8种常见的SQL错误用法

常见SQL错误用法

1. LIMIT 语句

分页查询是最常用的场景之一，但也通常也是最容易出问题的地方。比如对于下面简朴的语句，一样寻常DBA想到的办法是在type, name, create_time字段上加组合索引。如许条件排序都能有效的利用到索引，性能迅速提升。

1. <code >SELECT *
2. FROM operation
3. WHERE type = 'SQLStats'
4. AND name = 'SlowLog'
5. ORDER BY create_time
6. LIMIT 1000, 10;
7. </code>

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好吧，大概90%以上的DBA解决该问题就到此为止。但当 LIMIT 子句变成 “LIMIT 1000000,10” 时，程序员仍旧会诉苦：我只取10条记载为什么照旧慢？

要知道数据库也并不知道第1000000条记载从什么地方开始，纵然有索引也需要重新盘算一次。出现这种性能问题，多数情形下是程序员偷懒了。在前端数据浏览翻页，大概大数据分批导出等场景下，是可以将上一页的最大值当成参数作为查询条件的。SQL重新设计如下：

1. <code >SELECT *
2. FROM operation
3. WHERE type = 'SQLStats'
4. AND name = 'SlowLog'
5. AND create_time > '2019-10-19 14:00:00'
6. ORDER BY create_time limit 10;
7. </code>

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在新设计下查询时间基本固定，不会随着数据量的增长而发生厘革。

2. 隐式转换

SQL语句中查询变量和字段界说类型不匹配是另一个常见的错误。比如下面的语句：

1. <code >mysql> explain extended SELECT *
2. > FROM my_balance b
3. > WHERE b.bpn = 14000000123
4. > AND b.isverified IS NULL ;
5. mysql> show warnings;
6. | Warning | 1739 | Cannot use ref access on index 'bpn' due to type or collation conversion on field 'bpn'
7. </code>

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其中字段bpn的界说为varchar(20)，MySQL的策略是将字符串转换为数字之后再比较。函数作用于表字段，索引失效。

上述情况大概是应用程序框架主动填入的参数，而不是程序员的原意。现在应用框架很多很繁杂，利用方便的同时也警惕它大概给自己挖坑。

3. 关联更新、删除

虽然MySQL5.6引入了物化特性，但需要特别注意它现在仅仅针对查询语句的优化。对于更新或删除需要手工重写成JOIN。

比如下面UPDATE语句，MySQL实际实验的是循环/嵌套子查询（DEPENDENT SUBQUERY)，其实验时间可想而知。

1. <code >UPDATE operation o
2. SET status = 'applying'
3. WHERE o.id IN (SELECT id
4. FROM (SELECT o.id,
5. o.status
6. FROM operation o
7. WHERE o.group = 123
8. AND o.status NOT IN ( 'done' )
9. ORDER BY o.parent,
10. o.id
11. LIMIT 1) t);
12. </code>

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实验计划：

1. <code >+----+--------------------+-------+-------+---------------+---------+---------+-------+------+-----------------------------------------------------+
2. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
3. +----+--------------------+-------+-------+---------------+---------+---------+-------+------+-----------------------------------------------------+
4. | 1 | PRIMARY | o | index | | PRIMARY | 8 | | 24 | Using where; Using temporary |
5. | 2 | DEPENDENT SUBQUERY | | | | | | | | Impossible WHERE noticed after reading const tables |
6. | 3 | DERIVED | o | ref | idx_2,idx_5 | idx_5 | 8 | const | 1 | Using where; Using filesort |
7. +----+--------------------+-------+-------+---------------+---------+---------+-------+------+-----------------------------------------------------+
8. </code>

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重写为JOIN之后，子查询的选择模式从DEPENDENT SUBQUERY变成DERIVED,实验速率大大加速，从7秒低落到2毫秒。

1. <code >UPDATE operation o
2. JOIN (SELECT o.id,
3. o.status
4. FROM operation o
5. WHERE o.group = 123
6. AND o.status NOT IN ( 'done' )
7. ORDER BY o.parent,
8. o.id
9. LIMIT 1) t
10. ON o.id = t.id
11. SET status = 'applying'
12. </code>

