Mailing-List: contact mapreduce-issues-help@hadoop.apache.org; run by ezmlm
Precedence: bulk
Date: Wed, 9 Aug 2017 22:43:00 +0000 (UTC)
From: "Ravi Prakash (JIRA)" <jira@apache.org>
To: mapreduce-issues@hadoop.apache.org
Message-ID: <JIRA.13091372.1501536505000.133258.1502318580381@Atlassian.JIRA>
In-Reply-To: <JIRA.13091372.1501536505000@Atlassian.JIRA>
References: <JIRA.13091372.1501536505000@Atlassian.JIRA> <JIRA.13091372.1501536505821@jira-lw-us.apache.org>
Subject: [jira] [Updated] (MAPREDUCE-6923) Optimize MapReduce Shuffle I/O
 for small partitions
MIME-Version: 1.0
Content-Type: text/plain; charset=utf-8
Content-Transfer-Encoding: 7bit
archived-at: Wed, 09 Aug 2017 22:43:06 -0000


     [ https://issues.apache.org/jira/browse/MAPREDUCE-6923?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ravi Prakash updated MAPREDUCE-6923:
------------------------------------
       Resolution: Fixed
    Fix Version/s: 3.0.0-beta1
                   2.9.0
           Status: Resolved  (was: Patch Available)

Committed to branch-2 and trunk. Thanks a lot for your contribution Robert!

Good luck with your research. I hope to hear back from you when you publish. And look forward to more valuable contributions from you. 

> 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.


--
This message was sent by Atlassian JIRA
(v6.4.14#64029)

---------------------------------------------------------------------
To unsubscribe, e-mail: mapreduce-issues-unsubscribe@hadoop.apache.org
For additional commands, e-mail: mapreduce-issues-help@hadoop.apache.org