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Date: Fri, 29 Jun 2012 00:47:44 +0000 (UTC)
From: "Andrew Wang (JIRA)" <jira@apache.org>
To: issues@hbase.apache.org
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Subject: [jira] [Assigned] (HBASE-6261) Better approximate high-percentile
 percentile latency metrics
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     [ https://issues.apache.org/jira/browse/HBASE-6261?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Andrew Wang reassigned HBASE-6261:
----------------------------------

    Assignee: Andrew Wang
    
> 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
>
>
> 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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