CASE
一个这样的查询，每个表都只有几千条数据，但是查询非常慢，几十秒不出结果。
select
distinct abc.pro_col1, abc.col3
from
t0 p
INNER JOIN t1 abc
on p.id=abc.par_col2
inner join t2 s
on s.col3=abc.col3
inner join t3 po
on po.id=s.col4
where p.state=2 and po.state=3
order by abc.pro_col1, abc.col3; 优化方法
从语义来看，这条SQL是在经过几个JOIN后取其中一个表的两个字段的唯一值。
但是每一次关联，都可能产生冗余的值，所以导致了结果集越来越庞大。
修改建议，每一次JOIN都输出唯一值，减少冗余。
select
distinct pro_col1, col3 from
(
select
distinct t1.pro_col1, t1.col3, s.col4 from
(
select
distinct abc.pro_col1, abc.col3 from
t1 abc INNER JOIN t0 p
on (p.id = abc.par_col2 and p.state=2)
) t1
inner join t2 s
on (s.col3 = t1.col3)
) t2
inner join t3 po
on (po.id = t2.col4 and po.state=3)
order by t2.pro_col1, t2.col3 ;修改后几十毫秒可以输出结果。
重现
postgres=# create table rt1(id int, info text);
CREATE TABLE
postgres=# create table rt2(id int, info text);
CREATE TABLE
postgres=# create table rt3(id int, info text);
CREATE TABLE
postgres=# create table rt4(id int, info text);
CREATE TABLE
postgres=# insert into rt1 select generate_series(1,1000),'test';
INSERT 0 1000
postgres=# insert into rt2 select 1,'test' from generate_series(1,1000);
INSERT 0 1000
postgres=# insert into rt3 select 1,'test' from generate_series(1,1000);
INSERT 0 1000
postgres=# insert into rt4 select 1,'test' from generate_series(1,1000);
INSERT 0 1000以下查询，每次JOIN都产生大量的冗余数据，越到后面的JOIN，冗余越多，导致的查询非常漫长。
postgres=# explain select distinct rt1.id from rt1 join rt2 on rt1.id=rt2.id join rt3 on rt2.id=rt3.id join rt4 on rt3.id=rt4.id;
QUERY PLAN
-------------------------------------------------------------------------------------------------
HashAggregate (cost=145.25..155.25 rows=1000 width=4)
Group Key: rt1.id
-> Hash Join (cost=113.00..142.75 rows=1000 width=4)
Hash Cond: (rt4.id = rt1.id)
-> Seq Scan on rt4 (cost=0.00..16.00 rows=1000 width=4)
-> Hash (cost=100.50..100.50 rows=1000 width=12)
-> Hash Join (cost=70.75..100.50 rows=1000 width=12)
Hash Cond: (rt3.id = rt1.id)
-> Seq Scan on rt3 (cost=0.00..16.00 rows=1000 width=4)
-> Hash (cost=58.25..58.25 rows=1000 width=8)
-> Hash Join (cost=28.50..58.25 rows=1000 width=8)
Hash Cond: (rt2.id = rt1.id)
-> Seq Scan on rt2 (cost=0.00..16.00 rows=1000 width=4)
-> Hash (cost=16.00..16.00 rows=1000 width=4)
-> Seq Scan on rt1 (cost=0.00..16.00 rows=1000 width=4)
(15 rows)修改如下，可以很快的得到结果
postgres=# select distinct t2.id from
(
select distinct t1.id from
(select distinct rt1.id from rt1 join rt2 on rt1.id=rt2.id) t1
join
rt3 on t1.id=rt3.id
) t2
join rt4 on t2.id=rt4.id
;
id
----
1
(1 row)
Time: 2.052 ms
postgres=# explain select distinct t2.id from
postgres-# (
postgres(# select distinct t1.id from
postgres(# (select distinct rt1.id from rt1 join rt2 on rt1.id=rt2.id) t1
postgres(# join
postgres(# rt3 on t1.id=rt3.id
postgres(# ) t2
postgres-# join rt4 on t2.id=rt4.id
postgres-# ;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------
HashAggregate (cost=190.25..200.25 rows=1000 width=4)
Group Key: rt1.id
-> Hash Join (cost=158.00..187.75 rows=1000 width=4)
Hash Cond: (rt4.id = rt1.id)
-> Seq Scan on rt4 (cost=0.00..16.00 rows=1000 width=4)
-> Hash (cost=145.50..145.50 rows=1000 width=4)
-> HashAggregate (cost=125.50..135.50 rows=1000 width=4)
Group Key: rt1.id
-> Hash Join (cost=93.25..123.00 rows=1000 width=4)
Hash Cond: (rt3.id = rt1.id)
-> Seq Scan on rt3 (cost=0.00..16.00 rows=1000 width=4)
-> Hash (cost=80.75..80.75 rows=1000 width=4)
-> HashAggregate (cost=60.75..70.75 rows=1000 width=4)
Group Key: rt1.id
-> Hash Join (cost=28.50..58.25 rows=1000 width=4)
Hash Cond: (rt2.id = rt1.id)
-> Seq Scan on rt2 (cost=0.00..16.00 rows=1000 width=4)
-> Hash (cost=16.00..16.00 rows=1000 width=4)
-> Seq Scan on rt1 (cost=0.00..16.00 rows=1000 width=4)
(19 rows)
Time: 0.750 ms小结
这种SQL，如果要改内核的话，可以对统计信息进行分析（每个字段都有n_distinct），并对其进行query rewrite，得到同样的结果。
postgres=# \d pg_stats
View "pg_catalog.pg_stats"
Column | Type | Modifiers
------------------------+----------+-----------
schemaname | name |
tablename | name |
attname | name |
inherited | boolean |
null_frac | real |
avg_width | integer |
n_distinct | real |
most_common_vals | anyarray |
most_common_freqs | real[] |
histogram_bounds | anyarray |
correlation | real |
most_common_elems | anyarray |
most_common_elem_freqs | real[] |
elem_count_histogram | real[] | 祝大家玩得开心，欢迎随时来 阿里云促膝长谈业务需求 ，恭候光临。
阿里云的小伙伴们加油，努力 做好内核与服务，打造最贴地气的云数据库 。
