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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1725) New Partitioner for better load balancing for skewed data
Date Wed, 02 Sep 2015 16:43:46 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1725?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14727601#comment-14727601
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ASF GitHub Bot commented on FLINK-1725:
---------------------------------------

Github user tillrohrmann commented on the pull request:

    https://github.com/apache/flink/pull/1069#issuecomment-137164902
  
    Thanks :-)
    
    On Wed, Sep 2, 2015 at 6:39 PM, Gianmarco De Francisci Morales <
    notifications@github.com> wrote:
    
    > @tillrohrmann <https://github.com/tillrohrmann> here a paper that
    > describes the effect for a very similar setting:
    > http://www.eecs.harvard.edu/~michaelm/postscripts/tpds2001.pdf
    > The same arguments apply in this case.
    >
    > —
    > Reply to this email directly or view it on GitHub
    > <https://github.com/apache/flink/pull/1069#issuecomment-137163339>.
    >



> New Partitioner for better load balancing for skewed data
> ---------------------------------------------------------
>
>                 Key: FLINK-1725
>                 URL: https://issues.apache.org/jira/browse/FLINK-1725
>             Project: Flink
>          Issue Type: Improvement
>          Components: New Components
>    Affects Versions: 0.8.1
>            Reporter: Anis Nasir
>            Assignee: Anis Nasir
>              Labels: LoadBalancing, Partitioner
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> Hi,
> We have recently studied the problem of load balancing in Storm [1].
> In particular, we focused on key distribution of the stream for skewed data.
> We developed a new stream partitioning scheme (which we call Partial Key Grouping). It
achieves better load balancing than key grouping while being more scalable than shuffle grouping
in terms of memory.
> In the paper we show a number of mining algorithms that are easy to implement with partial
key grouping, and whose performance can benefit from it. We think that it might also be useful
for a larger class of algorithms.
> Partial key grouping is very easy to implement: it requires just a few lines of code
in Java when implemented as a custom grouping in Storm [2].
> For all these reasons, we believe it will be a nice addition to the standard Partitioners
available in Flink. If the community thinks it's a good idea, we will be happy to offer support
in the porting.
> References:
> [1]. https://melmeric.files.wordpress.com/2014/11/the-power-of-both-choices-practical-load-balancing-for-distributed-stream-processing-engines.pdf
> [2]. https://github.com/gdfm/partial-key-grouping



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