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https://issues.apache.org/jira/browse/FLINK-8532?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16590561#comment-16590561
]
ASF GitHub Bot commented on FLINK-8532:
---------------------------------------
Guibo-Pan 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-415499383
Hi @StephanEwen , your suggestion lead me to deep thinks, and the extreme
performance is exactly what we want. I am going to ask you for more
suggestions. I prefer to initialize the partitioner instance with a random
partition, however in the design ahead, the partitioner doesn't know the target
range.
The alternative is like this:
```
private final int[] returnArray = new int[] {new
Random().nextInt(Integer.MAX_VALUE - 1)};
@Override
public int[] selectChannels(SerializationDelegate<StreamRecord<T>> record,
int numberOfOutputChannels) {
this.returnArray[0] = (this.returnArray[0] + 1) %
numberOfOutputChannels;
return this.returnArray;
}
```
Please tell me how you think, thanks.
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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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