当前位置：首页 > 资讯 > 技术文档 > 数据库

SQL优化案例（2）：OR条件优化

时间：2020-10-27 19:16 编辑：云掣YUNCHE 来源：阅读：71
扫一扫，手机访问

摘要：

接下来上一篇文章《 SQL优化案例（1）：隐式转换》的介绍，此处内容围绕OR的优化展开。

在MySQL中，同样的查询条件，如果变换OR在SQL语句中的位置，那么查询的结果也会有差异，在多个复杂的情况下，可能会带来索引选择不佳的性能隐患，为了避免执行效率大幅度下降的问题，我们可以适当考虑使用统一所有对查询逻辑复杂的SQL进行分离。

常见OR使用场景，请阅读以下案例。

案例一：不同列使用OR条件查询

1.待优化场景

SELECT
..
..
 FROM`t1` a
 WHERE a.token= '16149684'
 AND a.store_id= '242950'
 AND(a.registrationId IS NOT NULL
 AND a.registrationId<> '')
 OR a.uid= 308475
 AND a.registrationId IS NOT NULL
 AND a.registrationId<> ''
1
2
3
4
5
6
7
8
9
10
11

执行计划

+--------------+-----------------------+-----------------+----------------+-------------------+-------------------+---------------+----------------+---------------------------------------------+
| id           | select_type           | table           | type           | key               | key_len           | ref           | rows           | Extra                                       |
+--------------+-----------------------+-----------------+----------------+-------------------+-------------------+---------------+----------------+---------------------------------------------+
| 1            | SIMPLE                | a               | range          |idx_registrationid | 99                |               | 100445         | Using index condition; Using where          |
+--------------+-----------------------+-----------------+----------------+-------------------+-------------------+---------------+----------------+---------------------------------------------+
1
2
3
4
5

共返回1行记录，花费 5 ms 。

2.场景解析

从查询条件中可以研磨令牌和uid过滤性都非常好，但是由于使用了，或者，需要采用索引合并的方法才能获得比较好的性能。但在实际执行过程中MySQL优化器替代选择了使用registrationId的索引，导致SQL的性能很差。

3.场景优化

我们将SQL改写成union all的形式。

SELECT
...
...
FROM`t1` a
WHERE a.token = '16054473'
AND a.store_id = '138343'
AND b.is_refund = 1
AND (a.registrationId IS NOT NULL
AND a.registrationId <> '')
union all
SELECT
...
...
FROM`t1` a
where a.uid = 181579
AND a.registrationId IS NOT NULL
AND a.registrationId <> ''
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

+--------------+-----------------------+-----------------+----------------+------------------------------+---------------+-------------------+------------------------------+----------------+------------------------------------+
| id           | select_type           | table           | type           | possible_keys                | key           | key_len           | ref                          | rows           | Extra                              |
+--------------+-----------------------+-----------------+----------------+------------------------------+---------------+-------------------+------------------------------+----------------+------------------------------------+
| 1            | PRIMARY               | a               | ref            | IDX_TOKEN,IDX_STORE_ID_TOKEN | IDX_TOKEN     | 63                | const                        | 1              | Using index condition; Using where |
| 1            | PRIMARY               | b               | eq_ref         | PRIMARY                      | PRIMARY       | 4                 | youdian_life_sewsq.a.role_id | 1              | Using where                        |
| 2            | UNION                 | a               | const          | PRIMARY                      | PRIMARY       | 4                 | const                        | 1              |                                    |
| 2            | UNION                 | b               | const          | PRIMARY                      | PRIMARY       | 4                 | const                        | 0              | unique row not found               |
|              | UNION RESULT          | <union1,2>      | ALL            |                              |               |                   |                              |                | Using temporary                    |
+--------------+-----------------------+-----------------+----------------+------------------------------+---------------+-------------------+------------------------------+----------------+------------------------------------+
1
2
3
4
5
6
7
8
9

共返回5行记录，花费 5 ms 。

通过对比优化前后的执行计划，可以明显修剪，将SQL合并成两个子查询，再使用union对结果进行合并，稳定性和安全性更好，性能更高。

案例二：同一列使用OR查询条件

1.待优化场景

select
....
....
from
t1 as mci
left join t1 as ccv2_1 on ccv2_1.unique_no = mci=category_no1
left join t1 as ccv2_2 on ccv2_2.unique_no = mci=category_no2
left join t1 as ccv2_3 on ccv2_3.unique_no = mci=category_no3
left join(
 select product_id,
 count(0) count
 from t2 pprod
 inner join t3 pinfo on pinfo.promotion_id = pprod.promotion_id
 and pprod.is_enable =1
 and ppinfo.is_enable=1
 and pinfo.belong_t0 =1
 and pinfo.end_time >=now()
 and not (
 pinfo.onshelv_time>'2019-06-30 00:00:00'
 or pinfo.end_time>'2018-12-05 00:00:00'
 )group by pprod.product_id
)as pc on pc.product_id = mci.product_id
where mci.is_enable =0
and mci.comodifty_type in ('1', '5', '6')
and (pc.count =0 or pc.count isnull ) limit 0,5;
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

