From: Mischa Sandberg <mischa.sandberg@telus.net>
To: Andrew Dunstan <andrew@dunslane.net>
Cc: josh@agliodbs.com, pgsql-perform <pgsql-performance@postgresql.org>
Cc: pgsql-hackers@postgresql.org
Subject: Re: [HACKERS] Bad n_distinct estimation; hacks suggested?
Date: Tue, 26 Apr 2005 22:38:04 -0700
Message-ID: <1114580284.426f253cc0087@webmail.telus.net> (raw)
In-Reply-To: <426D565E.8040400@dunslane.net>
References: <200504191209.05181.josh@agliodbs.com>
<19276.1114442580@sss.pgh.pa.us>
<1114454941.21529.245.camel@localhost.localdomain>
<200504251213.18565.josh@agliodbs.com>
<426D565E.8040400@dunslane.net>
Quoting Andrew Dunstan <andrew@dunslane.net>:
> After some more experimentation, I'm wondering about some sort of
> adaptive algorithm, a bit along the lines suggested by Marko
Ristola, but limited to 2 rounds.
>
> The idea would be that we take a sample (either of fixed size, or
> some small proportion of the table) , see how well it fits a larger
sample
> > (say a few times the size of the first sample), and then adjust
the > formula accordingly to project from the larger sample the
estimate for the full population. Math not worked out yet - I think we
want to ensure that the result remains bounded by [d,N].
Perhaps I can save you some time (yes, I have a degree in Math). If I
understand correctly, you're trying extrapolate from the correlation
between a tiny sample and a larger sample. Introducing the tiny sample
into any decision can only produce a less accurate result than just
taking the larger sample on its own; GIGO. Whether they are consistent
with one another has no relationship to whether the larger sample
correlates with the whole population. You can think of the tiny sample
like "anecdotal" evidence for wonderdrugs.
--
"Dreams come true, not free." -- S.Sondheim, ITW
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To: pgsql-performance@postgresql.org
Cc: mischa.sandberg@telus.net, andrew@dunslane.net, pgsql-hackers@postgresql.org
Subject: Re: [HACKERS] Bad n_distinct estimation; hacks suggested?
In-Reply-To: <1114580284.426f253cc0087@webmail.telus.net>
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