The general shape of this problem is
"large complex geometry potentially (based on index filter) contains a
large number of smaller geometries, figure out which ones are actually
contained"
http://geotips.blogspot.com/2007/06/performance-and-contains.html
There's no fast way to do this in PostGIS right now. Contains() will be
maximally fast for the many-points-in-polygon, but still hardly optimal,
since our naive p-i-p is still O(N).
We are working on speeding this up for a client right now, and the
delivery will happen relatively soon, but the drawback is that it will
require using the JTS bindings instead of the GEOS ones, since the
solution required new JTS work that is not going to be in GEOS for some
time. The solution will build an internally-indexed version of the big
complex shape (the individual edges will have index entries) and then
keep that version around for re-use by each test of the
small-shape-vs-large-shape combinations.
I would suggest waiting for that release to hit CVS, see if it does what
you want, then if you are averse to using the JTS bindings buck up for
the GEOS port.
P.
(It's a shame, but large sparse objects like multi-* don't use the
over-all feauture-based index well, because the bbox covers the whole
complex of sub-shapes. If lots of your objects are sparse multis,
breaking them into simple polygons with a key relationship will help
things quite a bit, by reducing improper index hits.)
Burgholzer,Robert wrote:
Gregory,
I have been using within() to do the same, and have found many of the same
performance issues. There are a couple of points that I think are useful:
1) the "&&" queries are generally fast, especially when using a GIST index,
because it is a very effective index - nothing comparable exists (to my knowledge) for distance
and within().
2) In my investigations it seems that MULTIPOLYGONS may be the culprit in slowing things down. That is, it is
not the complexity of the geometry per se, but rather, the number of isolated geometries within a given shape
column, as indicated by the command "select my_id, numGeometries(the_geom) from my_table group by
my_id;". I believe that if you are operating on shapes where the "numGeometries" command
returns a value of 1, they will perform within() queries very quickly, however, when that number becomes
considerably greater than 1, the queries bog down. This occurs often when you have shoreline type geometries
that are non-continuous, or geometries that have a number of "holes" in them.
I think that if you can insure that your geometries are single continuous
entities that you can improve performance. I have thought to also investigate
the code that underlies the within() command, but have been unsuccessful in
understanding the code. Some optimization here would be very worthwhile. If
you were to be able to progress in this area, I would be interested in seeing
what you can find, and perhaps contributing to the investigation.
I would be interested to know if anyone who knows more about this topic thinks
my conclusions are correct.
Hope this is at least somewhat helpful,
r.b.
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Gregory
Williamson
Sent: Thursday, July 05, 2007 8:06 AM
To: PostGIS Users Discussion; [email protected]
Subject: RE: [postgis-users] Large geometry issue
FWIW, this is PostgreSQL 8.2.4, POSTGIS="1.2.1" GEOS="3.0.0rc4-CAPI-1.3.3"
USE_STATS
on a linux box with 8 gigs o' RAM ...
GSW
-----Original Message-----
From: [EMAIL PROTECTED] on behalf of Gregory Williamson
Sent: Thu 7/5/2007 3:09 AM
To: [email protected]
Subject: [postgis-users] Large geometry issue
Dear peoples,
I have a problem with a query that uses an absurdly large geometry (2530 in a
single polygon). This is srid -1 (part of a large test of postgres vs some
other database product). Everything has been vacuumed and analyzed.
