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https://issues.apache.org/jira/browse/CASSANDRA-13900?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Thomas Steinmaurer updated CASSANDRA-13900:
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    Attachment:     (was: cassandra2.1.18_vs_3.0.14.png)

> Massive GC suspension increase after updating to 3.0.14 from 2.1.18
> -------------------------------------------------------------------
>
>                 Key: CASSANDRA-13900
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-13900
>             Project: Cassandra
>          Issue Type: Bug
>            Reporter: Thomas Steinmaurer
>            Priority: Blocker
>         Attachments: cassandra2118_vs_3014.jpg
>
>
> In short: After upgrading to 3.0.14 (2.1.18), we aren't able to process the 
> same incoming write load on the same infrastructure anymore.
> We have a loadtest environment running 24x7 testing our software using 
> Cassandra as backend. Both, loadtest and production is hosted in AWS and do 
> have the same spec on the Cassandra-side, namely:
> * 9x m4.xlarge
> * 8G heap
> * CMS (400MB newgen)
> * 2TB EBS gp2
> per node. We have a solid/constant baseline in loadtest at ~ 60% CPU cluster 
> AVG with constant, simulated load running against our cluster, using 
> Cassandra 2.1 for > 2 years now.
> Recently we started to upgrade to 3.0.14 in this 9 node loadtest environment, 
> and basically, 3.0.14 isn't able to cope with the load anymore. No particular 
> special tweaks, memory settings/changes etc., all the same as in 2.1.8. We 
> also didn't upgrade sstables yet, thus the increase mentioned below is not 
> related to any manually triggered maintenance operation after upgrading to 
> 3.0.14.
> According to our monitoring, with 3.0.14, we see a GC suspension time 
> increase by a factor of > 2, of course directly correlating with an CPU 
> increase > 80%.
> !cassandra2118_vs_3014.jpg!
> This all means that our incoming load for several weeks now against 2.1.18 is 
> something, 3.0.14 can't handle. So, we would need to either scale up (e.g. to 
> m4.2xlarge) or scale out for being able to handle the same load.



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