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https://issues.apache.org/jira/browse/HDDS-10612?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17833872#comment-17833872
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Ivan Andika commented on HDDS-10612:
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[~afilpp] Containers can be closed automatically due to variety of reasons
(e.g. container is almost full, container is unhealthy, etc). Since the size of
the keys are nowhere near the max container size, maybe not.
The possible problem I mentioned was due to the lag between the close container
event sent to event queue and it being processed, a container list operation
might list containers that are due to be closed as OPEN, which cause the test
to send another container close request.
Perhaps, we can use "Execute And Ignore Error" instead of "Execute" so that
container exception due to duplicate container close request / container that
are automatically closed will not cause the acceptance test to fail.
> Add Robot test to verify Container Balancer for RATIS containers
> ----------------------------------------------------------------
>
> Key: HDDS-10612
> URL: https://issues.apache.org/jira/browse/HDDS-10612
> Project: Apache Ozone
> Issue Type: Test
> Components: test
> Reporter: Anastasia Filippova
> Assignee: Anastasia Filippova
> Priority: Major
> Labels: pull-request-available
> Fix For: 1.5.0
>
>
> Currently there are only unit tests for Container Balancer and no acceptance
> tests at all. At a minimum, we should add a Robot test to verify Container
> Balancer for RATIS containers. And probably in the future we should add robot
> test for EC case.
> Test case:
> 1. Move 1 datanode to maintenance mode (we use 4 datanodes in this test)
> 2. Create multiple keys (after loading the data, we check that 3 datanodes
> are ~60% busy, and the one that is in maintenance mode is empty)
> 4. Start datanode recommission (wait until datanode recommissioning is
> completed)
> 5. Start container balancer (wait until container balancer is completed)
> 6. Check results (after balancing on all 4 datanodes, we should see
> approximately the same data distribution.)
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