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https://issues.apache.org/jira/browse/FLINK-1725?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14720272#comment-14720272
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ASF GitHub Bot commented on FLINK-1725:
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Github user tillrohrmann commented on the pull request:
https://github.com/apache/flink/pull/1069#issuecomment-135838438
@anisnasir thanks for your contribution. Out of curiosity I was wondering
why the `PartialPartitioner` distributes the data exactly between two channels.
Wouldn't it also be conceivable to distribute it between an arbitrary number?
Then one could adjust the `PartialPartitioner` depending on the actual data
skew. I assume that there are situations where your data is still skewed even
after distributing it onto two different consumers.
It would be great if you could add another test which tests the functioning
of the partitioner in a more applied scenario, if possible. Maybe one could
check that the skew of the input data decreases after the partitioning.
> 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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