Re: Extrapolating performance expectation

On Mon, May 18, 2009 at 6:47 AM, Kenneth Marshall <ktm(at)rice(dot)edu> wrote:

> On Sun, May 17, 2009 at 10:04:46PM -0600, Rob Sargent wrote:
> > Can one extrapolate future performance expectations for ever-growing
> tables
> > from a given (non-trivial) data set, and if so with what curve?
> Corollary:
> > what would one expect a performance curve to approximate in terms of
> query
> > execution time v. number of data rows (hardware, load staying constant).
> >
> > I have user and group information on system usage. I would like to be
> able
> > to do year-to-date counts per user given a single group id but in the
> data
> > for one business quarter the query is taking in between 10 and 60+
> seconds
> > depending on both on the size of the group and the group's total usage.
> > Groups typically have 10-100 users and consume 20K - 80K records in a 9M
> > record data set. Group id column is indexed, but it is not the primary
> > index. (Sad note: two pseudo groups account for 50 percent of the total
> > records IIRC (and will never be used for the usage-by-group query below)
> >
> > This is a single table query:
> >
> > select user_id, element_type, count(*)
> > from dataset
> > where group_id = N
> > group by user_id, element_type
> > order by user_id, element_type
> >
> > Is this the sort of situation which might benefit from increasing the
> number
> > of histogram bins (alter table alter column statistics (N>10))?
> >
> > Any and all pointers appreciated,
>
> Rob,
>
> What about partitioning the table based on the group. Then you could
> put the two pseudo groups in their own separate child table.
>
> Cheers,
> Ken
>

Ken,

Interesting point. I'm hoping the psuedo groups are on the verge of
extinction but will certainly investigate the possibility.

R.