From: | Jim Jarvie <jim(at)talentstack(dot)to> |
---|---|
To: | Michael Lewis <mlewis(at)entrata(dot)com> |
Cc: | Pgsql Performance <pgsql-performance(at)lists(dot)postgresql(dot)org> |
Subject: | Re: CPU hogged by concurrent SELECT..FOR UPDATE SKIP LOCKED |
Date: | 2020-08-20 21:13:27 |
Message-ID: | e97e1113-afb2-54dc-93f8-75cf8f9559c3@talentstack.to |
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Lists: | pgsql-performance |
On 20-Aug.-2020 13:30, Michael Lewis wrote:
> Great to hear that some of the issues are now mitigated. Though, perhaps
> you actually require that ORDER BY if items are expected to be sitting in
> the queue quite some time because you have incoming queue items in a burst
> pattern and have to play catch up sometimes. If so, I highly suspect the
> index on q_id is becoming very bloated and reindex concurrently would help.
I managed to bypass the need for the sort by relying on the active feed
only sending the oldest items in for processing (it was always doing
that) but based on some of the earlier e-mails in this thread, it
prompted the revelation that my order by when processing was really
pretty pointless because I need more-or-less ordered rather than
strictly ordered and that was already happening due to how the process
list was being fed.
>
> I don't know if you might have missed my last message, and the suggestion
> from Laurenz to check pgstattuple.
I still need to look at that, but since I had made some progress, I got
pretty exited and have not got round to this yet.
> *
> https://www.2ndquadrant.com/en/blog/what-is-select-skip-locked-for-in-postgresql-9-5/
This does warn about the overhead, but I've also upgraded pg_top on my
system today and saw a useful additional data point that it displays -
the number of locks held by a process.
What I see happening is that when the select statements collide, they
are holding about 10-12 locks each and then begin to very slowly acquire
more locks every few seconds. One process will grow quicker than others
then reach the target (250) and start processing. Then another takes
the lead and so on until a critical mass is reached and then the
remaining all acquire their locks in a few seconds.
I still keep thinking there is some scaling type issue here in the
locking and possibly due to it being a partitioned table (due to that
tuple moved error).
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