Please see this case:
TPC-H query 2:
select
s_acctbal,
s_name,
n_name,
p_partkey,
p_mfgr,
s_address,
s_phone,
s_comment
from
PART,
SUPPLIER,
PARTSUPP,
NATION,
REGION
where
p_partkey = ps_partkey
and s_suppkey = ps_suppkey
and p_size = 30
and p_type like '%STEEL'
and s_nationkey = n_nationkey
and n_regionkey = r_regionkey
and r_name = 'ASIA'
and ps_supplycost = (
select
min(ps_supplycost)
from
PARTSUPP,
SUPPLIER,
NATION,
REGION
where
p_partkey = ps_partkey
and s_suppkey = ps_suppkey
and s_nationkey = n_nationkey
and n_regionkey = r_regionkey
and r_name = 'ASIA'
)
order by
s_acctbal desc,
n_name,
s_name,
p_partkey
limit
100;
Its query plan and execution time:
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=66275.04..66275.05 rows=1 width=192) (actual
time=268.349..268.418 rows=100 loops=1)
-> Sort (cost=66275.04..66275.05 rows=1 width=192) (actual
time=268.348..268.411 rows=100 loops=1)
Sort Key: supplier.s_acctbal DESC, nation.n_name, supplier.s_name,
part.p_partkey
Sort Method: top-N heapsort Memory: 70kB
-> Hash Join (cost=37831.01..66275.03 rows=1 width=192) (actual
time=230.386..268.130 rows=485 loops=1)
Hash Cond: ((part.p_partkey = partsupp.ps_partkey) AND ((SubPlan
1) = partsupp.ps_supplycost))
-> Gather (cost=1000.00..6425.40 rows=784 width=30) (actual
time=0.586..0.753 rows=826 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Parallel Seq Scan on part (cost=0.00..5347.00
rows=327 width=30) (actual time=0.082..16.979 rows=275 loops=3)
Filter: (((p_type)::text ~~ '%STEEL'::text) AND
(p_size = 30))
Rows Removed by Filter: 66391
-> Hash (cost=30524.01..30524.01 rows=160000 width=172)
(actual time=228.502..228.506 rows=160240 loops=1)
Buckets: 65536 Batches: 8 Memory Usage: 4648kB
-> Hash Join (cost=408.01..30524.01 rows=160000
width=172) (actual time=4.820..165.744 rows=160240 loops=1)
Hash Cond: (partsupp.ps_suppkey = supplier.s_suppkey)
-> Seq Scan on partsupp (cost=0.00..25516.00
rows=800000 width=14) (actual time=0.013..63.459 rows=800000 loops=1)
-> Hash (cost=383.01..383.01 rows=2000 width=166)
(actual time=4.789..4.792 rows=2003 loops=1)
Buckets: 2048 Batches: 1 Memory Usage: 413kB
-> Hash Join (cost=2.51..383.01 rows=2000
width=166) (actual time=0.098..3.945 rows=2003 loops=1)
Hash Cond: (supplier.s_nationkey =
nation.n_nationkey)
-> Seq Scan on supplier
(cost=0.00..323.00 rows=10000 width=144) (actual time=0.013..2.060 rows=10000
loops=1)
-> Hash (cost=2.45..2.45 rows=5
width=30) (actual time=0.053..0.055 rows=5 loops=1)
Buckets: 1024 Batches: 1 Memory
Usage: 9kB
-> Hash Join (cost=1.07..2.45
rows=5 width=30) (actual time=0.043..0.049 rows=5 loops=1)
Hash Cond:
(nation.n_regionkey = region.r_regionkey)
-> Seq Scan on nation
(cost=0.00..1.25 rows=25 width=34) (actual time=0.008..0.010 rows=25 loops=1)
-> Hash (cost=1.06..1.06
rows=1 width=4) (actual time=0.014..0.014 rows=1 loops=1)
Buckets: 1024
Batches: 1 Memory Usage: 9kB
-> Seq Scan on region
(cost=0.00..1.06 rows=1 width=4) (actual time=0.009..0.010 rows=1 loops=1)
Filter: (r_name
= 'ASIA'::bpchar)
Rows Removed by
Filter: 4
SubPlan 1
-> Aggregate (cost=48.70..48.71 rows=1 width=32) (actual
