[jira] [Issue Comment Deleted] (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:all-tabpanel ]

Taras Ledkov updated IGNITE-3018:
---------------------------------
    Comment: was deleted

(was: 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(i) is placed in the specified order (e.g. node(i) 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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