Scott Marlowe wrote:
> On Tue, Jan 9, 2018 at 2:18 PM, Kumar, Virendra
> <Virendra.Kumar@guycarp.com> wrote:
> > Hello Gurus,
> >
> > I am struggling to tune a query which is doing join on top of aggregate for
> > around 3 million rows. The plan and SQL is attached to the email.
> >
> > Below is system Details:
> >
> > PGSQL version – 10.1
> >
> > OS – RHEL 3.10.0-693.5.2.el7.x86_64
> >
> > Binary – Dowloaded from postgres.org compiled and installed.
> >
> > Hardware – Virtual Machine with 8vCPU and 32GB of RAM, on XFS filesystem.
>
> I uploaded your query plan here: https://explain.depesz.com/s/14r6
>
> The most expensive part is the merge join at the end.
>
> Lines like this one: "Buffers: shared hit=676 read=306596, temp
> read=135840 written=135972"
>
> Tell me that your sorts etc are spilling to disk, so the first thing
> to try is upping work_mem a bit. Don't go crazy, as it can run your
> machine out of memory if you do. but doubling or tripling it and
> seeing the effect on the query performance is a good place to start.
>
> The good news is that most of your row estimates are about right, so
> the query planner is doing what it can to make the query fast, but I'm
> guessing if you get the work_mem high enough it will switch from a
> merge join to a hash_join or something more efficient for large
> numbers of rows.
Looking at the plan, I'd guess that the following index could be helpful:
CREATE INDEX ON ap.site_exposure(portfolio_id, peril_id, account_id);
Don't know how much it would buy you, but you could avoid the
sequential scan and the sort that way.
Yours,
Laurenz Albe