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From Marcello Nuccio <>
Subject Re: Writing Advanced Aggregate Queries -- Equivalent of SELECT COUNT(DISTINCT field) in CouchDB?
Date Thu, 17 Nov 2011 07:02:35 GMT
In this case, I would ask if the date ranges are predetermined. For
example, if you want the number of distinct songs for a user in a
given month, then you can:

    emit([month, user, song], 1);

and then count when song change.


2011/11/16 Zachary Zolton <>:
> Except that the OP specified the need to query for these counts within
> a date range. So, you have to collate by listening time, not the song.
> On Wed, Nov 16, 2011 at 2:45 PM, Marcello Nuccio
> <> wrote:
>> Hi Rob,
>> I remember I've done something similar a while ago, but I cannot find
>> the code right now and I don't have time to rewrite it right now...
>> however the trick is to only count when the song name changes. This
>> works because view rows are sorted.
>> HTH,
>>  Marcello
>> 2011/11/15 Rob Crowell <>:
>>> Hello everyone,
>>> I'm writing some log-parsing code which is currently running on a
>>> MySQL backend.  We're doing a huge amount of aggregates on this data
>>> right now, but performance is suffering and I'm looking for
>>> alternatives.  The idea of incremental map/reduce initially seemed
>>> like the exact right thing, but I can't seem to express some of the
>>> most important queries we are currently running in our production
>>> system.
>>> We're running a lot of queries of the SELECT COUNT(DISTINCT song_id)
>>> WHERE user_id = "boris" AND created >= "2010-01-01" AND created <
>>> "2010-02-01" variety.  Currently in MySQL-land we've got a cron job to
>>> pre-compute these aggregates (it checks modified timestamps and pulls
>>> in only new records) and write them to a summary table.  I initially
>>> believed I could use CouchDB's incremental map/reduce to effortlessly
>>> build and update our "summary information" as it changes, but I'm
>>> stuck.  I'm trying to relax, but I can't figure out exactly how :)
>>> In our example, our user "boris" listens to the same song many times
>>> each month, and we're interested in the number of distinct songs he's
>>> listened to during a specified time period (NOT the number of song
>>> plays, but the number of distinct songs played).  In CouchDB it isn't
>>> much trouble to get all of the unique songs that he's listened to
>>> during a period.  Here's our document:
>>> {
>>>  song_id: "happy birthday",
>>>  user_id: "boris",
>>>  date_played: [2011, 11, 14, 00, 12, 55],
>>>  _id: ...
>>> }
>>> To get the unique values, all we need to do is emit([doc.user_id,
>>> doc.date_played, doc.song_id], null), reduce with _count, and query
>>> with a startkey=["boris", "2011-01-01"]&endkey=["boris",
>>> "2011-02-01"]&group_level=1.  This query will yield results like:
>>> ["boris", "happy birthday"], 20
>>> ["boris", "yesterday"], 14
>>> ...
>>> However, if our user has listened to 50,000 songs during the date
>>> range, we'll get back 50,000 rows which seems expensive.  What I want
>>> is just the scalar 50,000.  I've tried writing a reduce that returns
>>> the set of distinct song_ids for each user (turning the values list
>>> into a dictionary and back again), but CouchDB complains that I am not
>>> reducing my values fast enough :-/  I'm also not sure how to reduce
>>> this list to a scalar at the end without returning the whole thing to
>>> my client (which defeats the purpose of all this anyways).
>>> Is this possible to do in CouchDB today?  If not, is it something that
>>> is on the roadmap, or does the internal structure of CouchDB's b-tree
>>> make this really hard to do?  It would of course be possible for me to
>>> implement this myself (subscribe to the update notifications and
>>> update my counts as appropriate in a custom script), but I wanted to
>>> move to CouchDB so that I wouldn't have to do all this myself.
>>> Thanks for any advice!
>>> --Rob

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