Hi Dan, it's not a general-purpose solution, but in the specific
example you gave where there's only one continuous variable you might
be able to do something like the following. Use a map function that
emits your filterable quantities as a list:
emit([doc.beds, doc.baths, doc.price], null)
and then query that view multiple times with:
startkey=[4,2,350000]&endkey=[4,2,400000]
startkey=[4,3,350000]&endkey=[4,3,400000]
startkey=[5,2,350000]&endkey=[5,2,400000]
startkey=[5,2,350000]&endkey=[4,3,400000]
The advantage is that you get only the data you need; the disadvantage
is that the number of queries scales non-linearly with the number of
fields used in the filter, and there's no easy way to skip fields
(you'd need to query with every possible value of that field, or else
write an additional view that doesn't emit it).
A possible middle ground would be to query this same view with a
single request:
startkey=[4,2,350000]&endkey=[5,3,400000]
You'll still need to filter the results on the client side, since e.g.
a 4 bed, 2 bath, $600k listing would get included, but at least the
data volume would be smaller than doing the whole intersection
yourself. If you go this route, the discrete vs. continuous variable
thing doesn't really matter; just arrange the keys so that the one
with the greatest discriminating power comes first.
Best, Adam
On Dec 14, 2008, at 12:06 PM, Dan Woolley wrote:
I'm researching Couchdb for a project dealing with real estate
listing data. I'm very interested in Couchdb because the schema
less nature, RESTful interface, and potential off-line usage with
syncing fit my problem very well. I've been able to do some
prototyping and search on ranges for a single field very
successfully. I'm having trouble wrapping my mind around views for
a popular use case in real estate, which is a query like:
Price = 350000-400000
Beds = 4-5
Baths = 2-3
Any single range above is trivial, but what is the best model for
handling this AND scenario with views? The only thing I've been
able to come up with is three views returning doc id's - which
should be very fast - with an array intersection calculation on the
client side. Although I haven't tried it yet, that client side
calculation worries me with a potential document with 1M records -
the client would potentially be dealing with calculating the
intersection of multiple 100K element arrays. Is that a realistic
calculation?
Please tell me there is a better model for dealing with this type of
scenario - or that this use case is not well suited for Couchdb at
this time and I should move along.
Dan Woolley
profile: http://www.linkedin.com/in/danwoolley
company: http://woolleyrobertson.com
product: http://dwellicious.com
blog: http://tzetzefly.com
