From: | Robert Haas <robertmhaas(at)gmail(dot)com> |
---|---|
To: | Stephen Frost <sfrost(at)snowman(dot)net> |
Cc: | Jim Nasby <Jim(dot)Nasby(at)bluetreble(dot)com>, 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>, John Gorman <johngorman2(at)gmail(dot)com>, pgsql-hackers <pgsql-hackers(at)postgresql(dot)org> |
Subject: | Re: Parallel Seq Scan |
Date: | 2015-01-29 02:59:41 |
Message-ID: | CA+TgmoYh+dRcaHXbPTR40=vX0dguuWtDMK3=4bCG6nVOKDJvVg@mail.gmail.com |
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On Wed, Jan 28, 2015 at 8:27 PM, Stephen Frost <sfrost(at)snowman(dot)net> wrote:
> I feel like one of us is misunderstanding the numbers, which is probably
> in part because they're a bit piecemeal over email, but the seqscan
> speed in this case looks pretty close to dd performance for this
> particular test, when things are uncached. Cached numbers are
> different, but that's not what we're discussing here, I don't think.
>
> Don't get me wrong- I've definitely seen cases where we're CPU bound
> because of complex filters, etc, but that doesn't seem to be the case
> here.
To try to clarify a bit: What we've testing here is a function I wrote
called parallel_count(regclass), which counts all the visible tuples
in a named relation. That runs as fast as dd, and giving it extra
workers or prefetching or the ability to read the relation with
different I/O patterns never seems to speed anything up very much.
The story with parallel sequential scan itself may well be different,
since that has a lot more CPU overhead than a dumb-simple
tuple-counter.
--
Robert Haas
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company
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