执行计划

2.场景解析

本例的SQL查询中有一个子查询，子查询被当成成驱动表，产生了auto_key，通过SQL进行进行测试，验证主要是（pc.count = 0或pc.count为null）会影响到整个SQL的性能，需要进行比较改写。

3.场景优化

首先我们可以单独思考（pc.count = 0或pc.count为null）如何进行优化？先写一个类似的SQL

Select col from test where col =100 or col is null;
+--------+
| col    |
+--------+
|    100 |
|   NULL |
+--------+
2 rows in set (0.00 sec)
1
2
3
4
5
6
7
8

这个时候我们看到的其实是同一个列，但对应不同的值，这种情况可以利用case when进行转换。

Select col From test where case when col is null then 100 else col =100 end;
+--------+
| col    |
+--------+
|    100 |
|   NULL |
+--------+
2 rows in set (0.00 sec)
1
2
3
4
5
6
7
8

再回到原始SQL进行改写。

select
....
....
from
t1 as mci
left join t1 as ccv2_1 on ccv2_1.unique_no = mci=category_no1
left join t1 as ccv2_2 on ccv2_2.unique_no = mci=category_no2
left join t1 as ccv2_3 on ccv2_3.unique_no = mci=category_no3
left join(
 select product_id,
 count(0) count
 from t2 pprod
 inner join t3 pinfo on pinfo.promotion_id = pprod.promotion_id
 and pprod.is_enable =1
 and ppinfo.is_enable=1
 and pinfo.belong_t0 =1
 and pinfo.end_time >=now()
 and not (
 pinfo.onshelv_time>'2019-06-30 00:00:00'
 or pinfo.end_time>'2018-12-05 00:00:00'
 )group by pprod.product_id
)as pc on pc.product_id = mci.product_id
where mci.is_enable =0
and mci.comodifty_type in ('1', '5', '6')
and case when pc.count is null then 0 else pc.count end=0 limit 0,5;
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

可以抛光优化后的SQL比原始SQL快了30秒，执行效率提升约50倍。

案例三：优化关联SQL OR条件

1.待优化场景

SELECT user_msg.msg_id AS ‘msg_id’, user_msg.content AS ‘msg_content’, …
FROM user_msg
LEFT JOIN user ON user_msg.user_id = user.user_id
LEFT JOIN group ON user_msg.group_id = group.group_id
WHERE user_msg.gmt_modified >= date_sub('2018-03-29 09:31:44', INTERVAL30SECOND)
OR user.gmt_modified >= date_sub('2018-03-29 09:31:44', INTERVAL 30 SECOND)
OR group.gmt_modified >= date_sub('2018-03-29 09:31:44', INTERVAL 30 SECOND)
1
2
3
4
5
6
7

2.场景解析

我们仔细分析上述查询语句，发现虽然业务逻辑只需要查询半分钟内修改的数据，但执行过程却必须对所有的数据进行关联操作，带来的性能损失。

3.场景优化

我们对原始SQL进行分解操作，第一部分sql-01如下：

SELECT user_msg.msg_id AS ‘msg_id’, user_msg.content AS ‘msg_content’, …
FROM user_msg
LEFT JOIN user ON user_msg.user_id = user.user_id
LEFT JOIN group ON user_msg.group_id = group.group_id
WHERE user_msg.gmt_modified >= date_sub('2018-03-29 09:31:44', INTERVAL 30 SECOND)
1
2
3
4
5

sql-01以user_msg表为驱动，使用gmt_modified索引过滤最新数据。

第二部分sql-02如下：

SELECT user_msg.msg_id AS ‘msg_id’, user_msg.content AS ‘msg_content’, …
FROM user_msg
LEFT JOIN user ON user_msg.user_id = user.user_id
LEFT JOIN group ON user_msg.group_id = group.group_id
WHERE user.gmt_modified >= date_sub('2018-03-29 09:31:44', INTERVAL 30 SECOND)
1
2
3
4
5

sql-02以用户为驱动表，msg user_id的索引过滤行很好。

第三部分sql-03如下：

SELECT user_msg.msg_id AS ‘msg_id’, user_msg.content AS ‘msg_content’, …
FROM user_msg
LEFT JOIN user ON user_msg.user_id = user.user_id
LEFT JOIN group ON user_msg.group_id = group.group_id
WHERE group.gmt_modified >= date_sub('2018-03-29 09:31:44', INTERVAL 30 SECOND)
1
2
3
4
5