On Tue, Mar 23, 2021 at 03:00:38PM +1100, Paul McGarry wrote: > I have a query where Postgresql (11.9 at the moment) is making an odd plan > choice, choosing to use index scans which require filtering out millions of > rows, rather than "just" doing an aggregate over the rows the where clause > targets which is much faster. > AFAICT it isn't a statistics problem, at least increasing the stats target > and analyzing the table doesn't seem to fix the problem.
> explain analyze select min(risk_id),max(risk_id) from risk where > time>='2020-01-20 15:00:07+00' and time < '2020-01-21 15:00:08+00';
I'm guessing the time and ID columns are highly correlated...
So the planner thinks it can get the smallest ID by scanning the ID index, but then ends up rejecting the first 161e6 rows for which the time is too low, and fails the >= condition.
And thinks it can get the greatest ID by backward scanning the ID idx, but ends up rejecting/filtering the first 41e6 rows, for which the time is too high, failing the < condition.
Yes, the columns are highly correlated, but that alone doesn't seem like it should be sufficient criteria to choose this plan. Ie the selection criteria (1 day of data about a year ago) has a year+ worth of data after it and probably a decade of data before it, so anything walking a correlated index from top or bottom is going to have to walk past a lot of data before it gets to data that fits the criteria.
One solution seems to be to create an index on (i,j), but I don't know if there's a better way.
Adding the count() stops the planner considering the option so that will work for now. My colleague has pointed out that we had the same issue in November and I came up with the count() workaround then too, but somehow seem to have forgotten it in the meantime and reinvented it today. I wonder if I posted to pgsql-performance then too.....
Maybe time for me to read the PG12 release notes....