time=0.018..0.018 rows=1 loops=1311)
-> Nested Loop (cost=0.85..48.70 rows=1 width=6)
(actual time=0.013..0.017 rows=1 loops=1311)
Join Filter: (region_1.r_regionkey =
nation_1.n_regionkey)
Rows Removed by Join Filter: 3
-> Seq Scan on region region_1 (cost=0.00..1.06
rows=1 width=4) (actual time=0.000..0.001 rows=1 loops=1311)
Filter: (r_name = 'ASIA'::bpchar)
Rows Removed by Filter: 4
-> Nested Loop (cost=0.85..47.58 rows=4
width=10) (actual time=0.009..0.016 rows=4 loops=1311)
-> Nested Loop (cost=0.71..46.96 rows=4
width=10) (actual time=0.008..0.012 rows=4 loops=1311)
-> Index Scan using partsupp_pkey on
partsupp partsupp_1 (cost=0.42..13.75 rows=4 width=10) (actual
time=0.006..0.007 rows=4 loops=1311)
Index Cond: (ps_partkey =
part.p_partkey)
-> Index Scan using supplier_pkey on
supplier supplier_1 (cost=0.29..8.30 rows=1 width=8) (actual time=0.001..0.001
rows=1 loops=5244)
Index Cond: (s_suppkey =
partsupp_1.ps_suppkey)
-> Index Scan using nation_pkey on nation
nation_1 (cost=0.14..0.16 rows=1 width=8) (actual time=0.001..0.001 rows=1
loops=5244)
Index Cond: (n_nationkey =
supplier_1.s_nationkey)
Planning Time: 2.613 ms
Execution Time: 268.610 ms
(50 rows)
After applying this patch:
diff --git a/src/backend/optimizer/path/joinpath.c
b/src/backend/optimizer/path/joinpath.c
index 5be8da9e09..0f11b1cbdf 100644
--- a/src/backend/optimizer/path/joinpath.c
+++ b/src/backend/optimizer/path/joinpath.c
@@ -449,7 +449,7 @@ paraminfo_get_equal_hashops(PlannerInfo *root,
ParamPathInfo *param_info,
*binary_mode = false;
/* Add join clauses from param_info to the hash key */
- if (param_info != NULL)
+ if (false)
{
List *clauses = param_info->ppi_clauses;
The query plan and execution time:
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=6713.81..6713.81 rows=1 width=192) (actual time=86.461..86.532
rows=100 loops=1)
-> Sort (cost=6713.81..6713.81 rows=1 width=192) (actual
time=86.460..86.526 rows=100 loops=1)
Sort Key: supplier.s_acctbal DESC, nation.n_name, supplier.s_name,
part.p_partkey
Sort Method: top-N heapsort Memory: 69kB
-> Nested Loop (cost=1000.72..6713.80 rows=1 width=192) (actual
time=0.855..86.150 rows=485 loops=1)
Join Filter: (region.r_regionkey = nation.n_regionkey)
-> Nested Loop (cost=1000.72..6712.72 rows=1 width=196)
(actual time=0.852..85.448 rows=485 loops=1)
Join Filter: (nation.n_nationkey = supplier.s_nationkey)
Rows Removed by Join Filter: 6381
-> Nested Loop (cost=1000.72..6711.16 rows=1 width=170)
(actual time=0.847..83.975 rows=485 loops=1)
-> Nested Loop (cost=1000.43..6710.86 rows=1
width=34) (actual time=0.828..83.116 rows=485 loops=1)
-> Gather (cost=1000.00..6425.40 rows=784
width=30) (actual time=0.579..7.037 rows=826 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Parallel Seq Scan on part
(cost=0.00..5347.00 rows=327 width=30) (actual time=0.094..17.686 rows=275
loops=3)
Filter: (((p_type)::text ~~
'%STEEL'::text) AND (p_size = 30))
Rows Removed by Filter: 66391
-> Memoize (cost=0.43..133.03 rows=1
width=14) (actual time=0.076..0.092 rows=1 loops=826)
Cache Key:
Cache Mode: logical
