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
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TIP 9: In versions below 8.0, the planner will ignore your desire to
choose an index scan if your joining column's datatypes do not
match
