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https://issues.apache.org/jira/browse/COUCHDB-2971?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16502737#comment-16502737
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ASF subversion and git services commented on COUCHDB-2971:
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Commit 6d44e17fccc44c377476247d9765fc573154097f in couchdb's branch
refs/heads/master from [~kocolosk]
[ https://gitbox.apache.org/repos/asf?p=couchdb.git;h=6d44e17 ]
Add _approx_count_distinct as a builtin reduce function (#1346)
This introduces a new builtin reduce function, which uses a HyperLogLog
algorithm to estimate the number of distinct keys in the view index. The
precision is currently fixed to 2^11 observables andtherefore uses
approximately 1.5 KB of memory.
It also introduces a finalize step which can be used to improve the
efficiency of other builtin reduce functions going forward.
Closes 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
> Priority: Major
> Attachments: rebar.config.script
>
>
> 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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