Greg Stark <gsstark@MIT.EDU> writes:
> The approach they take is to have a function which calculates an
> abstract "distance" between any two entries. There's an algorithm that
> they use to pick the split based on this distance function.
> If you abandoned "PickSplit" and instead exposed this distance
> function as the external API then the behaviour for multi-column
> indexes is clear. You calculate the distance along all the axes and
> calculate the diagonal distance.
Hmm ... the problem with that is the assumption that different opclasses
will compute similarly-scaled distances. If opclass A generates
distances in the range (0,1e6) while B generates in the range (0,1),
combining them with Euclidean distance won't work well at all. OTOH you
can't blindly normalize, because in some cases maybe the data is such
that a massive difference in distances is truly appropriate.
I'm also a bit leery of the assumption that every GiST application can
reduce its PickSplit logic to Euclidean distances.
regards, tom lane
