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Posted to issues@yunikorn.apache.org by "Peter Bacsko (Jira)" <ji...@apache.org> on 2023/05/16 15:34:00 UTC

[jira] [Updated] (YUNIKORN-1740) [Umbrella] MockScheduler improvements

     [ https://issues.apache.org/jira/browse/YUNIKORN-1740?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Peter Bacsko updated YUNIKORN-1740:
-----------------------------------
    Summary: [Umbrella] MockScheduler improvements  (was: MockScheduler improvements)

> [Umbrella] MockScheduler improvements
> -------------------------------------
>
>                 Key: YUNIKORN-1740
>                 URL: https://issues.apache.org/jira/browse/YUNIKORN-1740
>             Project: Apache YuniKorn
>          Issue Type: Improvement
>          Components: shim - kubernetes, test - unit
>            Reporter: Peter Bacsko
>            Assignee: Peter Bacsko
>            Priority: Major
>
> With the {{MockScheduler}} object, it's possible to test Yunikorn as a whole without having an actual Kubernetes cluster.
> Currently, there are some limitations of {{{}MockScheduler{}}}. For example:
>  * adding node only updates the core, not the shim
>  * recovery is not tested (ignored because of {{APIProvider.IsTestingMode()}}
>  * predicates are not called (ignored because of {{APIProvider.IsTestingMode()}}
> However, it's possible to update the logic in such a way that we can simulate the entire code path for the lifecycle of an application:
>  * New {{*v1.Pod}} object -> shim -> core -> allocation
>  * allocation -> create SI object -> shim -> bind Pod
>  * Running state: updated {{*v1.Pod}} -> shim -> process pod object in the cache
>  * Completed/Failed state updated {{*v1.Pod}} -> shim > process pod object in the cache -> core -> update application state
>  * {{*v1.Node}} object -> shim -> update cache -> core -> node&cluster update
> This can also gives us a possibility to check the performance of Yunikorn and collect CPU&heap samples. This will not a replacement of a real performance test, but with a proper test case, we can quickly do a rough assessment of performance/memory usage related changes.



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