PostgreSQL利用递归优化求稀疏列唯一值的方法

2022-03-31 09:44 浏览：137

在数据库中经常会碰到一些表的列是稀疏列，只有很少的值，例如性别字段，一般就只有2种不同的值。

但是当我们求这些稀疏列的唯一值时，如果表的数据量很大，速度还是会很慢。

例如：

创建测试表

bill=# create table t_sex (sex char(1), otherinfo text);
CREATE TABLE
bill=# insert into t_sex select 'm', generate_series(1,10000000)||'this is test';
INSERT 0 10000000
bill=# insert into t_sex select 'w', generate_series(1,10000000)||'this is test';
INSERT 0 10000000

查询：

可以看到下面的查询速度很慢。

bill=# select count(distinct sex) from t_sex;
count
-------
2
(1 row)

Time: 8803.505 ms (00:08.804)
bill=# select sex from t_sex t group by sex;
sex
-----
m
w
(2 rows)

Time: 1026.464 ms (00:01.026)

那么我们对该字段加上索引又是什么情况呢？

速度依然没有明显

bill=# create index idx_sex_1 on t_sex(sex);
CREATE INDEX
bill=# select count(distinct sex) from t_sex;
count
-------
2
(1 row)

Time: 8502.460 ms (00:08.502)
bill=# select sex from t_sex t group by sex;
sex
-----
m
w
(2 rows)

Time: 572.353 ms

的变化，可以看到执行计划已经使用Index only Scan了。

bill=# explain select count(distinct sex) from t_sex;
QUERY PLAN
----------------------------------------------------------------------------------------------
Aggregate (cost=371996.44..371996.45 rows=1 width=8)
-> Index only Scan using idx_sex_1 on t_sex (cost=0.44..321996.44 rows=20000000 width=2)
(2 rows)

同样的SQL我们看看在Oracle中性能如何？

创建测试表：

SQL> create table t_sex (sex char(1), otherinfo varchar2(100));

Table created.

SQL> insert into t_sex select 'm', rownum||'this is test' from dual connect by level <=10000000;

10000000 rows created.

SQL> commit;

Commit complete.

SQL> insert into t_sex select 'w', rownum||'this is test' from dual connect by level <=10000000;

10000000 rows created.

SQL> commit;

Commit complete.

性能测试：

SQL> set lines 1000 pages 2000
SQL> set autotrace on
SQL> set timing on

SQL> select count(distinct sex) from t_sex;

COUNT(DISTINCTSEX)
------------------
2

Elapsed: 00:00:01.58

Execution Plan
----------------------------------------------------------
Plan hash value: 3915432945

----------------------------------------------------------------------------
| Id | Operation     | Name | Rows | Bytes | Cost (%CPU)| Time   |
----------------------------------------------------------------------------
| 0 | SELECT STATEMENT |    |   1 |   3 | 20132 (1)| 00:00:01 |
| 1 | SORT GROUP BY   |    |   1 |   3 |      |     |
| 2 | TABLE ACCESS FULL| T_SEX | 14M| 42M| 20132 (1)| 00:00:01 |
----------------------------------------------------------------------------

Note
-----
- dynamic statistics used: dynamic sampling (level=2)

Statistics
----------------------------------------------------------
     0 recursive calls
     0 db block gets
   74074 consistent gets
     0 physical reads
     0 redo size
    552 bytes sent via SQL*Net to client
    608 bytes received via SQL*Net from client
     2 SQL*Net roundtrips to/from client
     1 sorts (memory)
     0 sorts (disk)
     1 rows processed

SQL> select sex from t_sex t group by sex;

SE
--
m
w

Elapsed: 00:00:01.08

Execution Plan
----------------------------------------------------------
Plan hash value: 3915432945

----------------------------------------------------------------------------
| Id | Operation     | Name | Rows | Bytes | Cost (%CPU)| Time   |
----------------------------------------------------------------------------
| 0 | SELECT STATEMENT |    | 14M| 42M| 20558 (3)| 00:00:01 |
| 1 | SORT GROUP BY   |    | 14M| 42M| 20558 (3)| 00:00:01 |
| 2 | TABLE ACCESS FULL| T_SEX | 14M| 42M| 20132 (1)| 00:00:01 |
----------------------------------------------------------------------------

Note
-----
- dynamic statistics used: dynamic sampling (level=2)

Statistics
----------------------------------------------------------
     0 recursive calls
     0 db block gets
   74074 consistent gets
     0 physical reads
     0 redo size
    589 bytes sent via SQL*Net to client
    608 bytes received via SQL*Net from client
     2 SQL*Net roundtrips to/from client
     1 sorts (memory)
     0 sorts (disk)
     2 rows processed

可以看到Oracle的性能即使不加索引也明显比PostgreSQL中要好。

那么我们在PostgreSQL中是不是没办法继续优化了呢？这种情况我们利用pg中的递归语句结合索引可以大幅提升性能。

SQL改写：

bill=# with recursive tmp as (
bill(# (
bill(#   select min(t.sex) as sex from t_sex t where t.sex is not null
bill(# )
bill(# union all
bill(# (
bill(#   select (select min(t.sex) from t_sex t where t.sex > s.sex and t.sex is not null)
bill(#    from tmp s where s.sex is not null
bill(# )
bill(# )
bill-# select count(distinct sex) from tmp;
count
-------
   2
(1 row)

Time: 2.711 ms

查看执行计划：

bill=# explain with recursive tmp as (
bill(# (
bill(#   select min(t.sex) as sex from t_sex t where t.sex is not null
bill(# )
bill(# union all
bill(# (
bill(#   select (select min(t.sex) from t_sex t where t.sex > s.sex and t.sex is not null)
bill(#    from tmp s where s.sex is not null
bill(# )
bill(# )
bill-# select count(distinct sex) from tmp;
                           QUERY PLAN
----------------------------------------------------------------------------------------------------------------------
Aggregate (cost=53.62..53.63 rows=1 width=8)
CTE tmp
   -> Recursive Union (cost=0.46..51.35 rows=101 width=32)
      -> Result (cost=0.46..0.47 rows=1 width=32)
         InitPlan 3 (returns $1)
          -> Limit (cost=0.44..0.46 rows=1 width=2)
             -> Index only Scan using idx_sex_1 on t_sex t (cost=0.44..371996.44 rows=20000000 width=2)
                Index Cond: (sex IS NOT NULL)
      -> WorkTable Scan on tmp s (cost=0.00..4.89 rows=10 width=32)
         Filter: (sex IS NOT NULL)
-> CTE Scan on tmp (cost=0.00..2.02 rows=101 width=32)
(11 rows)