Re: MergeJoin beats HashJoin in the case of multiple hash clauses - Mailing list pgsql-hackers

Hi!

Hi, all.

Some of my clients use JOIN's with three - four clauses. Quite frequently, I see complaints on unreasonable switch of JOIN algorithm to Merge Join instead of Hash Join. Quick research have shown one weak place - estimation of an average bucket size in final_cost_hashjoin (see q2.sql in attachment) with very conservative strategy.
Unlike estimation of groups, here we use smallest ndistinct value across all buckets instead of multiplying them (or trying to make multivariate analysis).
It works fine for the case of one clause. But if we have many clauses, and if each has high value of ndistinct, we will overestimate average size of a bucket and, as a result, prefer to use Merge Join. As the example in attachment shows, it leads to worse plan than possible, sometimes drastically worse.
I assume, this is done with fear of functional dependencies between hash clause components. But as for me, here we should go the same way, as estimation of groups.
The attached patch shows a sketch of the solution.

This problem is very important.

Honestly, I'm still learning your code and looking for cases on which cases your patch can affect for the worse or for the better. But I have already found something that seemed interesting to me. I have found several other interesting cases where your patch can solve some problem in order to choose a more correct plan, but in focus on memory consumption.
To make it easier to evaluate, I added a hook to your patch that makes it easier to switch to your or the original way of estimating the size of baskets (diff_estimate.diff).

Here are other cases where your fix improves the query plan.

First of all, I changed the way creation of tables are created to look at the behavior of the query plan in terms of planning and execution time:

DROP TABLE IF EXISTS a,b CASCADE;
CREATE TABLE a AS
  SELECT ((3*gs) % 300) AS x, ((3*gs+1) % 300) AS y, ((3*gs+2) % 300) AS z
  FROM generate_series(1,1e5) AS gs;
CREATE TABLE b AS
  SELECT gs % 90 AS x, gs % 49 AS y, gs %100 AS z, 'abc' || gs AS payload
  FROM generate_series(1,1e5) AS gs;
ANALYZE a,b;

SET enable_cost_size = 'on';
EXPLAIN ANALYZE
SELECT * FROM a,b
WHERE a.x=b.x AND a.y=b.y AND a.z=b.z;

SET enable_cost_size = 'off';
EXPLAIN ANALYZE
SELECT * FROM a,b
WHERE a.x=b.x AND a.y=b.y AND a.z=b.z;

                                QUERY PLAN                                 
---------------------------------------------------------------------------
 Hash Join (actual time=200.872..200.879 rows=0 loops=1)
   Hash Cond: ((b.x = a.x) AND (b.y = a.y) AND (b.z = a.z))
   ->  Seq Scan on b (actual time=0.029..15.946 rows=100000 loops=1)
   ->  Hash (actual time=97.645..97.649 rows=100000 loops=1)
         Buckets: 131072  Batches: 1  Memory Usage: 5612kB
         ->  Seq Scan on a (actual time=0.024..17.153 rows=100000 loops=1)
 Planning Time: 2.910 ms
 Execution Time: 201.949 ms
(8 rows)

SET
                                QUERY PLAN                                 
---------------------------------------------------------------------------
 Merge Join (actual time=687.415..687.416 rows=0 loops=1)
   Merge Cond: ((b.y = a.y) AND (b.x = a.x) AND (b.z = a.z))
   ->  Sort (actual time=462.022..536.716 rows=100000 loops=1)
         Sort Key: b.y, b.x, b.z
         Sort Method: external merge  Disk: 3328kB
         ->  Seq Scan on b (actual time=0.017..12.326 rows=100000 loops=1)
   ->  Sort (actual time=111.295..113.196 rows=16001 loops=1)
         Sort Key: a.y, a.x, a.z
         Sort Method: external sort  Disk: 2840kB
         ->  Seq Scan on a (actual time=0.020..10.129 rows=100000 loops=1)
 Planning Time: 0.752 ms
 Execution Time: 688.829 ms
(12 rows)

Secondly, I found another case that is not related to the fact that the planner would prefer to choose merge join rather than hash join, but we have the opportunity to see that the plan has become better due to the consumption of less memory, and also takes less planning time.

Here, with the same query, the planning time was reduced by 5 times, and the number of buckets by 128 times, therefore, memory consumption also decreased:

DROP TABLE IF EXISTS a,b CASCADE;

CREATE TABLE a AS
  SELECT ((3*gs) % 300) AS x, ((3*gs+1) % 300) AS y, ((3*gs+2) % 300) AS z
  FROM generate_series(1,600) AS gs;
CREATE TABLE b AS
  SELECT gs % 90 AS x, gs % 49 AS y, gs %100 AS z, 'abc' || gs AS payload
  FROM generate_series(1,1e5) AS gs;
ANALYZE a,b;

SET enable_cost_size = 'on';
EXPLAIN ANALYZE
SELECT * FROM a,b
WHERE a.x=b.x AND a.y=b.y AND a.z=b.z;

