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https://issues.apache.org/jira/browse/IGNITE-3018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15885471#comment-15885471
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ASF GitHub Bot commented on IGNITE-3018:
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GitHub user tledkov-gridgain opened a pull request:
https://github.com/apache/ignite/pull/1575
IGNITE-3018: RendezvousAffinityFunction performance tuning
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/gridgain/apache-ignite ignite-3018
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/ignite/pull/1575.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #1575
----
commit ed35a9eb6f02771c5996209a1e19a4b0557309b6
Author: tledkov-gridgain <tled...@gridgain.com>
Date: 2017-02-27T09:49:31Z
IGNITE-3018: RendezvousAffinityFunction performance tuning
----
> 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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