PostgreSQL利用递归优化求稀疏列唯一值的方法

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)

Time: 1.371 ms

可以看到执行时间从原先的8000ms降低到了2ms,提升了几千倍!

甚至对比Oracle,性能也是提升了很多。

但是需要注意的是:这种写法仅仅是针对稀疏列,换成数据分布广泛的字段,显然性能是下降的, 所以使用递归SQL不适合数据分布广泛的字段的group by或者count(distinct)操作。

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