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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 Wed, 04 May 2016 15:15:12 GMT

    [ https://issues.apache.org/jira/browse/IGNITE-3018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15270799#comment-15270799
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Taras Ledkov commented on IGNITE-3018:
--------------------------------------

Please take a look at the attached heatmaps. The node distributions of affinity function with
MD5 hash & Wang hash with bucket based algorithm are compared for 3, 64, 100, 128, 200,
..., 600 nodes.

Horizontally: node's order (primary node, backup0, backup 1);
Vertically: all nodes from topology;
Z-order: count of node is placed in the specified order (e.g. node is primary nodes) for all
partitions.


> 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: Critical
>             Fix For: 1.6
>
>         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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