[postgis-users] Large geometry issue

[Gregory Williamson]

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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