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Date: Wed, 2 Sep 2015 16:01:45 +0000 (UTC)
From: "ASF GitHub Bot (JIRA)" <jira@apache.org>
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Subject: [jira] [Commented] (FLINK-1725) New Partitioner for better load
 balancing for skewed data
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    [ https://issues.apache.org/jira/browse/FLINK-1725?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14727545#comment-14727545 ] 

ASF GitHub Bot commented on FLINK-1725:
---------------------------------------

Github user gdfm commented on the pull request:

    https://github.com/apache/flink/pull/1069#issuecomment-137144309
  
    Sure, you can connect multiple containers.
    But while the gain you have from going from 1 to 2 is exponential, the gain from 2 to 3 and forward is just a constant factor. Nevertheless, there might be datasets with extreme skew for which having more choices is necessary. So I agree to make it configurable with a default of 2.


> 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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