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https://issues.apache.org/jira/browse/FLINK-8532?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16592910#comment-16592910
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ASF GitHub Bot commented on FLINK-8532:
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StephanEwen commented on issue #6544: [FLINK-8532] [Streaming] modify
RebalancePartitioner to use a random partition as its first partition
URL: https://github.com/apache/flink/pull/6544#issuecomment-416043267
Thanks for taking a deeper look. Unfortunately, divisions (modulo) are even
more expensive, so would be good to avoid them.
I think the solution can be actually a bit simpler. It would probably be
sufficient to simply initialize the array to `INT_MAX - 1` replace the
`this.returnArray[0] = 0;` in the original code with `this.returnArray[0] =
resetValue()`. Inside the `resetValue()` you can do the initialization.
That way, common cases have no additional check, and the overflow/reset case
gets one additional branch, which is already a good improvement.
We could possibly do a followup optimization, where outputs that have only
one channel swap in a special selector that always returns just `0`. The
one-channel-only case is probably the one that would be affected most by this
change, because it always overflows each time.
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> 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
>
> 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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