Timothy Hunter created SPARK-23996:
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Summary: 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
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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