[
https://issues.apache.org/jira/browse/IGNITE-3018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15270569#comment-15270569
]
Taras Ledkov commented on IGNITE-3018:
--------------------------------------
The results of the test the bucket based implementation:
Test 63 nodes. Old: 49 ms +/- 10.377 ms; New: 2 ms +/- 5.966 ms; <-- the
threshold nodes count to switch on bucket based calculation
Test 100 nodes. Old: 77 ms +/- 10.279 ms; New: 1 ms +/- 2.149 ms;
Test 200 nodes. Old: 154 ms +/- 13.921 ms; New: 1 ms +/- 2.165 ms;
Test 300 nodes. Old: 233 ms +/- 14.627 ms; New: 1 ms +/- 2.675 ms;
Test 400 nodes. Old: 314 ms +/- 16.676 ms; New: 2 ms +/- 2.520 ms;
Test 500 nodes. Old: 397 ms +/- 18.704 ms; New: 2 ms +/- 2.775 ms;
Test 600 nodes. Old: 477 ms +/- 18.628 ms; New: 2 ms +/- 2.841 ms;
> 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
>
>
> 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).
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
