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.
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.
Certainly it's worth testing this in a low impact way like just keeping the
existing sample method and dialing up the sample sizes before you try anything
that would sacrifice the statistical validity of the more solid estimates.
--
greg
