Nested Loop Join with FDW

Update: See below, but I didn’t test the full pushdown case, and the result is pretty awesome.

I have been wondering for a while if Postgres would correctly plan a spatial join over FDW, in which one table was local and one was remote. The specific use case would be “keeping a large pile of data on one side of the link, and joining to it”.

Because spatial joins always plan out to a “nested loop” execution, where one table is chosen to drive the loop, and the other to be filtered on the rows from the driver, there’s nothing to prevent the kind of remote execution I was looking for.

I set up my favourite spatial join test: BC voting areas against BC electoral districts, with local and remote versions of both tables.

CREATE EXTENSION postgres_fdw;

-- Loopback foreign server connects back to
-- this same database
host '',
dbname 'test',
extensions 'postgis'

OPTIONS (user 'pramsey', password '');

-- Foreign versions of the local tables
gid integer,
edname text,
edabbr text,
geom geometry(MultiPolygon,4326)
) SERVER test
table_name 'ed_2013',
use_remote_estimate 'true');

gid integer OPTIONS (column_name 'gid'),
id text OPTIONS (column_name 'id'),
vaabbr text OPTIONS (column_name 'vaabbr'),
edabbr text OPTIONS (column_name 'edabbr'),
geom geometry(MultiPolygon,4326) OPTIONS (column_name 'geom')
) SERVER test
table_name 'va_2013',
use_remote_estimate 'true');

The key option here is use_remote_estimate set to true. This tells postgres_fdw to query the remote server for an estimate of the remote table selectivity, which is then fed into the planner. Without use_remote_estimate, PostgreSQL will generate a terrible plan that pulls the contents of the `va_2013_fdw table local before joining.

With use_remote_estimate in place, the plan is just right:

SELECT count(*), e.edabbr
FROM ed_2013 e
JOIN va_2013_fdw v
ON ST_Intersects(e.geom, v.geom)
WHERE e.edabbr in ('VTB', 'VTS')
GROUP BY e.edabbr;
GroupAggregate  (cost=241.14..241.21 rows=2 width=12)
 Output: count(*), e.edabbr
 Group Key: e.edabbr
 ->  Sort  (cost=241.14..241.16 rows=6 width=4)
     Output: e.edabbr
     Sort Key: e.edabbr
     ->  Nested Loop  (cost=100.17..241.06 rows=6 width=4)
         Output: e.edabbr
         ->  Seq Scan on public.ed_2013 e  (cost=0.00..22.06 rows=2 width=158496)
             Output: e.gid, e.edname, e.edabbr, e.geom
             Filter: ((e.edabbr)::text = ANY ('{VTB,VTS}'::text[]))
         ->  Foreign Scan on public.va_2013_fdw v  (cost=100.17..109.49 rows=1 width=4236)
             Output: v.gid,, v.vaabbr, v.edabbr, v.geom
             Remote SQL: SELECT geom FROM public.va_2013 WHERE (($1::public.geometry(MultiPolygon,4326) OPERATOR(public.&&) geom)) AND (public._st_intersects($1::public.geometry(MultiPolygon,4326), geom))

For FDW drivers other than postgres_fdw this means there’s a benefit to going to the trouble to support the FDW estimation callbacks, though the lack of exposed estimation functions in a lot of back-ends may mean the support will be ugly hacks and hard-coded nonsense. PostgreSQL is pretty unique in exposing fine-grained information about table statistics.


One “bad” thing about the join pushdown plan above is that it still pulls all the resultant records back to the source before aggregating them, so there’s a missed opportunity there. However, if both the tables in the join condition are remote, the system will correctly plan the query as a remote join and aggregation.

SELECT count(*), e.edabbr
FROM ed_2013_fdw e
JOIN va_2013_fdw v
ON ST_Intersects(e.geom, v.geom)
WHERE e.edabbr in ('VTB', 'VTS')
GROUP BY e.edabbr;
 Foreign Scan  
   (cost=157.20..157.26 rows=1 width=40) 
   (actual time=32.750..32.752 rows=2 loops=1)
   Output: (count(*)), e.edabbr
   Relations: Aggregate on ((public.ed_2013_fdw e) INNER JOIN (public.va_2013_fdw v))
   Remote SQL: SELECT count(*), r1.edabbr FROM (public.ed_2013 r1 INNER JOIN public.va_2013 r2 ON (((r1.geom OPERATOR(public.&&) r2.geom)) AND (public._st_intersects(r1.geom, r2.geom)) AND ((r1.edabbr = ANY ('{VTB,VTS}'::text[]))))) GROUP BY r1.edabbr
 Planning time: 12.752 ms
 Execution time: 33.145 ms