On Thu, 2006-01-05 at 00:33 -0500, Greg Stark wrote:
> Simon Riggs <simon@2ndquadrant.com> writes:
>
> > The approach I suggested uses the existing technique for selecting
> > random blocks, then either an exhaustive check on all of the rows in a
> > block or the existing random row approach, depending upon available
> > memory. We need to check all of the rows in a reasonable sample of
> > blocks otherwise we might miss clusters of rows in large tables - which
> > is the source of the problems identified.
> >
> > The other reason was to increase the sample size, which is a win in any
> > form of statistics.
>
> Only if your sample is random and independent. The existing mechanism tries
> fairly hard to ensure that every record has an equal chance of being selected.
> If you read the entire block and not appropriate samples then you'll introduce
> systematic sampling errors. For example, if you read an entire block you'll be
> biasing towards smaller records.
Yes, I discussed that, following Brutlag & Richardson [2000]. The bottom
line is if there is no clustering, block sampling is random, which is
good; if there is clustering, then you spot it, which is good.
> I think it would be useful to have a knob to increase the sample size
> separately from the knob for the amount of data retained in the statistics
> tables. Though I think you'll be disappointed and find you have to read an
> unreasonably large sample out of the table before you get more useful distinct
> estimates.
OK, I'll look at doing that.
Best Regards, Simon Riggs
