileChannel.read()}} returned as many bytes as I wanted
to transfer, {{trans}} is decremented correctly, {{position}} is increased correctly and the
{{byteBuffer}} is flipped as usual. {{byteBuffer}} 's contents are written to {{target}} as
usual, {{byteBuffer}} is cleared and then hopefully GCed never to be seen again.
{quote}
I'd say that for {{readSize == trans}}, we're in the [else block|https://github.com/apache/hadoop/blob/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-shuffle/src/main/java/org/apache/hadoop/mapred/FadvisedFileRegion.java#L127],
and thus {{byteBuffer}} is {{limit()}} ed to {{trans}} (which is the size it already has because
we're in the case where {{trans < shuffleBufferSize}}. It's correctly positioned to {{0}}
as we're done reading, and {{trans}} is correctly set to {{0}}. Afterwards, the loop breaks
(it can only be one iteration here because otherwise {{trans}} would have been larger than
{{shuffleBufferSize}}), {{byteBuffer}} is written to {{target}} and then cleared.

{quote}
if {{readSize < trans}} (almost the same thing as above happens, but in a while loop).
The only change this patch makes is that the {{byteBuffer}} may be smaller than before this
patch, but it doesn't matter because its big enough for the number of bytes we need to transfer.
{quote}
Now we have the situation you described for the previous case, and I agree with your reasoning
here.

> Optimize MapReduce Shuffle I/O for small partitions
> ---------------------------------------------------
>
>                 Key: MAPREDUCE-6923
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-6923
>             Project: Hadoop Map/Reduce
>          Issue Type: Improvement
>         Environment: Observed in Hadoop 2.7.3 and above (judging from the source code
of future versions), and Ubuntu 16.04.
>            Reporter: Robert Schmidtke
>            Assignee: Robert Schmidtke
>             Fix For: 2.9.0, 3.0.0-beta1
>
>         Attachments: MAPREDUCE-6923.00.patch, MAPREDUCE-6923.01.patch
>
>
> When a job configuration results in small partitions read by each reducer from each mapper
(e.g. 65 kilobytes as in my setup: a [TeraSort|https://github.com/apache/hadoop/blob/branch-2.7.3/hadoop-mapreduce-project/hadoop-mapreduce-examples/src/main/java/org/apache/hadoop/examples/terasort/TeraSort.java]
of 256 gigabytes using 2048 mappers and reducers each), and setting
> {code:xml}
> <property>
>   <name>mapreduce.shuffle.transferTo.allowed</name>
>   <value>false</value>
> </property>
> {code}
> then the default setting of
> {code:xml}
> <property>
>   <name>mapreduce.shuffle.transfer.buffer.size</name>
>   <value>131072</value>
> </property>
> {code}
> results in almost 100% overhead in reads during shuffle in YARN, because for each 65K
needed, 128K are read.
> I propose a fix in [FadvisedFileRegion.java|https://github.com/apache/hadoop/blob/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-shuffle/src/main/java/org/apache/hadoop/mapred/FadvisedFileRegion.java#L114]
as follows:
> {code:java}
> ByteBuffer byteBuffer = ByteBuffer.allocate(Math.min(this.shuffleBufferSize, trans >
Integer.MAX_VALUE ? Integer.MAX_VALUE : (int) trans));
> {code}
> e.g. [here|https://github.com/apache/hadoop/compare/branch-2.7.3...robert-schmidtke:adaptive-shuffle-buffer].
This sets the shuffle buffer size to the minimum value of the shuffle buffer size specified
in the configuration (128K by default), and the actual partition size (65K on average in my
setup). In my benchmarks this reduced the read overhead in YARN from about 100% (255 additional
gigabytes as described above) down to about 18% (an additional 45 gigabytes). The runtime
of the job remained the same in my setup.



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