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From "Taras Ledkov (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (IGNITE-3018) Cache affinity calculation is slow with large nodes number
Date Tue, 07 Mar 2017 13:20:38 GMT

    [ https://issues.apache.org/jira/browse/IGNITE-3018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15899422#comment-15899422
] 

Taras Ledkov commented on IGNITE-3018:
--------------------------------------

Tests [results|http://195.239.208.174/project.html?projectId=IgniteTests&tab=projectOverview&branch_IgniteTests=pull%2F1600%2Fhead]
with partition balancer. Partition balancer try to make partition distribution closer to even
distribution.

> 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
>             Fix For: 2.0
>
>         Attachments: 003.png, 064.png, 100.png, 128.png, 200.png, 300.png, 400.png, 500.png,
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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