To: Andrew Lazarus <andrew@pillette.com>
cc: Mark Kirkwood <markir@paradise.net.nz>, pgsql-performance@postgresql.org
Subject: Re: index structure for 114-dimension vector 
In-reply-to: <3810566934.20070420172820@pillette.com> 
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	<01db01c781e4$c700bf30$2e00a8c0@tridecap.com>
	<b42b73150704181716wb1600aey8761b6793d3fd702@mail.gmail.com>
	<1217328326.20070420120729@pillette.com>
	<1177105284.28383.31.camel@dogma.v10.wvs>
	<46294FEB.9010909@paradise.net.nz>
	<3810566934.20070420172820@pillette.com>
Comments: In-reply-to Andrew Lazarus <andrew@pillette.com>
	message dated "Fri, 20 Apr 2007 17:28:20 -0700"
Date: Fri, 20 Apr 2007 20:44:31 -0400
Message-ID: <3357.1177116271@sss.pgh.pa.us>
From: Tom Lane <tgl@sss.pgh.pa.us>

Andrew Lazarus <andrew@pillette.com> writes:
> Because I know the 25 closest are going to be fairly close in each
> coordinate, I did try a multicolumn index on the last 6 columns and
> used a +/- 0.1 or 0.2 tolerance on each. (The 25 best are very probably inside
> that hypercube on the distribution of data in question.)

> This hypercube tended to have 10-20K records, and took at least 4
> seconds to retrieve. I was a little surprised by how long that took.
> So I'm wondering if my data representation is off the wall.

A multicolumn btree index isn't going to be helpful at all.  Jeff's idea
of using six single-column indexes with the above query might work,
though.

			regards, tom lane