Hi Steve,
if the only problem is that the size of your zipcode squared (more
accurately, n * (n-1) if you order the pairs of persons, assuming that
distance is symmetric) is too large, it might help to split the zipcode
into buckets by some hash function and partition the all-pairs
computations over buckets.
That is, if you have 10 buckets containing the users of a zipcode and
its neighboring zipcodes, first compute all pairs of persons within
bucket 1, then all pairs of persons in bucket 1 and 2, then 1-3, 1-4
etc. up to 10-10. Obviously, you don't need to do 4-1 if you've already
done 1-4 (symmetry, see above), so you'll end up doing n * (n+1) pairs
of buckets (55 in this case).
Basically, this means creating artificial, smaller zipcodes.
Apart from that, I'd like to point out that there's been a lot of
research on nearest-neighbor search; perhaps some state-of-the-art
algorithm will be applicable to your problem.
http://en.wikipedia.org/wiki/Nearest_neighbor_search
Hope this helps,
Christoph
On 14.08.2013 19:06, Steve Lewis wrote:
I have the problem of performing a operation of a data set on itself.
Assume, for example, that I have a list of people and their
addresses and for each person I want the ten closest members of the set.
(this is not the problem but illustrated critical aspects). I know that
the ten closest people will be in the same zipcode or a neighboring zip
code. This means unless the database is very large I can have the mapper
send every person out with keys representing their zipcode and also
keys representing the neighboring zip codes. In the reducer I can keep
all people in memory and compute distances between them (assume the
distance computation is slightly expensive).
The problem is that this approach will not scale - eventually the
number of people assigned to a zip code will exceed memory. In the
current problem the number of "people" is about 100 million and doubling
every 6 months. The size of a "zipcode" requires keeping about 100,000
items in memory - doable today but marginal in terms of future growth.
Are there other ways to solve the problem. I considered keeping a
random subset, finding the closest in that subset and then repeating
with different random subsets. The solution of midifying the splitter to
generate all pairs
https://github.com/adamjshook/mapreducepatterns/blob/master/MRDP/src/main/java/mrdp/ch5/CartesianProduct.java
will
not work for a dataset with 100 million items
Any bright ideas?
--
Christoph Schmitz
Software-Architekt
Targeting Core Product
1&1 Internet AG | Brauerstraße 50 | 76135 Karlsruhe | Germany
Phone: +49 721 91374-6733
E-Mail: christoph.schm...@1und1.de | Web: www.1und1.de
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Member of United Internet
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