[jira] [Commented] (IGNITE-3018) Cache affinity calculation is slow with large nodes number

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/IGNITE-3018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16407799#comment-16407799 ] 

ASF GitHub Bot commented on IGNITE-3018:
----------------------------------------

Github user tledkov-gridgain closed the pull request at:

    https://github.com/apache/ignite/pull/1647


> 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: Taras Ledkov
>            Priority: Major
>              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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