[jira] [Updated] (KAFKA-13600) Rebalances while streams is in degraded state can cause stores to be reassigned and restore from scratch

["Tim Patterson (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/KAFKA-13600?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Tim Patterson updated KAFKA-13600:
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    Summary: Rebalances while streams is in degraded state can cause stores to be reassigned and restore from scratch  (was: Rebalances while streams is in degraded state can stores to be assigned and restore from scratch)

> Rebalances while streams is in degraded state can cause stores to be reassigned and restore from scratch
> --------------------------------------------------------------------------------------------------------
>
>                 Key: KAFKA-13600
>                 URL: https://issues.apache.org/jira/browse/KAFKA-13600
>             Project: Kafka
>          Issue Type: Bug
>          Components: streams
>    Affects Versions: 2.8.1, 3.0.0
>            Reporter: Tim Patterson
>            Priority: Major
>
> Consider this scenario:
>  # A node is lost from the cluster.
>  # A rebalance is kicked off with a new "target assignment"'s(ie the rebalance is attempting to move a lot of tasks - see https://issues.apache.org/jira/browse/KAFKA-10121).
>  # The kafka cluster is now a bit more sluggish from the increased load.
>  # A Rolling Deploy happens triggering rebalances, during the rebalance processing continues but offsets can't be committed(Or nodes are restarted but fail to commit offsets)
>  # The most caught up nodes now aren't within `acceptableRecoveryLag` and so the task is started in it's "target assignment" location, restoring all state from scratch and delaying further processing instead of using the "almost caught up" node.
> We've hit this a few times and having lots of state (~25TB worth) and being heavy users of IQ this is not ideal for us.
> While we can increase `acceptableRecoveryLag` to larger values to try get around this that causes other issues (ie a warmup becoming active when its still quite far behind)
> The solution seems to be to balance "balanced assignment" with "most caught up nodes".
> We've got a fork where we do just this and it's made a huge difference to the reliability of our cluster.
> Our change is to simply use the most caught up node if the "target node" is more than `acceptableRecoveryLag` behind.
> This gives up some of the load balancing type behaviour of the existing code but in practise doesn't seem to matter too much.
> I guess maybe an algorithm that identified candidate nodes as those being within `acceptableRecoveryLag` of the most caught up node might allow the best of both worlds.
>  
> Our fork is
> [https://github.com/apache/kafka/compare/trunk...tim-patterson:fix_balance_uncaughtup?expand=1]
> (We also moved the capacity constraint code to happen after all the stateful assignment to prioritise standby tasks over warmup tasks)
> Ideally we don't want to maintain a fork of kafka streams going forward so are hoping to get a bit of discussion / agreement on the best way to handle this.
> More than happy to contribute code/test different algo's in production system or anything else to help with this issue



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