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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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