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https://issues.apache.org/jira/browse/FLINK-20390?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Flink Jira Bot updated FLINK-20390:
-----------------------------------
      Labels: auto-deprioritized-major auto-deprioritized-minor  (was: 
auto-deprioritized-major stale-minor)
    Priority: Not a Priority  (was: Minor)

This issue was labeled "stale-minor" 7 days ago and has not received any 
updates so it is being deprioritized. If this ticket is actually Minor, please 
raise the priority and ask a committer to assign you the issue or revive the 
public discussion.


> Programmatic access to the back-pressure
> ----------------------------------------
>
>                 Key: FLINK-20390
>                 URL: https://issues.apache.org/jira/browse/FLINK-20390
>             Project: Flink
>          Issue Type: New Feature
>          Components: API / Core
>            Reporter: Gaël Renoux
>            Priority: Not a Priority
>              Labels: auto-deprioritized-major, auto-deprioritized-minor
>
> It would be useful to access the back-pressure monitoring from within 
> functions.
> Here is our use case: we have a real-time Flink job, which takes decisions 
> based on input data. Sometimes, we have traffic spikes on the input and the 
> decisions process cannot processe records fast enough. Back-pressure starts 
> mounting, all the way back to the Source. What we want to do is to start 
> dropping records in this case, because it's better to make decisions based on 
> just a sample of the data rather than accumulate too much lag.
> Right now, the only way is to have a filter with a hard-limit on the number 
> of records per-interval-of-time, and to drop records once we are over this 
> limit. However, this requires a lot of tuning to find out what the correct 
> limit is, especially since it may depend on the nature of the inputs (some 
> decisions take longer to make than others). It's also heavily dependent on 
> the buffers: the limit needs to be low enough that all records that pass the 
> limit can fit in the downstream buffers, or the back-pressure will will go 
> back past the filtering task and we're back to square one. Finally, it's not 
> very resilient to change: whenever we scale the infrastructure up, we need to 
> redo the whole tuning thing.
> With programmatic access to the back-pressure, we could simply start dropping 
> records based on its current level. No tuning, and adjusted to the actual 
> issue. For performance, I assume it would be better if it reused the existing 
> back-pressure monitoring mechanism, rather than looking directly into the 
> buffer. A sampling of the back-pressure should be enough, and if more 
> precision is needed you can simply change the existing back-pressure 
> configuration.



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