On Mon, Feb 25, 2013 at 10:22 PM, Greg Stark <stark@mit.edu> wrote:
> On Mon, Feb 25, 2013 at 8:26 PM, Robert Haas <robertmhaas@gmail.com> wrote:
>> On Sun, Feb 24, 2013 at 7:27 PM, Jim Nasby <jim@nasby.net> wrote:
>>> We actually do that in our application and have discovered that random
>>> sampling can end up significantly skewing your data.
>>
>> /me blinks.
>>
>> How so?
>
> Sampling is a pretty big area of statistics. There are dozens of
> sampling methods to deal with various problems that occur with
> different types of data distributions.
>
> One problem is if you have some very rare events then random sampling
> can produce odd results since those rare events will drop out entirely
> unless your sample is very large whereas less rare events are
> represented proportionally. There are sampling methods that ensure
> that x% of the rare events are included even if those rare events are
> less than x% of your total data set. One of those might be appropriate
> to use for profiling data when you're looking for rare slow queries
> amongst many faster queries.
I'll grant all that, but it still seems to me like x% of all queries
plus all queries running longer than x milliseconds would cover most
of the interesting cases.
--
Robert Haas
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company
