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https://issues.apache.org/jira/browse/IGNITE-3018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15963999#comment-15963999
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Yakov Zhdanov commented on IGNITE-3018:
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[~tledkov-gridgain], can you please update the table with migrating backups
count. I am assuming that migration will result in actual rebalancing. E.g. if
node A has primary partition # 1 on topology X and on X+1 it has it as a backup
then no actual rebalancing happens.
> Cache affinity calculation is slow with large nodes number
> ----------------------------------------------------------
>
> Key: IGNITE-3018
> URL: https://issues.apache.org/jira/browse/IGNITE-3018
> Project: Ignite
> Issue Type: Bug
> Components: cache
> Reporter: Semen Boikov
> Assignee: Yakov Zhdanov
> Labels: important
> Fix For: 2.0
>
> Attachments: 003.png, 004.png, 008.png, 016.png, 064.png, 100.png,
> 128.png, 200.png, 256.png, 400.png, 600.png, balanced.003.png,
> balanced.004.png, balanced.008.png, balanced.016.png, balanced.064.png,
> balanced.100.png, balanced.128.png, balanced.200.png, balanced.256.png,
> balanced.400.png, balanced.600.png
>
>
> With large number of cache server nodes (> 200) RendezvousAffinityFunction
> and FairAffinityFunction work pretty slow .
> For RendezvousAffinityFunction.assignPartitions can take hundredes of
> milliseconds, for FairAffinityFunction it can take seconds.
> For RendezvousAffinityFunction most time is spent in MD5 hash calculation and
> nodes list sorting. As optimization we can try to cache {partion, node} MD5
> hash or try another hash function. Also several minor optimizations are
> possible (avoid unncecessary allocations, only one thread local 'get', etc).
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