>> If you think about it, it's a bit ridiculous to look at the whole table
>> *just* to "estimate" ndistinct - if we go that far why dont we just
>> store the full distribution and be done with it?
>
> - the best you could do is to average the
> individual probabilities which gives ... well, 1/ndistinct.
>
That is certainly *not* the best you could do in every case. The mean
is only the best estimate in L2, which is definitely not the case
here.
Consider a table with 10K values, 9,990 of which are 1 and the rest of
which are 2, 3, ..., 10, versus a table that has the same 10 distinct
values evenly distributed. For a simple equality query, in the first
case, a bitmap scan might be best. In the second case, a sequential
scan would always be best.
This is precisely the point I was trying to make in my email: the loss
function is very complicated. Improving the ndistinct estimator could
significantly improve the estimates of ndistinct ( in the quadratic
loss sense ) while only marginally improving the plans.
-Nathan
