Andrew Lazarus wrote:
Because I know the 25 closest are going to be fairly close in each coordinate, I did try a multicolumn index on the last 6 columns and used a +/- 0.1 or 0.2 tolerance on each. (The 25 best are very probably inside that hypercube on the distribution of data in question.) This hypercube tended to have 10-20K records, and took at least 4 seconds to retrieve. I was a little surprised by how long that took. So I'm wondering if my data representation is off the wall. I should mention I also tried a cube index using gist on all 114 elements, but CREATE INDEX hadn't finished in 36 hours, when I killed it, and I wasn't in retrospect sure an index that took something like 6GB by itself would be helpful on a 2GB of RAM box. MK> I don't think that will work for the vector norm i.e: MK> |x - y| = sqrt(sum over j ((x[j] - y[j])^2))
Sorry, in that case it probably *is* worth trying out 6 single column indexes and seeing if they get bitmap and'ed together...
Mark
