Re: Parallel Seq Scan

From: Stephen Frost <sfrost(at)snowman(dot)net>
To: Robert Haas <robertmhaas(at)gmail(dot)com>
Cc: Tom Lane <tgl(at)sss(dot)pgh(dot)pa(dot)us>, Heikki Linnakangas <hlinnakangas(at)vmware(dot)com>, Amit Kapila <amit(dot)kapila16(at)gmail(dot)com>, Jim Nasby <Jim(dot)Nasby(at)bluetreble(dot)com>, John Gorman <johngorman2(at)gmail(dot)com>, pgsql-hackers <pgsql-hackers(at)postgresql(dot)org>
Subject: Re: Parallel Seq Scan
Date: 2015-01-28 15:56:48
Message-ID: 20150128155648.GV3854@tamriel.snowman.net
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* Robert Haas (robertmhaas(at)gmail(dot)com) wrote:
> On Wed, Jan 28, 2015 at 10:40 AM, Tom Lane <tgl(at)sss(dot)pgh(dot)pa(dot)us> wrote:
> > I thought the proposal to chunk on the basis of "each worker processes
> > one 1GB-sized segment" should work all right. The kernel should see that
> > as sequential reads of different files, issued by different processes;
> > and if it can't figure out how to process that efficiently then it's a
> > very sad excuse for a kernel.

Agreed.

> I agree. But there's only value in doing something like that if we
> have evidence that it improves anything. Such evidence is presently a
> bit thin on the ground.

You need an i/o subsystem that's fast enough to keep a single CPU busy,
otherwise (as you mentioned elsewhere), you're just going to be i/o
bound and having more processes isn't going to help (and could hurt).

Such i/o systems do exist, but a single RAID5 group over spinning rust
with a simple filter isn't going to cut it with a modern CPU- we're just
too darn efficient to end up i/o bound in that case. A more complex
filter might be able to change it over to being more CPU bound than i/o
bound and produce the performance improvments you're looking for.

The caveat to this is if you have multiple i/o *channels* (which it
looks like you don't in this case) where you can parallelize across
those channels by having multiple processes involved. We only support
multiple i/o channels today with tablespaces and we can't span tables
across tablespaces. That's a problem when working with large data sets,
but I'm hopeful that this work will eventually lead to a parallelized
Append node that operates against a partitioned/inheirited table to work
across multiple tablespaces.

Thanks,

Stephen

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