From: | Andrew Dunstan <andrew(at)dunslane(dot)net> |
---|---|
To: | Tom Lane <tgl(at)sss(dot)pgh(dot)pa(dot)us> |
Cc: | Thomas Kellerer <spam_eater(at)gmx(dot)net>, pgsql-performance(at)postgresql(dot)org |
Subject: | Re: query optimization |
Date: | 2012-04-26 20:27:38 |
Message-ID: | 4F99AFBA.4090605@dunslane.net |
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Lists: | pgsql-performance |
On 04/26/2012 04:08 PM, Tom Lane wrote:
> Thomas Kellerer<spam_eater(at)gmx(dot)net> writes:
>> Tom Lane wrote on 26.04.2012 21:17:
>>> Um ... did you analyze all the tables, or just some of them? I get
>>> sub-millisecond runtimes if all four tables have been analyzed, but it
>>> does seem to pick lousy plans if, say, only a and b have been analyzed.
>> Here it's similar to Richard's experience:
>> Before analyzing the four tables, the first statement yields this plan:
>> [ merge joins ]
>> This continues to stay the plan for about 10-15 repetitions, then it turns to this plan
>> [ hash joins ]
> Hmm. I see it liking the merge-join plan (with minor variations) with
> or without analyze data, but if just some of the tables have been
> analyzed, it goes for the hash plan which is a good deal slower. The
> cost estimates aren't that far apart though. In any case, the only
> reason the merge join is so fast is that the data is perfectly ordered
> in each table; on a less contrived example, it could well be a lot
> slower.
>
It's not so terribly contrived, is it? It's common enough to have tables
which are append-only and to join them by something that corresponds to
the append order (serial field, timestamp etc.)
cheers
andrew
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