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Posted to issues@flink.apache.org by "Till Rohrmann (JIRA)" <ji...@apache.org> on 2018/10/06 22:59:00 UTC
[jira] [Resolved] (FLINK-8532) RebalancePartitioner should use
Random value for its first partition
[ https://issues.apache.org/jira/browse/FLINK-8532?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Till Rohrmann resolved FLINK-8532.
----------------------------------
Resolution: Fixed
Fix Version/s: 1.7.0
Fixed via 347482c752e3c693f8a7066d33ee485719e383e0
> RebalancePartitioner should use Random value for its first partition
> --------------------------------------------------------------------
>
> Key: FLINK-8532
> URL: https://issues.apache.org/jira/browse/FLINK-8532
> Project: Flink
> Issue Type: Improvement
> Components: DataStream API
> Reporter: Yuta Morisawa
> Assignee: Guibo Pan
> Priority: Major
> Labels: pull-request-available
> Fix For: 1.7.0
>
>
> In some conditions, RebalancePartitioner doesn't balance data correctly because it use the same value for selecting next operators.
> RebalancePartitioner initializes its partition id using the same value in every threads, so it indeed balances data, but at one moment the amount of data in each operator is skew.
> Particularly, when the data rate of former operators is equal , data skew becomes severe.
>
>
> Example:
> Consider a simple operator chain.
> -> map1 -> rebalance -> map2 ->
> Each map operator(map1, map2) contains three subtasks(subtask 1, 2, 3, 4, 5, 6).
> map1 map2
> st1 st4
> st2 st5
> st3 st6
>
> At the beginning, every subtasks in map1 sends data to st4 in map2 because they use the same initial parition id.
> Next time the map1 receive data st1,2,3 send data to st5 because they increment its partition id when they processed former data.
> In my environment, it takes twice the time to process data when I use RebalancePartitioner as long as I use other partitioners(rescale, keyby).
>
> To solve this problem, in my opinion, RebalancePartitioner should use its own operator id for the initial value.
>
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