[ https://issues.apache.org/jira/browse/FLINK-1725?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Robert Metzger updated FLINK-1725: ---------------------------------- Assignee: Anis Nasir > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332)