Ask YC: Expressive languages for spatial nodes

MonetDB could be useful here.<p>MonetDB[1] is good for applications where you load the data once and then run lots of joins over binary relations, assuming your application works well with an RM/T[2] style schema.<p>In RM/T you have one entity relation that holds your entity ids and one property relation for each attribute. All your queries become many-way joins as they must traverse all the property relations. This is something you would never do with an ordinary RDBMS but is exactly what MonetDB's binary-association table scheme is designed to handle[3].<p>With MonetDB, you use SQL to define your schema in the familiar way. At the physical level MonetDB stores your data in an RM/T-style representation.<p>You may also want to see if Proximity[4][5]'s QGraph is suitable for your application. It uses MonetDB in it's implementation. The tutorial[6] includes an example of how to use it with Jython.<p>[1] <a href="http://monetdb.cwi.nl" rel="nofollow">http://monetdb.cwi.nl</a><p>[2] <a href="http://en.wikipedia.org/wiki/Relational_Model/Tasmania" rel="nofollow">http://en.wikipedia.org/wiki/Relational_Model/Tasmania</a><p>[3] <a href="http://monetdb.cwi.nl/projects/monetdb/Assets/monetdb_lecture.pdf" rel="nofollow">http://monetdb.cwi.nl/projects/monetdb/Assets/monetdb_lectur...</a><p>[4] <a href="http://kdl.cs.umass.edu/proximity" rel="nofollow">http://kdl.cs.umass.edu/proximity</a><p>[5] <a href="http://kdl.cs.umass.edu/proximity/about.html" rel="nofollow">http://kdl.cs.umass.edu/proximity/about.html</a><p>[6] <a href="http://kdl.cs.umass.edu/software/documentation/tutorial/ch06s08.html" rel="nofollow">http://kdl.cs.umass.edu/software/documentation/tutorial/ch06...</a>

If this is perf. critical and your data easily fits in RAM, then your problem does not sound exotic enough to resort to obscure or special-purpose languages. You should be able to whip up efficient data structures for this in python without much trouble. In fact depending on your data set size and query specifics, it may be acceptably fast in any of the listed languages (including Ruby!) assuming you have the right data structures. You really wont know until you have tried it. I would suggest you start in whatever language you know best.<p>(If it doesnt fit in RAM, I would consider it a much harder problem and I can imagine resorting to an at least partially SQL-based solution).<p>Btw, for a very similar problem, I used C++ w/STL for pure performance. It worked well. But I'm migrating it to python to shorten dev time. I figure in the same amt of time in python, I can try a variety of approaches and algo optimizations, for the same time it would take to do a single one in C++ w/STL. And for algos on complicated data sets, you want to really understand your data set. That means playing with it a lot, which is easier in python.<p>The data structures you use often have a much bigger impact on performance, than the language.

Prolog. For info see "P-99: Ninety-Nine Prolog Problems" at <a href="https://prof.ti.bfh.ch/hew1/informatik3/prolog/p-99/" rel="nofollow">https://prof.ti.bfh.ch/hew1/informatik3/prolog/p-99/</a><p>Scroll down to the section on Graphs (after problem 73).

AllegroGraph[1] sounds like it could be suitable as well.<p>[1] <a href="http://agraph.franz.com/allegrograph" rel="nofollow">http://agraph.franz.com/allegrograph</a>

Your advice is deeply appreciated, thank you everyone who has posted so far.