On Wed, Apr 14, 2010 at 08:46:13AM -0700, Richard Broersma wrote:
> On Wed, Apr 14, 2010 at 7:54 AM, Louis-David Mitterrand
> <vindex+lists-pgsql-sql@apartia.org> wrote:
>
> > Now, I'd like to make a graph of average prices per week, per
> > id_product. As some prices don't vary much, distribution would not be
> > ideal if I simply 'group by extract(week from p.modified)'.
>
> I created a view for a similar problem that I had. Only I was
> calculating the counts per day. this query could be crafted to work
> for you.
>
> CREATE OR REPLACE VIEW opendiscrepencydailycounts AS
> WITH opendays(day) AS (
> SELECT gs.day::date AS day
> FROM generate_series((( SELECT
> min(discrepencylist.discstartdt) AS min
> FROM discrepencylist))::timestamp without time
> zone, 'now'::text::date::timestamp without time zone, '1
> day'::interval) gs(day)
> )
> SELECT opendays.day, ds.resolvingparty, count(opendays.day) AS
> opendiscrepancies
> FROM discrepencylist ds, opendays
> WHERE opendays.day >= ds.discstartdt AND opendays.day <=
> LEAST('now'::text::date, ds.resolutiondate)
> GROUP BY opendays.day, ds.resolvingparty
> ORDER BY opendays.day, ds.resolvingparty;
You confirm my modus operandi. I tried the following which seems to give
me optimal price distribution:
select w.week,count( p.id_price) from (select generate_series(min(p.created_on),max(p.modified_on),'1 week') as
weekfrom price p) as w join price p on (p.created_on < w.week + '7 days' and p.modified_on > w.week + '7 days')
groupby w.week order by w.week
week | count
------------------------+--------2010-02-10 15:32:18+01 | 1253692010-02-17 15:32:18+01 | 1268822010-02-24 15:32:18+01 |
1283072010-03-0315:32:18+01 | 1267422010-03-10 15:32:18+01 | 1335962010-03-17 15:32:18+01 | 1490192010-03-24
15:32:18+01| 1499082010-03-31 15:32:18+02 | 147617
The rest should be easy from there!
Thanks for your input,
