From: | Amit Kapila <amit(dot)kapila16(at)gmail(dot)com> |
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To: | Robert Haas <robertmhaas(at)gmail(dot)com> |
Cc: | Rafia Sabih <rafia(dot)sabih(at)enterprisedb(dot)com>, Ashutosh Bapat <ashutosh(dot)bapat(at)enterprisedb(dot)com>, PostgreSQL Developers <pgsql-hackers(at)postgresql(dot)org> |
Subject: | Re: Effect of changing the value for PARALLEL_TUPLE_QUEUE_SIZE |
Date: | 2017-06-02 13:15:20 |
Message-ID: | CAA4eK1+mTd0qjH6zb6tsy9O54tshZBd2t1DFaN4wmb=Dmbn2VA@mail.gmail.com |
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On Fri, Jun 2, 2017 at 6:38 PM, Robert Haas <robertmhaas(at)gmail(dot)com> wrote:
> On Fri, Jun 2, 2017 at 9:01 AM, Amit Kapila <amit(dot)kapila16(at)gmail(dot)com> wrote:
>> Your reasoning sounds sensible to me. I think the other way to attack
>> this problem is that we can maintain some local queue in each of the
>> workers when the shared memory queue becomes full. Basically, we can
>> extend your "Faster processing at Gather node" patch [1] such that
>> instead of fixed sized local queue, we can extend it when the shm
>> queue become full. I think that way we can handle both the problems
>> (worker won't stall if shm queues are full and workers can do batched
>> writes in shm queue to avoid the shm queue communication overhead) in
>> a similar way.
>
> We still have to bound the amount of memory that we use for queueing
> data in some way.
>
Yeah, probably till work_mem (or some percentage of work_mem). If we
want to have some extendable solution then we might want to back it up
with some file, however, we might not need to go that far. I think we
can do some experiments to see how much additional memory is
sufficient to give us maximum benefit.
--
With Regards,
Amit Kapila.
EnterpriseDB: http://www.enterprisedb.com
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