Here is, how you can receive all one billion rows with
pieces of 2048 rows. This changes PostgreSQL and ODBC behaviour:
Change ODBC data source configuration in the following way:
Fetch = 2048
UseDeclareFetch = 1
It does not create core dumps with 32 bit computers with billions of rows!
This is a b
Here is my opinion.
I hope this helps.
Maybe there is no one good formula:
On boolean type, there are at most 3 distinct values.
There is an upper bound for fornames in one country.
There is an upper bound for last names in one country.
There is a fixed number of states and postal codes in one coun
Andrew,
> The math in the paper does not seem to look at very low levels of q (=
> sample to pop ratio).
Yes, I think that's the failing. Mind you, I did more testing and found out
that for D/N ratios of 0.1 to 0.3, the formula only works within 5x accuracy
(which I would consider acceptable)
Folks,
> I wonder if this paper has anything that might help:
> http://www.stat.washington.edu/www/research/reports/1999/tr355.ps - if I
> were more of a statistician I might be able to answer :-)
Actually, that paper looks *really* promising. Does anyone here have enough
math to solve for D(s
On Sat, Apr 23, 2005 at 01:00:40AM -0400, Tom Lane wrote:
> "Jim C. Nasby" <[EMAIL PROTECTED]> writes:
> >> Feel free to propose better cost equations.
>
> > Where would I look in code to see what's used now?
>
> All the gold is hidden in src/backend/optimizer/path/costsize.c.
>
>
Hi Tom,
Thanks! That's exactly what it was. There was a discrepancy in the
data that turned this into an endless loop. Everything has been
running smoothly since I made a change.
Thanks so much,
Richard
On Apr 23, 2005, at 12:50 PM, Tom Lane wrote:
Richard Plotkin <[EMAIL PROTECTED]> writes:
Josh Berkus writes:
> Tom, how does our heuristic sampling work? Is it pure random sampling, or
> page sampling?
Manfred probably remembers better than I do, but I think the idea is
to approximate pure random sampling as best we can without actually
examining every page of the table.