Joel Reymont <joe...@gmail.com> writes:
> I'm trying to optimize the following query that performs KL Divergence [1].
> As you can see the distance function operates on vectors of 150 floats.
> CREATE OR REPLACE FUNCTION docs_within_distance(vec topics, threshold float)
> RETURNS TABLE(id doc_id, distance float) AS $$
> BEGIN
> RETURN QUERY
> SELECT *
> FROM (SELECT doc_id, (SELECT sum(vec[i] * ln(vec[i] / topics[i]))
> FROM generate_subscripts(topics, 1) AS i
> WHERE topics[i] > 0) AS distance
> FROM docs) AS tab
> WHERE tab.distance <= threshold;
> END;
> $$ LANGUAGE plpgsql;
Yikes. That sub-select is a mighty expensive way to compute the scalar
product. Push it into a sub-function that takes the two arrays and
iterates over them with a for-loop. For another couple orders of
magnitude, convert the sub-function to C code. (I don't think you need
a whole data type, just a function that does the scalar product.)
regards, tom lane
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