The precise numbers are as follows:
Total Rows in the table: 45922415
if I use:
select
pos_lat,
pos_lon
from
feed_all.common_pos_messages
where
st_contains(ST_GeomFromText('POLYGON((0.00433541700872238
51.6875086022247,1.4450615615687 51.6875086022247,1.4450615615687
51.2018083846302,0.00433541700872238
51.2018083846302,0.00433541700872238 51.6875086022247))', 4326), pos_point)
I get back: 4493678 in 4.77 mins
Doing explain analyze I get:
"Bitmap Heap Scan on common_pos_messages (cost=82262.99..522647.01
rows=771600 width=8) (actual time=127735.424..198141.843 rows=4493678
loops=1)"
" Recheck Cond:
('0103000020E61000000100000005000000D29145A403C2713F0B23294800D84940E04E3ADFF81EF73F0B23294800D84940E04E3ADFF81EF73FCC056EDBD4994940D29145A403C2713FCC056EDBD4994940D29145A403C2713F0B23294800D84940'::geometry
&& pos_point)"
" Filter:
_st_contains('0103000020E61000000100000005000000D29145A403C2713F0B23294800D84940E04E3ADFF81EF73F0B23294800D84940E04E3ADFF81EF73FCC056EDBD4994940D29145A403C2713FCC056EDBD4994940D29145A403C2713F0B23294800D84940'::geometry,
pos_point)"
" -> Bitmap Index Scan on idx_pos (cost=0.00..82070.09 rows=2314801
width=0) (actual time=127732.000..127732.000 rows=4493679 loops=1)"
" Index Cond:
('0103000020E61000000100000005000000D29145A403C2713F0B23294800D84940E04E3ADFF81EF73F0B23294800D84940E04E3ADFF81EF73FCC056EDBD4994940D29145A403C2713FCC056EDBD4994940D29145A403C2713F0B23294800D84940'::geometry
&& pos_point)"
"Total runtime: 199206.428 ms"
Obviously the times and the rows do not seem to much with the actual
results. However after creating my own index based on lat/lon values
(integers) on the same table, executing this:
select
pos_lat,
pos_lon
from
feed_all.common_pos_messages
where
pos_lat between 30721085 and 31012505 and
pos_lon between 2601 and 867037
I get back 4493680 in 2.8 mins
Doing explain analyze I get:
"Bitmap Heap Scan on common_pos_messages (cost=161748.26..601144.64
rows=1686719 width=8) (actual time=10064.427..60738.808 rows=4493680
loops=1)"
" Recheck Cond: ((pos_lat >= 30721085) AND (pos_lat <= 31012505) AND
(pos_lon >= 2601) AND (pos_lon <= 867037))"
" -> Bitmap Index Scan on idx_lat_lon (cost=0.00..161326.58
rows=1686719 width=0) (actual time=10061.108..10061.108 rows=4493680
loops=1)"
" Index Cond: ((pos_lat >= 30721085) AND (pos_lat <= 31012505)
AND (pos_lon >= 2601) AND (pos_lon <= 867037))"
"Total runtime: 61850.720 ms"
The predictions are still "out" from the actual but the btree index
seems to behave better.
Any suggestions? Probably I need to bring up to date my statistics.
Kind Regards
Yiannis
On 30/03/2011 13:30, Sandro Santilli wrote:
On Wed, Mar 30, 2011 at 10:58:57AM +0100, Ioannis Anagnostopoulos wrote:
I am involved in a heavy database design initiative where the only kind
of geometries I am dealing with are points. I have recently hit a
50million rows long table with those points and my default gist index on
the points does not seem to be working very fast (if not at all to be
honest). I have started now thinking that probably for "points" an index
may not be the best option since in a 50million rows long table most of
the points are unique so the index may just duplicate the actual table,
of course I may be wrong and I may just missing a very important part of
the concept.
How many points from the 50M set does your tipical query hits ?
Does PostgreSQL selectivity estimator make a good guess about that ?
Use EXPLAIN ANALYZE<your query here> to see.
--strk;
() Free GIS& Flash consultant/developer
/\ http://strk.keybit.net/services.html
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