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https://issues.apache.org/jira/browse/KAFKA-2397?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14731364#comment-14731364
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Onur Karaman commented on KAFKA-2397:
-------------------------------------
Here's a summary of what I think each approach has to offer over the other.
Pros of LeaveGroupRequest:
1. simplicity. I think the logic is pretty straightforward, keeps the network
and api layers separated, fits well with existing patterns, and doesn't require
a complicated refactoring.
2. opens up the possibility for tooling that lets you kick out a consumer and
trigger a rebalance. This might be a useful admin tool for when things go wrong.
3. opens up the possibility for rolling bounces without triggering a rebalance.
We can modify the consumer to have a close and closeNow (close sends out a
LeaveGroupRequest, closeNow doesn't). The application can persist the consumer
id somewhere. The consumer can initially try out the persisted consumer id
after it comes back up.
Pros of tcp disconnect:
1. rebalance gets triggered on process death. This would be a con if you want
the possibility for rolling bounces without triggering a rebalance.
P.S: I'm going to be on vacation from tonight to Tuesday so my responsiveness
will be a bit spotty from tonight until Tuesday. I think Jiangjie may be in a
similar situation.
> leave group request
> -------------------
>
> Key: KAFKA-2397
> URL: https://issues.apache.org/jira/browse/KAFKA-2397
> Project: Kafka
> Issue Type: Sub-task
> Components: consumer
> Reporter: Onur Karaman
> Assignee: Onur Karaman
> Priority: Minor
> Fix For: 0.8.3
>
>
> Let's say every consumer in a group has session timeout s. Currently, if a
> consumer leaves the group, the worst case time to stabilize the group is 2s
> (s to detect the consumer failure + s for the rebalance window). If a
> consumer instead can declare they are leaving the group, the worst case time
> to stabilize the group would just be the s associated with the rebalance
> window.
> This is a low priority optimization!
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