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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