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实验计划简化为：

1. <code >+----+-------------+-------+------+---------------+-------+---------+-------+------+-----------------------------------------------------+
2. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
3. +----+-------------+-------+------+---------------+-------+---------+-------+------+-----------------------------------------------------+
4. | 1 | PRIMARY | | | | | | | | Impossible WHERE noticed after reading const tables |
5. | 2 | DERIVED | o | ref | idx_2,idx_5 | idx_5 | 8 | const | 1 | Using where; Using filesort |
6. +----+-------------+-------+------+---------------+-------+---------+-------+------+-----------------------------------------------------+
7. </code>

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4. 混淆排序

MySQL不能利用索引进行混淆排序。但在某些场景，照旧有时机利用特别方法提升性能的。

1. <code >SELECT *
2. FROM my_order o
3. INNER JOIN my_appraise a ON a.orderid = o.id
4. ORDER BY a.is_reply ASC,
5. a.appraise_time DESC
6. LIMIT 0, 20
7. </code>

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实验计划显示为全表扫描：

1. <code >+----+-------------+-------+--------+-------------+---------+---------+---------------+---------+-+
2. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra
3. +----+-------------+-------+--------+-------------+---------+---------+---------------+---------+-+
4. | 1 | SIMPLE | a | ALL | idx_orderid | NULL | NULL | NULL | 1967647 | Using filesort |
5. | 1 | SIMPLE | o | eq_ref | PRIMARY | PRIMARY | 122 | a.orderid | 1 | NULL |
6. +----+-------------+-------+--------+---------+---------+---------+-----------------+---------+-+
7. </code>

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由于is_reply只有0和1两种状态，我们按照下面的方法重写后，实验时间从1.58秒低落到2毫秒。

1. <code >SELECT *
2. FROM ((SELECT *
3. FROM my_order o
4. INNER JOIN my_appraise a
5. ON a.orderid = o.id
6. AND is_reply = 0
7. ORDER BY appraise_time DESC
8. LIMIT 0, 20)
9. UNION ALL
10. (SELECT *
11. FROM my_order o
12. INNER JOIN my_appraise a
13. ON a.orderid = o.id
14. AND is_reply = 1
15. ORDER BY appraise_time DESC
16. LIMIT 0, 20)) t
17. ORDER BY is_reply ASC,
18. appraisetime DESC
19. LIMIT 20;
20. </code>

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5. EXISTS语句

MySQL对待EXISTS子句时，仍旧接纳嵌套子查询的实验方式。如下面的SQL语句：

1. <code >SELECT *
2. FROM my_neighbor n
3. LEFT JOIN my_neighbor_apply sra
4. ON n.id = sra.neighbor_id
5. AND sra.user_id = 'xxx'
6. WHERE n.topic_status < 4
7. AND EXISTS(SELECT 1
8. FROM message_info m
9. WHERE n.id = m.neighbor_id
10. AND m.inuser = 'xxx')
11. AND n.topic_type <> 5
12. </code>

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实验计划为：

1. <code >+----+--------------------+-------+------+-----+------------------------------------------+---------+-------+---------+ -----+
2. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
3. +----+--------------------+-------+------+ -----+------------------------------------------+---------+-------+---------+ -----+
4. | 1 | PRIMARY | n | ALL | | NULL | NULL | NULL | 1086041 | Using where |
5. | 1 | PRIMARY | sra | ref | | idx_user_id | 123 | const | 1 | Using where |
6. | 2 | DEPENDENT SUBQUERY | m | ref | | idx_message_info | 122 | const | 1 | Using index condition; Using where |
7. +----+--------------------+-------+------+ -----+------------------------------------------+---------+-------+---------+ -----+
8. </code>

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去掉exists更改为join，能够制止嵌套子查询，将实验时间从1.93秒低落为1毫秒。

1. <code >SELECT *
2. FROM my_neighbor n
3. INNER JOIN message_info m
4. ON n.id = m.neighbor_id
5. AND m.inuser = 'xxx'
6. LEFT JOIN my_neighbor_apply sra
7. ON n.id = sra.neighbor_id
8. AND sra.user_id = 'xxx'
9. WHERE n.topic_status < 4
10. AND n.topic_type <> 5
11. </code>