Hits: 0 Misses: 826 Evictions: 825
Overflows: 0 Memory Usage: 1kB
-> Index Scan using partsupp_pkey on
partsupp (cost=0.42..133.02 rows=1 width=14) (actual time=0.075..0.091 rows=1
loops=826)
Index Cond: (ps_partkey =
part.p_partkey)
Filter: (ps_supplycost = (SubPlan
1))
Rows Removed by Filter: 3
SubPlan 1
-> Aggregate
(cost=30.11..30.12 rows=1 width=32) (actual time=0.018..0.018 rows=1 loops=3304)
-> Nested Loop
(cost=0.86..30.11 rows=1 width=6) (actual time=0.013..0.017 rows=1 loops=3304)
Join Filter:
(region_1.r_regionkey = nation_1.n_regionkey)
Rows Removed by Join
Filter: 3
-> Seq Scan on
region region_1 (cost=0.00..1.06 rows=1 width=4) (actual time=0.001..0.001
rows=1 loops=3304)
Filter:
(r_name = 'ASIA'::bpchar)
Rows Removed
by Filter: 4
-> Nested Loop
(cost=0.86..29.00 rows=4 width=10) (actual time=0.005..0.016 rows=4 loops=3304)
-> Nested
Loop (cost=0.72..28.37 rows=4 width=10) (actual time=0.004..0.011 rows=4
loops=3304)
->
Index Scan using partsupp_pkey on partsupp partsupp_1 (cost=0.42..13.75 rows=4
width=10) (actual time=0.002..0.002 rows=4 loops=3304)
Index Cond: (ps_partkey = part.p_partkey)
->
Memoize (cost=0.30..8.31 rows=1 width=8) (actual time=0.002..0.002 rows=1
loops=13216)
Cache Key:
Cache Mode: logical
Hits: 0 Misses: 13216 Evictions: 13215 Overflows: 0 Memory Usage: 1kB
->
Index Scan using supplier_pkey on supplier supplier_1 (cost=0.29..8.30 rows=1
width=8) (actual time=0.001..0.001 rows=1 loops=13216)
Index Cond: (s_suppkey = partsupp_1.ps_suppkey)
-> Index Scan
using nation_pkey on nation nation_1 (cost=0.14..0.16 rows=1 width=8) (actual
time=0.001..0.001 rows=1 loops=13216)
Index
Cond: (n_nationkey = supplier_1.s_nationkey)
-> Index Scan using supplier_pkey on supplier
(cost=0.29..0.30 rows=1 width=144) (actual time=0.001..0.001 rows=1 loops=485)
Index Cond: (s_suppkey = partsupp.ps_suppkey)
-> Seq Scan on nation (cost=0.00..1.25 rows=25 width=34)
(actual time=0.000..0.001 rows=14 loops=485)
-> Seq Scan on region (cost=0.00..1.06 rows=1 width=4) (actual
time=0.001..0.001 rows=1 loops=485)
Filter: (r_name = 'ASIA'::bpchar)
Rows Removed by Filter: 2
Planning Time: 2.669 ms
Execution Time: 86.712 ms
(53 rows)
The estimated cost is reduced by 90%, and the execution time is reduced by 68%.
The second query plan includes the operation Memoize, while the first query
plan does not. I am wondering if we can optimize the logic anywhere to enable
the second query plan.
Environment:
For PostgreSQL, I used the default configuration file.
For the hardware, my disk is HDD.
For the benchmark, I used 1 GB data, and my entire data folder can be
downloaded here:
https://drive.google.com/file/d/1ZBLHanIRwxbaMQIhRUSPv4I7y8g_0AWi/view?usp=sharing
The connection string is: postgresql://ubuntu:[email protected]:5432/tpch"
tpch=# select version();
version
--------------------------------------------------------------------------------------------------
PostgreSQL 17.0 on x86_64-pc-linux-gnu, compiled by gcc (Ubuntu
13.2.0-23ubuntu4) 13.2.0, 64-bit
(1 row)
Best regards,
Jinsheng Ba
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