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https://issues.apache.org/jira/browse/FLINK-10886?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Flink Jira Bot updated FLINK-10886:
-----------------------------------
    Labels: auto-unassigned stale-major  (was: auto-unassigned)

I am the [Flink Jira Bot|https://github.com/apache/flink-jira-bot/] and I help 
the community manage its development. I see this issues has been marked as 
Major but is unassigned and neither itself nor its Sub-Tasks have been updated 
for 60 days. I have gone ahead and added a "stale-major" to the issue". If this 
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> Event time synchronization across sources
> -----------------------------------------
>
>                 Key: FLINK-10886
>                 URL: https://issues.apache.org/jira/browse/FLINK-10886
>             Project: Flink
>          Issue Type: Improvement
>          Components: Connectors / Common
>            Reporter: Jamie Grier
>            Priority: Major
>              Labels: auto-unassigned, stale-major
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> When reading from a source with many parallel partitions, especially when 
> reading lots of historical data (or recovering from downtime and there is a 
> backlog to read), it's quite common for there to develop an event-time skew 
> across those partitions.
>  
> When doing event-time windowing -- or in fact any event-time driven 
> processing -- the event time skew across partitions results directly in 
> increased buffering in Flink and of course the corresponding state/checkpoint 
> size growth.
>  
> As the event-time skew and state size grows larger this can have a major 
> effect on application performance and in some cases result in a "death 
> spiral" where the application performance get's worse and worse as the state 
> size grows and grows.
>  
> So, one solution to this problem, outside of core changes in Flink itself, 
> seems to be to try to coordinate sources across partitions so that they make 
> progress through event time at roughly the same rate.  In fact if there is 
> large skew the idea would be to slow or even stop reading from some 
> partitions with newer data while first reading the partitions with older 
> data.  Anyway, to do this we need to share state somehow amongst sub-tasks.
>  



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