[jira] [Commented] (IGNITE-3018) Cache affinity calculation is slow with large nodes number

["Taras Ledkov (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/IGNITE-3018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15257772#comment-15257772 ] 

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

New implementation use the cache for MD5 hash & cache of serialization results of a node's resolvers.
Approximates times of call the RendezvousAffinityFunction.assignPartitions. The test replaces the last node 500 times:

Test 100 nodes. Old: 74 ms +/- 11.202 ms; New: 24 ms +/- 8.604 ms;
Test 200 nodes. Old: 152 ms +/- 15.816 ms; New: 64 ms +/- 13.450 ms;
Test 300 nodes. Old: 231 ms +/- 16.516 ms; New: 103 ms +/- 15.008 ms;
Test 400 nodes. Old: 310 ms +/- 18.549 ms; New: 181 ms +/- 28.094 ms;
Test 500 nodes. Old: 385 ms +/- 15.571 ms; New: 264 ms +/- 36.831 ms;
Test 600 nodes. Old: 464 ms +/- 16.210 ms; New: 383 ms +/- 73.448 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).



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