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https://issues.apache.org/jira/browse/CASSANDRA-8503?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14253264#comment-14253264
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Benedict commented on CASSANDRA-8503:
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bq. as a reason to not do
Not at all. I am suggesting we take a timeseries data model and workload and
see if we can represent it in a way that is functionally equivalent for the
tests we will be performing (which, note, will be short lived tests so DTCS and
other compaction considerations won't be so important) with stress' current
facilities. It may not *look* identical, but it could be functionally
equivalent all the same. If it comes up short at being able to represent it, we
can introduce some small tweaks to make it much closer still (such as using the
seed to bound the timestamp generation for PK and clustering columns, at which
point I think the only possible limitation would be certain kinds of query
slicing behaviours).
The main impediment is 7980. I'm not sure what you mean about structural vs
functional. It's not a dramatically difficult feature to add, it's just
allocating the time to do so.
> Collect important stress profiles for regression analysis done by jenkins
> -------------------------------------------------------------------------
>
> Key: CASSANDRA-8503
> URL: https://issues.apache.org/jira/browse/CASSANDRA-8503
> Project: Cassandra
> Issue Type: Task
> Reporter: Ryan McGuire
> Assignee: Ryan McGuire
> Attachments: inmemory.yaml, ycsb.yaml
>
>
> We have a weekly job setup on CassCI to run a performance benchmark against
> the dev branches as well as the last stable releases.
> Here's an example:
> http://cstar.datastax.com/tests/id/8223fe2e-8585-11e4-b0bf-42010af0688f
> This test is currently pretty basic, it's running on three nodes, with a the
> default stress profile. We should crowdsource a collection of stress profiles
> to run, and then once we have many of these tests running we can collect them
> all into a weekly email.
> Ideas:
> * Timeseries (Can this be done with stress? not sure)
> * compact storage
> * compression off
> * ...
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