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https://issues.apache.org/jira/browse/HBASE-6261?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13403280#comment-13403280
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Andrew Wang commented on HBASE-6261:
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I don't think performance is very sensitive to the buffer size, it's just a way
of batching inserts for efficiency. Definitely doesn't affect accuracy because
I have it call insertBatch() on every query().
We can maintain the compress count and track the # items removed, but I don't
know if it's really worth exposing to the user (metrics for our metrics?). I
think it's nice for testing though, so I'll try to expose it internally.
I've never seen compress() fail to remove any items, but I guess this could
happen with some adversarial pattern. I don't think you can do much about it
though, since the algo needs those items to maintain the error bounds.
> 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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