Dear All,
I have read about query tuning and attempt to check the impact of
exchanging the driving table in Join condition.
To test Simple Join condition, I prepared Two tables.
One is the table for employees and another is the table for departments.
Employees table has the foreign key which referencing departments table.
The proportion of each table, filtered table and joined table is below:
(E means employees table and D means departments table.)
#rows in E #rows in D #filtered rows in E #filtered rows in D #rows in E and D
10000 490 1000 245 9800
After make the index on the filtered column, I tried the query:
EXPLAIN ANALYZE
SELECT D.Department_Name, E.Last_Name, E.First_Name
FROM Employees E, Departments D
WHERE E.Department_Id=D.Department_Id
AND E.Exempt_Flag='Y'
AND D.US_Based_Flag='Y'
;
Result was:
Hash Join (cost=8.85..241.59 rows=499 width=15) (actual time=0.105..2.052 rows=518 loops=1)
Hash Cond: (e.department_id = d.department_id)
-> Seq Scan on employees e (cost=0.00..209.00 rows=5000 width=17) (actual time=0.007..1.541 rows=5000 loops=1)
Filter: (exempt_flag = 'Y'::bpchar)
Rows Removed by Filter: 5000
-> Hash (cost=8.24..8.24 rows=49 width=14) (actual time=0.087..0.087 rows=49 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 3kB
-> Bitmap Heap Scan on departments d (cost=4.63..8.24 rows=49 width=14) (actual time=0.069..0.078 rows=49 loops=1)
Recheck Cond: (us_based_flag = 'Y'::bpchar)
-> Bitmap Index Scan on dept2_flg_idx (cost=0.00..4.62 rows=49 width=0) (actual time=0.063..0.063 rows=49 loops=1)
Index Cond: (us_based_flag = 'Y'::bpchar)
Total runtime: 2.095 ms
In order to exchange the driving table, I tried the query:
EXPLAIN ANALYZE
SELECT D.Department_Name, E.Last_Name, E.First_Name
FROM Departments D, Employees E
WHERE D.Department_Id=E.Department_Id
AND E.Exempt_Flag='Y'
AND D.US_Based_Flag='Y'
;
However, the result was same.
I think this is because the query planner can optimizer the 2nd query based on table statistics of E and D.
E being the larger number of records and has higher filtering rate so it continues to be driving table.
Now, I tried another test case to confirm my assumption.
The proportion of another test case is different from above one.
The proportion of each table, filtered table and joined table is below:
(E means employees table and D means departments table.)
#rows in E #rows in D #filtered rows in E #filtered rows in D #rows in E and D
10000 490 5000 49 9800
The important point is the difference in the filtering rate.
In this case, departments table is higher filtering rate,
so taking departments table as driving table will be able to cut the computational cost, I think.
I tried same query:
EXPLAIN ANALYZE
SELECT D.Department_Name, E.Last_Name, E.First_Name
FROM Employees E, Departments D
WHERE E.Department_Id=D.Department_Id
AND E.Exempt_Flag='Y'
AND D.US_Based_Flag='Y'
And
EXPLAIN ANALYZE
SELECT D.Department_Name, E.Last_Name, E.First_Name
FROM Employees E, Departments D
WHERE E.Department_Id=D.Department_Id
AND E.Exempt_Flag='Y'
AND D.US_Based_Flag='Y'
The results was same as earlier , though I expected driving table to be Departments table in both these cases
Question:
How does PostgerSQL planner decide driving table?
My environment is
Postgres ver. 9.2.7 on x86_64-redhat-linux-gnu
CentOS ver. 7.0.1406
on Virtual Box ver. 4.3.16
in Windows7
Regards,
Shingo Horiuchi
