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Posted to issues@flink.apache.org by "Yuta Morisawa (JIRA)" <ji...@apache.org> on 2018/01/31 07:41:00 UTC
[jira] [Updated] (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 ]
Yuta Morisawa updated FLINK-8532:
---------------------------------
Description:
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.
was:
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.
> 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
> Priority: Minor
>
> 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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