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From "Andrew Wang (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (HBASE-6261) Better approximate high-percentile percentile latency metrics
Date Fri, 28 Dec 2012 15:22:14 GMT

     [ https://issues.apache.org/jira/browse/HBASE-6261?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Andrew Wang resolved HBASE-6261.
--------------------------------

    Resolution: Fixed

Elliot took care of this, porting the histogram from HADOOP-8541 to HBase in HBASE-6409. Thanks!
                
> 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
>          Components: metrics
>            Reporter: Andrew Wang
>            Assignee: Andrew Wang
>              Labels: metrics
>         Attachments: Latencyestimation.pdf, MetricsHistogram.data, parse.py, 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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