A common approach (at least common to me) is to divide your hash bit string in several chunks and query on these chunks for an exact match. This is a “pre-filter” step. You then can perform a bitwise hamming distance computation on the returned results which should be only a smaller subset of your overall … Read more
NEW ANSWER Given the recent update, you could try A* with the Hamming distance as a heuristic. It’s an admissible heuristic since it’s not going to overestimate the distance OLD ANSWER You can modify the dynamic-program used to compute the Levenshtein distance to obtain the sequence of operations. EDIT: If there are a constant number … Read more
It appears that storing the data in a BINARY column is an approach bound to perform poorly. The only fast way to get decent performance is to split the content of the BINARY column in multiple BIGINT columns, each containing an 8-byte substring of the original data. In my case (32 bytes) this would mean … Read more
Question: What do we know about the Hamming distance d(x,y)? Answer: It is non-negative: d(x,y) ≥ 0 It is only zero for identical inputs: d(x,y) = 0 ⇔ x = y It is symmetric: d(x,y) = d(y,x) It obeys the triangle inequality, d(x,z) ≤ d(x,y) + d(y,z) Question: Why do we care? Answer: Because it … Read more