postgresql 数据库执行计划 Nested Loop

os: centos 7.4
db: postgresql 10.11

Nested Loop 比较适合两个表的数据量都比较少的情况(最简单的 table join 方式)

1. 外层驱动表为小表，且过滤后的数据量较少。
2. 内层被驱动表的关联列上最好有高效索引(主键或者唯一性索引)。

版本

# cat /etc/centos-release
CentOS Linux release 7.4.1708 (Core) 
# 
# su - postgres
$
$ psql -c "select version();"
                                                 version                                                  
----------------------------------------------------------------------------------------------------------
 PostgreSQL 10.11 on x86_64-pc-linux-gnu, compiled by gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-39), 64-bit
(1 row)

create table

$ psql
psql (10.11)
Type "help" for help.

postgres=# 
postgres=# drop table if exists tmp_t4;
drop table if exists tmp_t5;


postgres=# create table tmp_t4( 
	id    int8 primary key,
	name  varchar(100)
);

create table tmp_t5( 
	id    int8 primary key,
	name  varchar(100)
);

postgres=# insert into tmp_t4 
select id,
       md5(id::varchar)
  from generate_series(1,1000000) as id;

insert into tmp_t5 
select id,
       md5(id::varchar)
  from generate_series(1,1000000) as id;

postgres=# vacuum analyze tmp_t4;
vacuum analyze tmp_t5;

Nested Loop

postgres=# set max_parallel_workers_per_gather=0;

postgres=# explain analyze 
select t4.*,t5.*
  from tmp_t4 t4,
       tmp_t5 t5
 where 1=1
   and t4.id = t5.id
   and t4.id = 499999
;

                                                          QUERY PLAN                                                          
------------------------------------------------------------------------------------------------------------------------------
 Nested Loop  (cost=0.85..16.90 rows=1 width=82) (actual time=0.015..0.016 rows=1 loops=1)
   ->  Index Scan using tmp_t4_pkey on tmp_t4 t4  (cost=0.42..8.44 rows=1 width=41) (actual time=0.008..0.008 rows=1 loops=1)
         Index Cond: (id = 499999)
   ->  Index Scan using tmp_t5_pkey on tmp_t5 t5  (cost=0.42..8.44 rows=1 width=41) (actual time=0.006..0.007 rows=1 loops=1)
         Index Cond: (id = 499999)
 Planning time: 0.079 ms
 Execution time: 0.032 ms
(7 rows)
   

执行计划显示： tmp_t4 作为驱动表 通过 Nested Loop 方式 探测 tmp_t5 数据，类似 for 循环

 for(t4.data in tmp_t4) 
 { 
	t5.data in tmp_t5 on t5.data = t4.data
 }
 

参考: