Tom,
I cranked (join|from)_collapse_limit up to 50, then 500 just to exclude the limits completey, and attempted the query both times. The planner came up with an estimate close to the other estimates (1,944,276) and I stopped actual execution after some length of time.I had set session pg_default_statistics to 10000 and analyzed prior to the earlier runs to allow it to have the best stats it could. I've looked at it a little more closely, setting pg_default_statistics back to default of 100 and re-ran analyze on that database.
The value 'cahsee_ela' occurs 75,000 times in column analysis.iteration__student__test__year.test which totals 11M rows. It's ranked about 60 of 91 values in frequency.
By setting statistics=1000 on the column 'test' the MCV from pg_stats contains all 91 distinct values (there are no nulls) and there is no histogram_bounds value for the column. From MCV: cahsee_ela = 0.00658 which is accurate.
I think that should give the planner good info on the selectivity of the where clause. It appears from the var_eq_const function that it will use that exact value when found. It doesn' t seem to help the outcome though as it had good stats before. I just understand it a little better now - which is good.
Do you have any suggestions where to probe next?
I see some statistics hooks mentioned in some of the source codes but don't know how to take advantage of them or whether it would be of use.
I see some statistics hooks mentioned in some of the source codes but don't know how to take advantage of them or whether it would be of use.
I suppose the answer could eventually be we have to reorganize our queries?
Thanks,
Marty
On Thu, Mar 28, 2013 at 12:18 PM, Tom Lane <tgl@xxxxxxxxxxxxx> wrote:
Marty Frasier <m.frasier@xxxxxxxxxxxxx> writes:One quick thought is that it's probably worth cranking up
> We've been using postgreSQL for a few years. This is my first post here
> and first real dive into query plans.
join_collapse_limit and/or from_collapse_limit, since the number of
relations in the query is considerably more than the default values of
those limits. This will make planning take longer but possibly find
better plans. I'm not sure it will help a lot, since most of the
problem is evidently bad rowcount estimates, but it might help.
Also it seems like the major rowcount failing is in the estimate for the
t12 subquery. I can't tell why that particular combination of WHERE
clauses is giving it such a hard time --- is there something odd about
the distribution of 'cahsee_ela' tests? Why is that particular subquery
grouped over school/student when all the others are grouped over just
student?
regards, tom lane
