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https://issues.apache.org/jira/browse/FLINK-1725?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Anis Nasir updated FLINK-1725:
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Description:
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
was:
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 skewede
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
> 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
> Labels: LoadBalancing, Partitioner
> Original Estimate: 1h
> Remaining Estimate: 1h
>
> 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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