PostgreSQL雕虫小技，分组TOP性能提升44倍

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业务配景

按分组取出top值,是非经常见的业务需求.

好比提取每位歌手的下载量top 10的曲目、提取每个城市纳税前10的人或企业.

传统办法

传统的办法是使用窗口查询,Postgresql是支持窗口查询的.

例子

测试表和测试数据,生成10000个分组,1000万条记录.

postgres=# create table tbl(c1 int,c2 int,c3 int);

CREATE table

postgres=# create index IDx1 on tbl(c1,c2);

CREATE INDEX

postgres=# insert into tbl select mod(trunc(random()*10000)::int,10000),trunc(random()*10000000) from generate_serIEs(1,10000000);

INSERT 0 10000000

使用窗口查询的执行计划

postgres=# explain select * from (select row_number() over(partition by c1 order by c2) as rn,* from tbl) t where t.rn<=10;

query PLAN

----------------------------------------------------------------------------------------

Subquery Scan on t (cost=0.43..770563.03 rows=3333326 wIDth=20)

Filter: (t.rn <= 10)

-> WindowAgg (cost=0.43..645563.31 rows=9999977 wIDth=12)

-> Index Scan using IDx1 on tbl (cost=0.43..470563.72 rows=9999977 wIDth=12)

(4 rows)

使用窗口查询的成果举例

postgres=# select * from (select row_number() over(partition by c1 order by c2) as rn,* from tbl) t where t.rn<=10;

rn | c1 | c2 | c3

----+------+--------+----

1 | 0 | 1657 |

2 | 0 | 3351 |

3 | 0 | 6347 |

4 | 0 | 12688 |

5 | 0 | 16991 |

6 | 0 | 19584 |

7 | 0 | 24694 |

8 | 0 | 36646 |

9 | 0 | 40882 |

10 | 0 | 41599 |

1 | 1 | 14465 |

2 | 1 | 29032 |

3 | 1 | 39969 |

4 | 1 | 41094 |

5 | 1 | 69481 |

6 | 1 | 70919 |

7 | 1 | 75575 |

8 | 1 | 81102 |

9 | 1 | 87496 |

10 | 1 | 90603 |

......

使用窗口查询的效率,20.1秒

postgres=# explain (analyze,verbose,costs,timing,buffers) select * from (select row_number() over(partition by c1 order by c2) as rn,* from tbl) t where t.rn<=10;

query PLAN

----------------------------------------------------------------------------------------------------------------------------------------------------

Subquery Scan on t (cost=0.43..770563.03 rows=3333326 wIDth=20) (actual time=0.040..20813.469 rows=100000 loops=1)

Output: t.rn,t.c1,t.c2,t.c3

Filter: (t.rn <= 10)

Rows Removed by Filter: 9900000

Buffers: shared hit=10035535

-> WindowAgg (cost=0.43..645563.31 rows=9999977 wIDth=12) (actual time=0.035..18268.027 rows=10000000 loops=1)

Output: row_number() OVER (?),tbl.c1,tbl.c2,tbl.c3

Buffers: shared hit=10035535

-> Index Scan using IDx1 on public.tbl (cost=0.43..470563.72 rows=9999977 wIDth=12) (actual time=0.026..11913.677 rows=10000000 loops=1)

Output: tbl.c1,tbl.c3

Buffers: shared hit=10035535

Planning time: 0.110 ms

Execution time: 20833.747 ms

(13 rows)

虫篆之技

如何优化?

可以参考我之前写的,使用递归查询,优化count distinct的办法.

https://yq.aliyun.com/articles/39689

(点击“阅读原文”,方可点击该连接进入哟!)

本文同样必要用到递归查询,获得分组ID：

postgres=# with recursive t1 as (

postgres(# (select min(c1) c1 from tbl )

postgres(# union all

postgres(# (select (select min(tbl.c1) c1 from tbl where tbl.c1>t.c1) c1 from t1 t where t.c1 is not null)

postgres(# )

postgres-# select * from t1;

写成SRF函数,如下：

postgres=# create or replace function f() returns setof tbl as $$

postgres$# declare

postgres$# v int;

postgres$# begin

postgres$# for v in with recursive t1 as (

postgres$# (select min(c1) c1 from tbl )

postgres$# union all

postgres$# (select (select min(tbl.c1) c1 from tbl where tbl.c1>t.c1) c1 from t1 t where t.c1 is not null)

postgres$# )

postgres$# select * from t1

postgres$# LOOP

postgres$# return query select * from tbl where c1=v order by c2 limit 10;

postgres$# END LOOP;

postgres$# return;

postgres$#

postgres$# end;

postgres$# $$ language plpgsql strict;

CREATE FUNCTION

优化后的查询成果例子

postgres=# select * from f();

c1 | c2 | c3

------+--------+----

0 | 1657 |

0 | 3351 |

0 | 6347 |

0 | 12688 |

0 | 16991 |

0 | 19584 |

0 | 24694 |

0 | 36646 |

0 | 40882 |

0 | 41599 |

1 | 14465 |

1 | 29032 |

1 | 39969 |

1 | 41094 |

1 | 69481 |

1 | 70919 |

1 | 75575 |

1 | 81102 |

1 | 87496 |

1 | 90603 |

......

优化后,只必要464毫秒返回10000个分组的top 10.

postgres=# explain (analyze,buffers) select * from f();

query PLAN

---------------------------------------------------------------------------------------------------------------------

Function Scan on public.f (cost=0.25..10.25 rows=1000 wIDth=12) (actual time=419.218..444.810 rows=100000 loops=1)

Output: c1,c2,c3

Function Call: f()

Buffers: shared hit=170407,temp read=221 written=220

Planning time: 0.037 ms

Execution time: 464.257 ms

(6 rows)

小 结

1)传统的办法使用窗口查询,输出多个每个分组的top 10,需要扫描所有的记录.效率较低.

2)由于分组不是非常多,只有10000个,所以可以选择使用递归的办法,用上索引取top 10,速度非常快.

3)目前Postgresql的递归语法不支持递归的启动表写在subquery里面,也不支持启动表在递归查询中使用order by,所以不能直接使用递归得出结果,目前必要套一层函数.

更多深度技术内容,请存眷云栖社区微信公众号：yunqiinsight.

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