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新的实验计划：

1. <code >+----+-------------+-------+--------+ -----+------------------------------------------+---------+ -----+------+ -----+
2. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
3. +----+-------------+-------+--------+ -----+------------------------------------------+---------+ -----+------+ -----+
4. | 1 | SIMPLE | m | ref | | idx_message_info | 122 | const | 1 | Using index condition |
5. | 1 | SIMPLE | n | eq_ref | | PRIMARY | 122 | ighbor_id | 1 | Using where |
6. | 1 | SIMPLE | sra | ref | | idx_user_id | 123 | const | 1 | Using where |
7. +----+-------------+-------+--------+ -----+------------------------------------------+---------+ -----+------+ -----+
8. </code>

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6. 条件下推

外部查询条件不能够下推到复杂的视图或子查询的情况有：

1. 聚合子查询；
2. 含有LIMIT的子查询；
3. UNION 或UNION ALL子查询；
4. 输出字段中的子查询；

如下面的语句，从实验计划可以看出其条件作用于聚合子查询之后：

1. <code >SELECT *
2. FROM (SELECT target,
3. Count(*)
4. FROM operation
5. GROUP BY target) t
6. WHERE target = 'rm-xxxx'
7. </code>

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1. <code >+----+-------------+------------+-------+---------------+-------------+---------+-------+------+-------------+
2. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
3. +----+-------------+------------+-------+---------------+-------------+---------+-------+------+-------------+
4. | 1 | PRIMARY | <derived2> | ref | <auto_key0> | <auto_key0> | 514 | const | 2 | Using where |
5. | 2 | DERIVED | operation | index | idx_4 | idx_4 | 519 | NULL | 20 | Using index |
6. +----+-------------+------------+-------+---------------+-------------+---------+-------+------+-------------+
7. </code>

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确定从语义上查询条件可以直接下推后，重写如下：

1. <code >SELECT target,
2. Count(*)
3. FROM operation
4. WHERE target = 'rm-xxxx'
5. GROUP BY target
6. </code>

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实验计划变为：

1. <code >+----+-------------+-----------+------+---------------+-------+---------+-------+------+--------------------+
2. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
3. +----+-------------+-----------+------+---------------+-------+---------+-------+------+--------------------+
4. | 1 | SIMPLE | operation | ref | idx_4 | idx_4 | 514 | const | 1 | Using where; Using index |
5. +----+-------------+-----------+------+---------------+-------+---------+-------+------+--------------------+
6. </code>

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关于MySQL外部条件不能下推的详细表明说明请参考从前文章：MySQL · 性能优化 · 条件下推到物化表

7. 提前缩小范围

先上初始SQL语句：

1. <code >SELECT *
2. FROM my_order o
3. LEFT JOIN my_userinfo u
4. ON o.uid = u.uid
5. LEFT JOIN my_productinfo p
6. ON o.pid = p.pid
7. WHERE ( o.display = 0 )
8. AND ( o.ostaus = 1 )
9. ORDER BY o.selltime DESC
10. LIMIT 0, 15
11. </code>

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该SQL语句原意是：先做一系列的左连接，然后排序取前15条记载。从实验计划也可以看出，最后一步估算排序记载数为90万，时间斲丧为12秒。

1. <code >+----+-------------+-------+--------+---------------+---------+---------+-----------------+--------+----------------------------------------------------+
2. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
3. +----+-------------+-------+--------+---------------+---------+---------+-----------------+--------+----------------------------------------------------+
4. | 1 | SIMPLE | o | ALL | NULL | NULL | NULL | NULL | 909119 | Using where; Using temporary; Using filesort |
5. | 1 | SIMPLE | u | eq_ref | PRIMARY | PRIMARY | 4 | o.uid | 1 | NULL |
6. | 1 | SIMPLE | p | ALL | PRIMARY | NULL | NULL | NULL | 6 | Using where; Using join buffer (Block Nested Loop) |
7. +----+-------------+-------+--------+---------------+---------+---------+-----------------+--------+----------------------------------------------------+
8. </code>

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由于最后WHERE条件以及排序均针对最左主表，因此可以先对my_order排序提前缩小数据量再做左连接。SQL重写后如下，实验时间缩小为1毫秒左右。

1. <code >SELECT *
2. FROM (
3. SELECT *
4. FROM my_order o
5. WHERE ( o.display = 0 )
6. AND ( o.ostaus = 1 )
7. ORDER BY o.selltime DESC
8. LIMIT 0, 15
9. ) o
10. LEFT JOIN my_userinfo u
11. ON o.uid = u.uid
12. LEFT JOIN my_productinfo p
13. ON o.pid = p.pid
14. ORDER BY o.selltime DESC
15. limit 0, 15
16. </code>

