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https://issues.apache.org/jira/browse/KAFKA-7149?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16948413#comment-16948413
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Ashish Surana commented on KAFKA-7149:
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I think along with these encoding changes we are also compressing the data 
before sending out the assignments.

If producer is already sending compressed data then compressing again while 
writing to __offsets topic won't help but instead if it could decrease the 
performance if the compression algorithm is different at producer and __offset 
topic. I am not sure it's good to add compression for __offset topic here.

> Reduce assignment data size to improve kafka streams scalability
> ----------------------------------------------------------------
>
>                 Key: KAFKA-7149
>                 URL: https://issues.apache.org/jira/browse/KAFKA-7149
>             Project: Kafka
>          Issue Type: Improvement
>          Components: streams
>    Affects Versions: 2.0.0
>            Reporter: Ashish Surana
>            Assignee: Vinoth Chandar
>            Priority: Major
>             Fix For: 2.4.0
>
>
> We observed that when we have high number of partitions, instances or 
> stream-threads, assignment-data size grows too fast and we start getting 
> below RecordTooLargeException at kafka-broker.
> Workaround of this issue is commented at: 
> https://issues.apache.org/jira/browse/KAFKA-6976
> Still it limits the scalability of kafka streams as moving around 100MBs of 
> assignment data for each rebalancing affects performance & reliability 
> (timeout exceptions starts appearing) as well. Also this limits kafka streams 
> scale even with high max.message.bytes setting as data size increases pretty 
> quickly with number of partitions, instances or stream-threads.
>  
> Solution:
> To address this issue in our cluster, we are sending the compressed 
> assignment-data. We saw assignment-data size reduced by 8X-10X. This improved 
> the kafka streams scalability drastically for us and we could now run it with 
> more than 8,000 partitions.



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