On Dec 6, 2007 6:28 PM, Decibel! <decibel@decibel.org> wrote:
> FWIW, I've never seen anything but a performance increase or no change
> when going from 10 to 100. In most cases there's a noticeable
> improvement since it's common to have over 100k rows in a table, and
> there's just no way to capture any kind of a real picture of that with
> only 10 buckets.
I'd be more inclined to try to do something that was at least somewhat
data aware.
The "interesting theory" that I'd like to verify if I had a chance
would be to run through a by-column tuning using a set of heuristics.
My "first order approximation" would be:
- If a column defines a unique key, then we know there will be no
clustering of values, so no need to increase the count...
- If a column contains a datestamp, then the distribution of values is
likely to be temporal, so no need to increase the count...
- If a column has a highly constricted set of values (e.g. - boolean),
then we might *decrease* the count.
- We might run a query that runs across the table, looking at
frequencies of values, and if it finds a lot of repeated values, we'd
increase the count.
That's a bit "hand-wavy," but that could lead to both increases and
decreases in the histogram sizes. Given that, we can expect the
overall stat sizes to not forcibly need to grow *enormously*, because
we can hope for there to be cases of shrinkage.
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