With the help of some of this list, I was able to successfully set up
and benchmark a cube-based replacement for geo_distance() calculations.
On a development box, the cube-based variations benchmarked consistently
running in about 1/3 of the time of the gel_distance() equivalents.
After setting up the same columns and indexes on a production
database, it's a different story. All the cube operations show
themselves to be about the same as, or noticeably slower than, the same
operations done with geo_distance().
I've stared at the EXPLAIN ANALYZE output as much I can to figure what's
gone. Could you help?
Here's the plan on the production server, which seems too slow. Below is the plan I get in
on the development server, which is much faster.
I tried "set enable_nestloop = off", which did change the plan, but the performance.
The production DB has much more data in it, but I still expected comparable results relative
to using geo_distance() calculations.
The production db gets a "VACUUM ANALYZE" every couple of hours now.
Thanks!
Mark
########
Sort (cost=6617.03..6617.10 rows=27 width=32) (actual time=2482.915..2487.008 rows=1375 loops=1)
Sort Key: (cube_distance($0, zipcodes.earth_coords) / 1609.344::double precision)
InitPlan
-> Index Scan using zipcodes_pkey on zipcodes (cost=0.00..3.01 rows=1 width=32) (actual time=0.034..0.038 rows=1 loops=1)
Index Cond: ((zipcode)::text = '90210'::text)
-> Index Scan using zipcodes_pkey on zipcodes (cost=0.00..3.01 rows=1 width=32) (actual time=0.435..0.438 rows=1 loops=1)
Index Cond: ((zipcode)::text = '90210'::text)
-> Nested Loop (cost=538.82..6610.36 rows=27 width=32) (actual time=44.660..2476.919 rows=1375 loops=1)
-> Nested Loop (cost=2.15..572.14 rows=9 width=36) (actual time=4.877..39.037 rows=136 loops=1)
-> Bitmap Heap Scan on zipcodes (cost=2.15..150.05 rows=42 width=41) (actual time=3.749..4.951 rows=240 loops=1)
Recheck Cond: (cube_enlarge(($1)::cube, 16093.4357308298::double precision, 3) @ earth_coords)
-> Bitmap Index Scan on zip_earth_coords_idx (cost=0.00..2.15 rows=42 width=0) (actual time=3.658..3.658 rows=240 loops=1)
Index Cond: (cube_enlarge(($1)::cube, 16093.4357308298::double precision, 3) @ earth_coords)
-> Index Scan using shelters_postal_code_for_joining_idx on shelters (cost=0.00..10.02 rows=2 width=12) (actual time=0.079..0.133 rows=1 loops=240)
Index Cond: ((shelters.postal_code_for_joining)::text = ("outer".zipcode)::text)
-> Bitmap Heap Scan on pets (cost=536.67..670.47 rows=34 width=4) (actual time=16.844..17.830 rows=10 loops=136)
Recheck Cond: ((pets.shelter_id = "outer".shelter_id) AND ((pets.pet_state)::text = 'available'::text))
Filter: (species_id = 1) Sort (cost=7004.53..7004.62 rows=39 width=32) (actual time=54.635..55.450 rows=475 loops=1)
-> BitmapAnd (cost=536.67..536.67 rows=34 width=0) (actual time=16.621..16.621 rows=0 loops=136)
-> Bitmap Index Scan on pets_shelter_id_idx (cost=0.00..3.92 rows=263 width=0) (actual time=0.184..0.184 rows=132 loops=136)
Index Cond: (pets.shelter_id = "outer".shelter_id)
-> Bitmap Index Scan on pets_pet_state_idx (cost=0.00..532.50 rows=39571 width=0) (actual time=26.922..26.922 rows=40390 loops=82)
Index Cond: ((pet_state)::text = 'available'::text)
Total runtime: 2492.852 ms
########### Faster plan in development:
Sort (cost=7004.53..7004.62 rows=39 width=32) (actual time=54.635..55.450 rows=475 loops=1)
Sort Key: (cube_distance($0, earth_distance.earth_coords) / 1609.344::double precision)
InitPlan
-> Bitmap Heap Scan on earth_distance (cost=4.74..624.60 rows=212 width=32) (actual time=0.113..0.115 rows=1 loops=1)
Recheck Cond: ((zipcode)::text = '90210'::text)
-> Bitmap Index Scan on earth_distance_zipcode_idx (cost=0.00..4.74 rows=212 width=0) (actual time=0.101..0.101 rows=2 loops=1)
Index Cond: ((zipcode)::text = '90210'::text)
-> Bitmap Heap Scan on earth_distance (cost=4.74..624.60 rows=212 width=32) (actual time=0.205..0.208 rows=1 loops=1)
Recheck Cond: ((zipcode)::text = '90210'::text)
-> Bitmap Index Scan on earth_distance_zipcode_idx (cost=0.00..4.74 rows=212 width=0) (actual time=0.160..0.160 rows=2 loops=1)
Index Cond: ((zipcode)::text = '90210'::text)
-> Hash Join (cost=618.67..5754.30 rows=39 width=32) (actual time=13.499..52.924 rows=475 loops=1)
Hash Cond: ("outer".shelter_id = "inner".shelter_id)
-> Bitmap Heap Scan on pets (cost=44.85..5158.42 rows=4298 width=4) (actual time=4.278..34.192 rows=3843 loops=1)
Recheck Cond: ((pet_state)::text = 'available'::text)
Filter: (species_id = 1)
-> Bitmap Index Scan on pets_pet_state_idx (cost=0.00..44.85 rows=6244 width=0) (actual time=3.623..3.623 rows=7257 loops=1)
Index Cond: ((pet_state)::text = 'available'::text)
-> Hash (cost=573.65..573.65 rows=66 width=36) (actual time=8.916..8.916 rows=102 loops=1)
-> Nested Loop (cost=3.15..573.65 rows=66 width=36) (actual time=3.004..8.513 rows=102 loops=1)
-> Bitmap Heap Scan on earth_distance (cost=3.15..152.36 rows=42 width=41) (actual time=2.751..3.432 rows=240 loops=1)
Recheck Cond: (cube_enlarge(($1)::cube, 16093.4357308298::double precision, 3) @ earth_coords)
-> Bitmap Index Scan on earth_coords_idx (cost=0.00..3.15 rows=42 width=0) (actual time=2.520..2.520 rows=480 loops=1)
Index Cond: (cube_enlarge(($1)::cube, 16093.4357308298::double precision, 3) @ earth_coords)
-> Index Scan using shelters_postal_code_for_joining_idx on shelters (cost=0.00..10.01 rows=2 width=12) (actual time=0.011..0.015 rows=0 loops=240)
Index Cond: ((shelters.postal_code_for_joining)::text = ("outer".zipcode)::text)
Total runtime: 58.038 ms
