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Posted to issues@hbase.apache.org by "Andrew Wang (JIRA)" <ji...@apache.org> on 2012/12/28 16:22:14 UTC
[jira] [Resolved] (HBASE-6261) Better approximate high-percentile
percentile latency metrics
[ 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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