[ https://issues.apache.org/jira/browse/IGNITE-3018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15963999#comment-15963999 ]
Yakov Zhdanov commented on IGNITE-3018: --------------------------------------- [~tledkov-gridgain], can you please update the table with migrating backups count. I am assuming that migration will result in actual rebalancing. E.g. if node A has primary partition # 1 on topology X and on X+1 it has it as a backup then no actual rebalancing happens. > 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 > Labels: important > Fix For: 2.0 > > Attachments: 003.png, 004.png, 008.png, 016.png, 064.png, 100.png, > 128.png, 200.png, 256.png, 400.png, 600.png, balanced.003.png, > balanced.004.png, balanced.008.png, balanced.016.png, balanced.064.png, > balanced.100.png, balanced.128.png, balanced.200.png, balanced.256.png, > balanced.400.png, balanced.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). -- This message was sent by Atlassian JIRA (v6.3.15#6346)