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From "Elliott Clark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-6261) Better approximate high-percentile percentile latency metrics
Date Tue, 24 Jul 2012 00:44:35 GMT

    [ https://issues.apache.org/jira/browse/HBASE-6261?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13421063#comment-13421063

Elliott Clark commented on HBASE-6261:

Yes.  I was thinking that I would bring Andrew's quantile stuff in while working on the transition
to metrics2.

Wow.  That's pretty awesome stuff.  I think your final recommendations are spot on.  We should
have a way for users to turn on/off these high fidelity histograms for different sets of metrics
(rpc, compactions, etc).
> Better approximate high-percentile percentile latency metrics
> -------------------------------------------------------------
>                 Key: HBASE-6261
>                 URL: https://issues.apache.org/jira/browse/HBASE-6261
>             Project: HBase
>          Issue Type: New Feature
>            Reporter: Andrew Wang
>            Assignee: Andrew Wang
>              Labels: metrics
>         Attachments: Latencyestimation.pdf, MetricsHistogram.data, SampleQuantiles.data,
> The existing reservoir-sampling based latency metrics in HBase are not well-suited for
providing accurate estimates of high-percentile (e.g. 90th, 95th, or 99th) latency. This is
a well-studied problem in the literature (see [1] and [2]), the question is determining which
methods best suit our needs and then implementing it.
> Ideally, we should be able to estimate these high percentiles with minimal memory and
CPU usage as well as minimal error (e.g. 1% error on 90th, or .1% on 99th). It's also desirable
to provide this over different time-based sliding windows, e.g. last 1 min, 5 mins, 15 mins,
and 1 hour.
> I'll note that this would also be useful in HDFS, or really anywhere latency metrics
are kept.
> [1] http://www.cs.rutgers.edu/~muthu/bquant.pdf
> [2] http://infolab.stanford.edu/~manku/papers/04pods-sliding.pdf

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