SET enable_cost_size = 'off';
EXPLAIN ANALYZE
SELECT * FROM a,b
WHERE a.x=b.x AND a.y=b.y AND a.z=b.z;

                                                   QUERY PLAN                                                   
----------------------------------------------------------------------------------------------------------------
 Hash Join  (cost=20.50..3157.58 rows=8 width=32) (actual time=95.648..95.651 rows=0 loops=1)
   Hash Cond: ((b.x = (a.x)::numeric) AND (b.y = (a.y)::numeric) AND (b.z = (a.z)::numeric))
   ->  Seq Scan on b  (cost=0.00..1637.00 rows=100000 width=20) (actual time=0.027..17.980 rows=100000 loops=1)
   ->  Hash  (cost=10.00..10.00 rows=600 width=12) (actual time=2.046..2.047 rows=600 loops=1)
         Buckets: 1024  Batches: 1  Memory Usage: 34kB
         ->  Seq Scan on a  (cost=0.00..10.00 rows=600 width=12) (actual time=0.022..0.315 rows=600 loops=1)
 Planning Time: 0.631 ms
 Execution Time: 95.730 ms
(8 rows)

SET
                                                      QUERY PLAN                                                      
----------------------------------------------------------------------------------------------------------------------
 Hash Join  (cost=3387.00..8621.58 rows=8 width=32) (actual time=102.873..102.877 rows=0 loops=1)
   Hash Cond: (((a.x)::numeric = b.x) AND ((a.y)::numeric = b.y) AND ((a.z)::numeric = b.z))
   ->  Seq Scan on a  (cost=0.00..10.00 rows=600 width=12) (actual time=0.014..0.131 rows=600 loops=1)
   ->  Hash  (cost=1637.00..1637.00 rows=100000 width=20) (actual time=101.920..101.921 rows=100000 loops=1)
         Buckets: 131072  Batches: 1  Memory Usage: 6474kB
         ->  Seq Scan on b  (cost=0.00..1637.00 rows=100000 width=20) (actual time=0.013..16.349 rows=100000 loops=1)
 Planning Time: 0.153 ms
 Execution Time: 103.518 ms
(8 rows)

I also give an improvement relative to the left external or right connection:

DROP TABLE IF EXISTS a,b CASCADE;

CREATE TABLE a AS
  SELECT ((3*gs) % 300) AS x, ((3*gs+1) % 300) AS y, ((3*gs+2) % 300) AS z
  FROM generate_series(1,600) AS gs;
CREATE TABLE b AS
  SELECT gs % 90 AS x, gs % 49 AS y, gs %100 AS z, 'abc' || gs AS payload
  FROM generate_series(1,1e5) AS gs;
ANALYZE a,b;


SET enable_cost_size = 'on';

EXPLAIN ANALYZE
SELECT * FROM a right join b
on a.x=b.x AND a.y=b.y AND a.z=b.z;

SET enable_cost_size = 'off';
EXPLAIN ANALYZE
SELECT * FROM a right join b
on a.x=b.x AND a.y=b.y AND a.z=b.z;

                                                   QUERY PLAN                                                   
----------------------------------------------------------------------------------------------------------------
 Hash Left Join  (cost=20.50..3157.58 rows=100000 width=32) (actual time=1.846..102.264 rows=100000 loops=1)
   Hash Cond: ((b.x = (a.x)::numeric) AND (b.y = (a.y)::numeric) AND (b.z = (a.z)::numeric))
   ->  Seq Scan on b  (cost=0.00..1637.00 rows=100000 width=20) (actual time=0.041..15.328 rows=100000 loops=1)
   ->  Hash  (cost=10.00..10.00 rows=600 width=12) (actual time=1.780..1.781 rows=600 loops=1)
         Buckets: 1024  Batches: 1  Memory Usage: 34kB
         ->  Seq Scan on a  (cost=0.00..10.00 rows=600 width=12) (actual time=0.031..0.252 rows=600 loops=1)
 Planning Time: 0.492 ms
 Execution Time: 107.609 ms
(8 rows)

SET
                                                      QUERY PLAN                                                      
----------------------------------------------------------------------------------------------------------------------
 Hash Right Join  (cost=3387.00..8500.08 rows=100000 width=32) (actual time=80.919..101.613 rows=100000 loops=1)
   Hash Cond: (((a.x)::numeric = b.x) AND ((a.y)::numeric = b.y) AND ((a.z)::numeric = b.z))
   ->  Seq Scan on a  (cost=0.00..10.00 rows=600 width=12) (actual time=0.017..0.084 rows=600 loops=1)
   ->  Hash  (cost=1637.00..1637.00 rows=100000 width=20) (actual time=80.122..80.123 rows=100000 loops=1)
         Buckets: 131072  Batches: 1  Memory Usage: 6474kB
         ->  Seq Scan on b  (cost=0.00..1637.00 rows=100000 width=20) (actual time=0.015..11.819 rows=100000 loops=1)
 Planning Time: 0.194 ms
 Execution Time: 104.662 ms
(8 rows)

-- 
Regards,
Alena Rybakina
Postgres Professional