On Wed, 17 Mar 2021 at 21:31, Tomas Vondra
<tomas.vondra@enterprisedb.com> wrote:
>
> I agree applying at least the [(a+b),c] stats is probably the right
> approach, as it means we're considering at least the available
> information about dependence between the columns.
>
> I think to improve this, we'll need to teach the code to use overlapping
> statistics, a bit like conditional probability. In this case we might do
> something like this:
>
> ndistinct((a+b),c) * (ndistinct((c+d)) / ndistinct(c))
Yes, I was thinking the same thing. That would be equivalent to
applying a multiplicative "correction" factor of
ndistinct(a,b,c,...) / ( ndistinct(a) * ndistinct(b) * ndistinct(c) * ... )
for each multivariate stat applicable to more than one
column/expression, regardless of whether those columns were already
covered by other multivariate stats. That might well simplify the
implementation, as well as probably produce better estimates.
> But that's clearly a matter for a future patch, and I'm sure there are
> cases where this will produce worse estimates.
Agreed.
> Anyway, I plan to go over the patches one more time, and start pushing
> them sometime early next week. I don't want to leave it until the very
> last moment in the CF.
+1. I think they're in good enough shape for that process to start.
Regards,
Dean
