On Thu, Dec 3, 2015 at 3:25 AM, Robert Haas <robertmh...@gmail.com> wrote:
>
> On Tue, Dec 1, 2015 at 7:21 AM, Amit Kapila <amit.kapil...@gmail.com>
wrote:
> > It would be better if we can split this patch into multiple patches like
> > Explain related changes, Append pushdown related changes, Join
> > Push down related changes. You can choose to push the patches as
> > you prefer, but splitting can certainly help in review/verification of
the
> > code.
>
> I don't think it really makes sense to split the append push-down
> changes from the join push-down changes; those share a great deal of
> code.
Not an issue. I have started looking into parallel join patch and below are
few findings:
1.
There are few compilation errors in the patch. It seems patch needs
to adapt the latest changes done in commit-edca44b1.
1>src/backend/optimizer/path/joinpath.c(420): error C2039:
'extra_lateral_rels' : is not a member of
'JoinPathExtraData'
1>
E:\WorkSpace\PostgreSQL\master\postgresql\src\include\nodes/relation.h(1727)
: see declaration of
'JoinPathExtraData'
..
..
2.
Why consider_parallel_nestloop() doesn't consider materializing inner
relation as we do in match_unsorted_outer()?
I have generated a test as below where non-parallel Nestloop join is
faster than parallel Nestloop join. I am using 'hydra' for testing this
patch.
CREATE TABLE t1(c1, c2) AS SELECT g, repeat('x', 5) FROM
generate_series(1, 10000000) g;
CREATE TABLE t2(c1, c2) AS SELECT g, repeat('x', 5) FROM
generate_series(1, 2000000) g;
Analyze t1;
Analyze t2;
Restart Server
Connect with psql
set enable_hashjoin=off;
set enable_mergejoin=off;
postgres=# Explain Analyze SELECT count(*) FROM t1 JOIN t2 ON t1.c1 = t2.c1
AND t1.c1 BETWEEN 100000 AND
100100;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------
--------------
Aggregate (cost=3294864.21..3294864.21 rows=1 width=0) (actual
time=42614.102..42614.102 rows=1 loops=1)
-> Nested Loop (cost=0.00..3294864.16 rows=20 width=0) (actual
time=4123.463..42614.084 rows=101
loops=1)
Join Filter: (t1.c1 = t2.c1)
Rows Removed by Join Filter: 201999899
-> Seq Scan on t2 (cost=0.00..30811.00 rows=2000000 width=4)
(actual time=0.027..284.979
rows=2000000 loops=1)
-> Materialize (cost=0.00..204053.41 rows=102 width=4) (actual
time=0.000..0.008 rows=101
loops=2000000)
-> Seq Scan on t1 (cost=0.00..204052.90 rows=102 width=4)
(actual time=13.920..2024.684
rows=101 loops=1)
Filter: ((c1 >= 100000) AND (c1 <= 100100))
Rows Removed by Filter: 9999899
Planning time: 0.085 ms
Execution time: 42614.135 ms
I have repeated the above statement 3 times and the above result is
median of 3 runs.
Restart Server
Connect with psql
set enable_hashjoin=off;
set enable_mergejoin=off;
set max_parallel_degree=4;
Explain Analyze SELECT count(*) FROM t1 JOIN t2 ON t1.c1 = t2.c1 AND t1.c1
BETWEEN 100000 AND 100100;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------
---------------------
Aggregate (cost=1311396.47..1311396.48 rows=1 width=0) (actual
time=45736.973..45736.973 rows=1 loops=1)
-> Gather (cost=1000.00..1311396.42 rows=20 width=0) (actual
time=709.083..45736.925 rows=101 loops=1)
Number of Workers: 4
-> Nested Loop (cost=0.00..1310394.42 rows=20 width=0) (actual
time=436.460..11240.321 rows=20
loops=5)
Join Filter: (t1.c1 = t2.c1)
Rows Removed by Join Filter: 40399980
-> Parallel Seq Scan on t1 (cost=0.00..45345.09 rows=23
width=4) (actual
time=425.178..425.232 rows=20 loops=5)
Filter: ((c1 >= 100000) AND (c1 <= 100100))
Rows Removed by Filter: 1999980
-> Seq Scan on t2 (cost=0.00..30811.00 rows=2000000
width=4) (actual time=0.011..270.986
rows=2000000 loops=101)
Planning time: 0.115 ms
Execution time: 45737.863 ms
I have repeated the above statement 3 times and the above result is
median of 3 runs.
Now here the point to observe is that non-parallel case uses both less
Execution time and Planning time to complete the statement. There
is a considerable increase in planning time without any benefit in
execution.
With Regards,
Amit Kapila.
EnterpriseDB: http://www.enterprisedb.com
