"Glen M. Witherington" <glen@xxxxxx> writes:
> And here's the query I want to do, efficiently:
> SELECT * FROM c
> JOIN b ON b.id = c.b_id
> JOIN a ON a.id = b.a_id
> WHERE a.id = 3
> ORDER BY b.created_at DESC
> LIMIT 10
At least for that dummy data, this seems sufficient:
regression=# create index on b (a_id, created_at);
CREATE INDEX
regression=# explain analyze SELECT * FROM c
JOIN b ON b.id = c.b_id
JOIN a ON a.id = b.a_id
WHERE a.id = 3
ORDER BY b.created_at DESC
LIMIT 10;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=0.14..21.95 rows=10 width=64) (actual time=0.064..1.176 rows=10 loops=1)
-> Nested Loop (cost=0.14..436079.81 rows=200000 width=64) (actual time=0.063..1.173 rows=10 loops=1)
Join Filter: (b.id = c.b_id)
Rows Removed by Join Filter: 1218
-> Nested Loop (cost=0.14..9.81 rows=20 width=40) (actual time=0.035..0.035 rows=1 loops=1)
-> Index Scan Backward using b_a_id_created_at_idx on b (cost=0.14..8.49 rows=20 width=24) (actual time=0.019..0.019 rows=1 loops=1)
Index Cond: (a_id = 3)
-> Materialize (cost=0.00..1.07 rows=1 width=16) (actual time=0.013..0.013 rows=1 loops=1)
-> Seq Scan on a (cost=0.00..1.06 rows=1 width=16) (actual time=0.009..0.009 rows=1 loops=1)
Filter: (id = 3)
Rows Removed by Filter: 2
-> Materialize (cost=0.00..27230.00 rows=1000000 width=24) (actual time=0.008..0.811 rows=1228 loops=1)
-> Seq Scan on c (cost=0.00..16370.00 rows=1000000 width=24) (actual time=0.007..0.310 rows=1228 loops=1)
Planning time: 0.796 ms
Execution time: 1.390 ms
(15 rows)
regards, tom lane
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