PostgreSQL如何利用递归优化求稀疏列唯一值的代码举例
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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