[jira] [Resolved] (KAFKA-9987) Improve sticky partition assignor algorithm

["Sophie Blee-Goldman (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/KAFKA-9987?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sophie Blee-Goldman resolved KAFKA-9987.
----------------------------------------
    Fix Version/s: 2.5.1
                   2.4.2
                   2.6.0
       Resolution: Fixed

> Improve sticky partition assignor algorithm
> -------------------------------------------
>
>                 Key: KAFKA-9987
>                 URL: https://issues.apache.org/jira/browse/KAFKA-9987
>             Project: Kafka
>          Issue Type: Improvement
>          Components: clients
>            Reporter: Sophie Blee-Goldman
>            Assignee: Sophie Blee-Goldman
>            Priority: Major
>             Fix For: 2.6.0, 2.4.2, 2.5.1
>
>
> In [KIP-429|https://cwiki.apache.org/confluence/display/KAFKA/KIP-429%3A+Kafka+Consumer+Incremental+Rebalance+Protocol] we added the new CooperativeStickyAssignor which leverages on the underlying sticky assignment algorithm of the existing StickyAssignor (moved to AbstractStickyAssignor). The algorithm is fairly complex as it tries to optimize stickiness while satisfying perfect balance _in the case individual consumers may be subscribed to different subsets of the topics._ While it does a pretty good job at what it promises to do, it doesn't scale well with large numbers of consumers and partitions.
> To give a concrete example, users have reported that it takes 2.5 minutes for the assignment to complete with just 2100 consumers reading from 2100 partitions. Since partitions revoked during the first of two cooperative rebalances will remain unassigned until the end of the second rebalance, it's important for the rebalance to be as fast as possible. And since one of the primary improvements of the cooperative rebalancing protocol is better scaling experience, the only OOTB cooperative assignor should not itself scale poorly
> If we can constrain the problem a bit, we can simplify the algorithm greatly. In many cases the individual consumers won't be subscribed to some random subset of the total subscription, they will all be subscribed to the same set of topics and rely on the assignor to balance the partition workload.
> We can detect this case by checking the group's individual subscriptions and call on a more efficient assignment algorithm. 



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