Table bl_data 9 million+ polygon rows with GIST index and the index is clustered
create index ii3 on bl_data using gist(geom); CLUSTER ii3 ON bl_data;
I have a query that finds records with the same borough,block,lot and geometry
select distinct a.id from bl_data a join bl_data b on st_equals(a.geom,b.geom) and a.yr<>b.yr and a.borough='BX' and a.block=3805 and a.lot=7501
running explain analyze verbose yields
"Unique (cost=0.43..237511571.55 rows=1 width=4) (actual time=7711.454..25624.703 rows=1 loops=1)" " Output: a.id" " -> Nested Loop (cost=0.43..237511560.46 rows=4437 width=4) (actual time=7711.453..25624.701 rows=1 loops=1)" " Output: a.id" " Join Filter: ((a.yr <> b.yr) AND st_equals(a.geom, b.geom))" " Rows Removed by Join Filter: 37741307" " -> Index Scan using ii3_indx on public.bl_data a (cost=0.43..1262864.82 rows=1 width=159) (actual time=7383.147..11020.128 rows=4 loops=1)" " Output: a.yr, a.borough, a.block, a.lot, a.geom, a.id, a.dupe" " Filter: (((a.borough)::text = 'BX'::text) AND (a.block = 3805) AND (a.lot = 7501))" " Rows Removed by Filter: 9435323" " -> Seq Scan on public.bl_data b (cost=0.00..331195.84 rows=9431984 width=155) (actual time=0.019..2132.602 rows=9435327 loops=4)" " Output: b.yr, b.borough, b.block, b.lot, b.geom, b.id, b.dupe" "Planning Time: 1.167 ms" "Execution Time: 25624.780 ms"
the query takes about 25 seconds to complete and I am planning to wrap this query into a function and run it on the entire table which will likely take weeks(?) to finish at this pace.
what steps can I take to speed this up? would indexing borough,block and lot be helpful? is the clustering not helpful?