[jira] [Comment Edited] (HBASE-25697) StochasticBalancer improvement for large scale clusters

["Clara Xiong (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HBASE-25697?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17406982#comment-17406982 ] 

Clara Xiong edited comment on HBASE-25697 at 8/31/21, 10:10 PM:
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[~idol] Thank you for the suggestion. Could you elaborate on the time window control  you have in mind?

Were you taking about putting more weight on the most recent data for dynamic load cost such as read requests?


was (Author: claraxiong):
[~idol] Thank you for the suggestion. Could you elaborate on the time window control  you have in mind?

> StochasticBalancer improvement for large scale clusters
> -------------------------------------------------------
>
>                 Key: HBASE-25697
>                 URL: https://issues.apache.org/jira/browse/HBASE-25697
>             Project: HBase
>          Issue Type: Improvement
>          Components: Balancer, master, UI
>            Reporter: Clara Xiong
>            Priority: Major
>
> h2. Findings on a large scale cluster (100,000 regions on 300 nodes)
>  * Balancer starts and stops before getting a plan
>  * Adding new racks doesn’t trigger balancer
>  * Balancer stops leaving some racks at 50% lower region counts
>  * Regions for large tables don’t get evenly distributed
>  * Observability is poor
>  * Too many knobs makes tuning empirical and takes many experiments
> h2. Improvements made and bing made
>  * Cost function enhancement to capture outliers especially table skew. https://issues.apache.org/jira/browse/HBASE-25625?filter=-2 
>  * Explain why balancer stops https://issues.apache.org/jira/browse/HBASE-25666 will back port too https://issues.apache.org/jira/browse/HBASE-24528
> h2. More proposals
>  * minCostNeedBalance for each cost function instead of weights. We want to trigger balancing if any factor is out of balancer instead of trying to combine the factors in arbitrary weights. This makes operation and configuration much easier.
>  * Simulated annealing to lower minCostNeedBalance periodically to unstuck the balancer from sub-optimum then gradually increase to keep the system stable. Also add cost of move as a counter measure for the decision [https://opensourcelibs.com/lib/tempest]
>  * Orchestrated scheduling of compaction, normalizer and balancer
>  * PID approach [https://www.amazon.com/dp/1449361692/ref=rdr_ext_tmb]



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