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https://issues.apache.org/jira/browse/HBASE-6261?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13403595#comment-13403595
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Elliott Clark commented on HBASE-6261:
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But we won't be able to require the new version of hadoop that would contain
the code for quite a while. So we would have to keep our current Histogram
implementation, use reflection to see if hadoop jars contain UberHistogram(or
whatever you plan on calling it), if so use reflection to interact with it.
> 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
> 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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