The initial search to find candidates in a target table is quite fast:
catest=# select count(*) from wtm_sub_cell w, order_line_item x WHERE x.bbox &&
w.geometry AND x.id_as_int = 114672;
count
-------
13168
(1 row)
Time: 9.472 ms
Trying to get the list narrowed to geometries that are completely contained by
the requested shape is slow:
catest=# select count(*) from wtm_sub_cell w, order_line_item x WHERE x.bbox &&
w.geometry AND distance(x.geometry,w.geometry) = 0 and x.id_as_int = 114672;
count
-------
1112
(1 row)
Time: 69277.780 ms
So I have two questions:
a) anything better to use than "distance(x,y) = 0) ? I tried st_within -- it
is about the same speed but returns no polys, which is strange to me, but I also
haven't looked at these in detail yet. For example:
catest=# select count(*) from wtm_sub_cell w, order_line_item x WHERE x.bbox &&
w.geometry AND st_within(x.geometry,w.geometry) and x.id_as_int = 114672;
count
-------
0
(1 row)
Time: 1173.185 ms
(same results with st_within(w.geometry,x.geometry):
catest=# select count(*) from wtm_sub_cell w, order_line_item x WHERE x.bbox &&
w.geometry AND st_within(w.geometry,x.geometry) and x.id_as_int = 114672;
count
-------
0
(1 row)
b) anything I can do to speed things up ? I have tried boosting work mem to
16 megs (from 1) and it made no apparent difference.
I have a self contained test case that shows the same behavior -- the one large
poly and all the candidates in another table. Apologies for the size; hopefully
it's not been mangled in the transfers.
Explain analyze of the sample (the sequential is sensible since there is only
one row in the table):
catest=# explain analyze select count(*) from wsc_candidates w, oli_req x WHERE w.geometry
&& x.bbox AND distance(w.geometry,x.oli_req_geom) > 0 AND x.oli_req_id = 114672;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=20.28..20.29 rows=1 width=0) (actual
time=77232.858..77232.859 rows=1 loops=1)
-> Nested Loop (cost=0.00..9.30 rows=4389 width=0) (actual
time=6.389..77221.506 rows=12056 loops=1)
Join Filter: (distance(w.geometry, x.oli_req_geom) > 0::double
precision)
-> Seq Scan on oli_req x (cost=0.00..1.01 rows=1 width=40602)
(actual time=0.007..0.009 rows=1 loops=1)
Filter: (oli_req_id = 114672)
-> Index Scan using wsc_c_spatial_ndx on wsc_candidates w
(cost=0.00..8.27 rows=1 width=109) (actual time=0.022..25.991 rows=13168 loops=1)
Index Cond: (w.geometry && x.bbox)
Filter: (w.geometry && x.bbox)
Total runtime: 77232.901 ms
(9 rows)
Time: 77233.773 ms
And for the real thing:
catest=# explain analyze select count(*) from wtm_sub_cell w, order_line_item x WHERE
w.geometry && x.bbox AND distance(w.geometry,x.geometry) = 0 AND x.id_as_int =
114672;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=141.83..141.84 rows=1 width=0) (actual
time=77457.587..77457.588 rows=1 loops=1)
-> Nested Loop (cost=5.99..141.83 rows=1 width=0) (actual
time=15.682..77456.541 rows=1112 loops=1)
Join Filter: (distance(w.geometry, x.geometry) = 0::double precision)
-> Index Scan using oli_id_ndx on order_line_item x (cost=0.00..8.30
rows=1 width=383) (actual time=0.012..0.018 rows=1 loops=1)
Index Cond: (id_as_int = 114672)
-> Bitmap Heap Scan on wtm_sub_cell w (cost=5.99..132.97 rows=32
width=109) (actual time=2.988..21.796 rows=13168 loops=1)
Filter: (w.geometry && x.bbox)
-> Bitmap Index Scan on wsc_geom_idx1 (cost=0.00..5.98 rows=32
width=0) (actual time=2.828..2.828 rows=13168 loops=1)
Index Cond: (w.geometry && x.bbox)
Total runtime: 77457.633 ms
(10 rows)
Time: 77458.458 ms
The tables involved by size:
catest=# select count(*) from wsc_candidates;
count
-------
13168
(1 row)
Time: 2.586 ms
catest=# select count(*) from oli_req;
count
-------
1
(1 row)
Time: 0.193 ms
catest=# select count(*) from wtm_sub_cell;
count
---------
6399928
(1 row)
Time: 1776.508 ms
catest=# select count(*) from order_line_item;
count
--------
395921
(1 row)
Time: 176.083 ms
Many thanks for your time and bandwidth!
Greg Williamson
Senior DBA
GlobeXplorer LLC, a DigitalGlobe company
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