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https://issues.apache.org/jira/browse/SPARK-23996?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-23996.
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Resolution: Incomplete
> Implement the optimal KLL algorithms for quantiles in streams
> -------------------------------------------------------------
>
> Key: SPARK-23996
> URL: https://issues.apache.org/jira/browse/SPARK-23996
> Project: Spark
> Issue Type: Improvement
> Components: MLlib, SQL
> Affects Versions: 2.3.0
> Reporter: Timothy Hunter
> Priority: Major
> Labels: bulk-closed
>
> The current implementation for approximate quantiles - a variant of
> Grunwald-Khanna, which I implemented - is not the best in light of recent
> papers:
> - it is not exactly the one from the paper for performance reasons, but the
> changes are not documented beyond comments on the code
> - there are now more optimal algorithms with proven bounds (unlike q-digest,
> the other contender at the time)
> I propose that we revisit the current implementation and look at the
> Karnin-Lang-Liberty algorithm (KLL) for example:
> [https://arxiv.org/abs/1603.05346]
> [https://edoliberty.github.io//papers/streamingQuantiles.pdf]
> This algorithm seems to have favorable characteristics for streaming and a
> distributed implementation, and there is a python implementation for
> reference.
> It is a fairly standalone piece, and in that respect available to people who
> don't know too much about spark internals.
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