Re: When does sequential performance matter in PG?

From: henk de wit
Subject: Re: When does sequential performance matter in PG?
Date: ,
Msg-id: COL104-W31005A94BBFDA5DA8B7ED0F5A10@phx.gbl
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In response to: Re: When does sequential performance matter in PG?  (Matthew Wakeling)
List: pgsql-performance

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When does sequential performance matter in PG?  (henk de wit, )
 Re: When does sequential performance matter in PG?  (Matthew Wakeling, )
  Re: When does sequential performance matter in PG?  (henk de wit, )
  Re: When does sequential performance matter in PG?  (Scott Carey, )
 Re: When does sequential performance matter in PG?  (Greg Smith, )

Hi,

> On Tue, 10 Mar 2009, henk de wit wrote:
> > Now I wonder if there is any situation in which
> > sequential IO performance comes into play. E.g. perhaps during a
> > tablescan on a non-fragmented table, or during a backup or restore?
>
> Yes, up to a point. That point is when a single CPU can no longer handle
> the sequential transfer rate. Yes, there are some parallel restore
> possibilities which will get you further. Generally it only takes a few
> discs to max out a single CPU though.

I see, but I take it you are only referring to a backup or a restore? It's of course unlikely (even highly undesirable) that multiple processes are doing a backup, but it doesn't seem unlikely that multiple queries are doing a table scan ;)

> Are you sure you're measuring the maximum IOPS, rather than measuring the
> IOPS capable in a single thread?

I'm pretty sure we're not testing the number of IOPS for a single thread, as we're testing with 1, 10 and 40 threads. There is a significant (2x) increase in the total number of IOPS when going from 1 to 10 threads, but no increase when going from 10 to 40 threads. You can read more details about the setup I used and the problems I ran into here: http://www.xtremesystems.org/forums/showthread.php?p=3707365

Henk


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From: Scott Carey
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Subject: Re: When does sequential performance matter in PG?
From: "Steve McLellan"
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Subject: Query performance over a large proportion of data