业务背景

按分组取出TOP值，是非常常见的业务需求。

比如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。

传统方法

传统的方法是使用窗口查询，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)

使用窗口查询的结果举例

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.c2, tbl.c3
               Buffers: shared hit=10035535
 Planning time: 0.110 ms
 Execution time: 20833.747 ms
(13 rows)

雕虫小技

如何优化？

可以参考我之前写的，使用递归查询，优化count distinct的方法。

本文同样需要用到递归查询，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

优化后的查询结果例子

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，只需要464毫秒返回10000个分组的TOP 10。

postgres=# explain (analyze,verbose,timing,costs,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. 传统的方法使用窗口查询，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方法，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，目前需要套一层函数。