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再查抄实验计划：子查询物化后（select_type=DERIVED)参与JOIN。虽然估算行扫描仍旧为90万，但是利用了索引以及LIMIT 子句后，实际实验时间变得很小。

1. <code >
2. +----+-------------+------------+--------+---------------+---------+---------+-------+--------+----------------------------------------------------+
3. | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
4. +----+-------------+------------+--------+---------------+---------+---------+-------+--------+----------------------------------------------------+
5. | 1 | PRIMARY | <derived2> | ALL | NULL | NULL | NULL | NULL | 15 | Using temporary; Using filesort |
6. | 1 | PRIMARY | u | eq_ref | PRIMARY | PRIMARY | 4 | o.uid | 1 | NULL |
7. | 1 | PRIMARY | p | ALL | PRIMARY | NULL | NULL | NULL | 6 | Using where; Using join buffer (Block Nested Loop) |
8. | 2 | DERIVED | o | index | NULL | idx_1 | 5 | NULL | 909112 | Using where |
9. +----+-------------+------------+--------+---------------+---------+---------+-------+--------+----------------------------------------------------+
10. </code>

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8. 中心结果集下推

再来看下面这个已经初步优化过的例子(左连接中的主表优先作用查询条件)：

1. <code >SELECT a.*,
2. c.allocated
3. FROM (
4. SELECT resourceid
5. FROM my_distribute d
6. WHERE isdelete = 0
7. AND cusmanagercode = '1234567'
8. ORDER BY salecode limit 20) a
9. LEFT JOIN
10. (
11. SELECT resourcesid， sum(ifnull(allocation, 0) * 12345) allocated
12. FROM my_resources
13. GROUP BY resourcesid) c
14. ON a.resourceid = c.resourcesid
15. </code>

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那么该语句还存在别的问题吗？不丢脸出子查询 c 是全表聚合查询，在表数量特别大的情况下会导致整个语句的性能降落。

着实对于子查询 c，左连接最后结果集只关心能和主表resourceid能匹配的数据。因此我们可以重写语句如下，实验时间从原来的2秒降落到2毫秒。

1. <code >SELECT a.*,
2. c.allocated
3. FROM (
4. SELECT resourceid
5. FROM my_distribute d
6. WHERE isdelete = 0
7. AND cusmanagercode = '1234567'
8. ORDER BY salecode limit 20) a
9. LEFT JOIN
10. (
11. SELECT resourcesid， sum(ifnull(allocation, 0) * 12345) allocated
12. FROM my_resources r,
13. (
14. SELECT resourceid
15. FROM my_distribute d
16. WHERE isdelete = 0
17. AND cusmanagercode = '1234567'
18. ORDER BY salecode limit 20) a
19. WHERE r.resourcesid = a.resourcesid
20. GROUP BY resourcesid) c
21. ON a.resourceid = c.resourcesid
22. </code>

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但是子查询 a 在我们的SQL语句中出现了多次。这种写法不光存在额外的开销，还使得整个语句显的繁杂。利用WITH语句再次重写：

1. <code >WITH a AS
2. (
3. SELECT resourceid
4. FROM my_distribute d
5. WHERE isdelete = 0
6. AND cusmanagercode = '1234567'
7. ORDER BY salecode limit 20)
8. SELECT a.*,
9. c.allocated
10. FROM a
11. LEFT JOIN
12. (
13. SELECT resourcesid， sum(ifnull(allocation, 0) * 12345) allocated
14. FROM my_resources r,
15. a
16. WHERE r.resourcesid = a.resourcesid
17. GROUP BY resourcesid) c
18. ON a.resourceid = c.resourcesid
19. </code>

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总结

1. 数据库编译器产生实验计划，决定着SQL的实际实验方式。但是编译器只是尽力服务，全部数据库的编译器都不是精致绝伦的。上述提到的多数场景，在别的数据库中也存在性能问题。相识数据库编译器的特性，才能避规其短处，写出高性能的SQL语句。
2. 程序员在设计数据模子以及编写SQL语句时，要把算法的头脑或意识带进来。
3. 编写复杂SQL语句要养成利用WITH语句的习惯。简便且思绪清晰的SQL语句也能减小数据库的负担 ^^。

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来源：https://www.cnblogs.com/cxydmx/p/11728419.html