On Thu, 17 Apr 2003, Victor Danilchenko wrote:
> Unique (cost=230.58..255.24 rows=123 width=171) (actual
> time=238.20..293.21 rows=128 loops=1)
> -> Sort (cost=230.58..233.66 rows=1233 width=171) (actual
> time=238.19..241.07 rows=1233 loops=1)
> Sort Key: maker.id, maker.fullname, maker.contact,
> maker.phone, maker.service_no, maker.lastuser, maker.comments
> -> Merge Join (cost=0.00..167.28 rows=1233 width=171) (actual
> time=0.27..81.49 rows=1233 loops=1)
> Merge Cond: ("outer".id = "inner".maker)
> -> Index Scan using maker_pkey on maker
> (cost=0.00..52.00 rows=1000 width=164) (actual time=0.11..4.29
> rows=137 loops=1)
> -> Index Scan using makers on model (cost=0.00..94.28
> rows=1233 width=7) (actual time=0.04..27.34 rows=1233 loops=1)
> Total runtime: 295.30 msec
> (8 rows)
Hmm, well, for this version, it looks like most of the time is probably
going into the sort. I wonder if raising sort_mem would help this version
of the query (try a set sort_mem=8192; before running the query). This
isn't likely to get the time below like 160 msec though.
> Following a suggestion sent in private mail, I have created an
> index for model.maker column:
>
> # create index model_maker on model(maker);
>
> but that doesn't seem to have made an appreciable difference in
> performance -- it's only about .05 seconds more than the above number if
> I drop the index.
Yeah, it looks like it's already using an index, but I didn't see that
index in the list of indexes on the table in the original mail, wierd.
