[jira] [Commented] (COUCHDB-2971) Provide cardinality estimate (COUNT DISTINCT) as builtin reducer

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    [ https://issues.apache.org/jira/browse/COUCHDB-2971?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15890503#comment-15890503 ] 

ASF subversion and git services commented on COUCHDB-2971:
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Commit f7c3c24db17db5908894447e1822936212d61fcd in couchdb-couch-mrview's branch refs/heads/2971-count-distinct from [~kocolosk]
[ https://git-wip-us.apache.org/repos/asf?p=couchdb-couch-mrview.git;h=f7c3c24 ]

Add _distinct as a built-in reduce

COUCHDB-2971


> Provide cardinality estimate (COUNT DISTINCT) as builtin reducer
> ----------------------------------------------------------------
>
>                 Key: COUCHDB-2971
>                 URL: https://issues.apache.org/jira/browse/COUCHDB-2971
>             Project: CouchDB
>          Issue Type: Improvement
>            Reporter: Adam Kocoloski
>
> We’ve seen a number of applications now where a user needs to count the number of unique keys in a view. Currently the recommended approach is to add a trivial reduce function and then count the number of rows in a _list function or client-side application code, but of course that doesn’t scale nicely.
> It seems that in a majority of these cases all that’s required is an approximation of the number of distinct entries, which brings us into the space of hash sets, linear probabilistic counters, and the ever-popular “HyperLogLog” algorithm. Taking HLL specifically, this seems like quite a nice candidate for a builtin reduce. The size of the data structure is independent of the number of input elements and individual HLL filters can be unioned together. There’s already what seems to be a good MIT-licensed implementation on GitHub:
> https://github.com/GameAnalytics/hyper
> One caveat is that this reducer would not work for group_level reductions; it’d only give the correct result for the exact key. I don’t think that should preclude us from evaluating it.



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