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https://issues.apache.org/jira/browse/SOLR-11730?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16334174#comment-16334174
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ASF subversion and git services commented on SOLR-11730:
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Commit 6752e4c72f8f98c6ddca2669e4ac34aa93b19294 in lucene-solr's branch
refs/heads/branch_7x from [~ab]
[ https://git-wip-us.apache.org/repos/asf?p=lucene-solr.git;h=6752e4c ]
SOLR-11730: Collect more stats in the benchmark. Add simulation framework
package docs.
> Test NodeLost / NodeAdded dynamics
> ----------------------------------
>
> Key: SOLR-11730
> URL: https://issues.apache.org/jira/browse/SOLR-11730
> Project: Solr
> Issue Type: Sub-task
> Security Level: Public(Default Security Level. Issues are Public)
> Components: AutoScaling
> Reporter: Andrzej Bialecki
> Priority: Major
>
> Let's consider a "flaky node" scenario.
> A node is going up and down at short intervals (eg. due to a flaky network
> cable). If the frequency of these events coincides with {{waitFor}} interval
> in {{nodeLost}} trigger configuration, the node may never be reported to the
> autoscaling framework as lost. Similarly it may never be reported as added
> back if it's lost again within the {{waitFor}} period of {{nodeAdded}}
> trigger.
> Other scenarios are possible here too, depending on timing:
> * node being constantly reported as lost
> * node being constantly reported as added
> One possible solution for the autoscaling triggers is that the framework
> should keep a short-term ({{waitFor * 2}} long?) memory of a node state that
> the trigger is tracking in order to eliminate flaky nodes (ie. those that
> transitioned between states more than once within the period).
> Situation like this is detrimental to SolrCloud behavior regardless of
> autoscaling actions, so it should probably be addressed at a node level by
> eg. shutting down Solr node after the number of disconnects in a time window
> reaches a certain threshold.
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