一 单表查询的语法
SELECT 字段1,字段2... FROM 表名
WHERE 条件
GROUP BY field
HAVING 筛选
ORDER BY field
LIMIT 限制条数
二 关键字的执行优先级(重点)
重点中的重点:关键字的执行优先级
from #从库中找到某张表
where #用where约束条件从表中取出符合条件的数据
group by #将取出的一条条记录进行分组group by,如果没有group by,则整体作为一组
having #将分组的结果进行having过滤
select #执行select
distinct #去重
order by #将结果按条件排序:order by
limit #限制结果的显示条数
三SELECT语句关键字的定义顺序
SELECT DISTINCT <select_list>
FROM <left_table>
<join_type> JOIN <right_table>
ON <join_condition>
WHERE <where_condition>
GROUP BY <group_by_list>
HAVING <having_condition>
ORDER BY <order_by_condition>
LIMIT <limit_number>
四 SELECT语句关键字的执行顺序
(7) SELECT
(8) DISTINCT <select_list>
(1) FROM <left_table>
(3) <join_type> JOIN <right_table>
(2) ON <join_condition>
(4) WHERE <where_condition>
(5) GROUP BY <group_by_list>
(6) HAVING <having_condition>
(9) ORDER BY <order_by_condition>
(10) LIMIT <limit_number>
五 测试sql语句的执行顺序
1. 新建一个测试数据库TestDB;
create database TestDB;
2.创建测试表table1和table2;
CREATE TABLE table1
(
customer_id VARCHAR(10) NOT NULL,
city VARCHAR(10) NOT NULL,
PRIMARY KEY(customer_id)
)ENGINE=INNODB DEFAULT CHARSET=UTF8;
CREATE TABLE table2
(
order_id INT NOT NULL auto_increment,
customer_id VARCHAR(10),
PRIMARY KEY(order_id)
)ENGINE=INNODB DEFAULT CHARSET=UTF8;
3.插入测试数据;
INSERT INTO table1(customer_id,city) VALUES('163','hangzhou');
INSERT INTO table1(customer_id,city) VALUES('9you','shanghai');
INSERT INTO table1(customer_id,city) VALUES('tx','hangzhou');
INSERT INTO table1(customer_id,city) VALUES('baidu','hangzhou');
INSERT INTO table2(customer_id) VALUES('163');
INSERT INTO table2(customer_id) VALUES('163');
INSERT INTO table2(customer_id) VALUES('9you');
INSERT INTO table2(customer_id) VALUES('9you');
INSERT INTO table2(customer_id) VALUES('9you');
INSERT INTO table2(customer_id) VALUES('tx');
INSERT INTO table2(customer_id) VALUES(NULL);
注释:准备工作做完以后,table1和table2看起来应该像下面这样:
mysql> select * from table1;
+-------------+----------+
| customer_id | city |
+-------------+----------+
| 163 | hangzhou |
| 9you | shanghai |
| baidu | hangzhou |
| tx | hangzhou |
+-------------+----------+
4 rows in set (0.00 sec)
mysql> select * from table2;
+----------+-------------+
| order_id | customer_id |
+----------+-------------+
| 1 | 163 |
| 2 | 163 |
| 3 | 9you |
| 4 | 9you |
| 5 | 9you |
| 6 | tx |
| 7 | NULL |
+----------+-------------+
7 rows in set (0.00 sec)
4、准备SQL逻辑查询测试语句
#查询来自杭州,并且订单数少于2的客户。
SELECT a.customer_id, COUNT(b.order_id) as total_orders
FROM table1 AS a
LEFT JOIN table2 AS b
ON a.customer_id = b.customer_id
WHERE a.city = 'hangzhou'
GROUP BY a.customer_id
HAVING count(b.order_id) < 2
ORDER BY total_orders DESC;
六 执行顺序分析
在这些SQL语句的执行过程中,都会产生一个虚拟表,用来保存SQL语句的执行结果(这是重点),我现在就来跟踪这个虚拟表的变化,得到最终的查询结果的过程,来分析整个SQL逻辑查询的执行顺序和过程。
1、执行FROM语句
第一步,执行FROM
语句。我们首先需要知道最开始从哪个表开始的,这就是FROM
告诉我们的。现在有了<left_table>
和<right_table>
两个表,我们到底从哪个表开始,还是从两个表进行某种联系以后再开始呢?它们之间如何产生联系呢?——笛卡尔积
关于什么是笛卡尔积,请自行Google补脑。经过FROM语句对两个表执行笛卡尔积,会得到一个虚拟表,暂且叫VT1(vitual table 1),内容如下:
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| 163 | hangzhou | 1 | 163 |
| 9you | shanghai | 1 | 163 |
| baidu | hangzhou | 1 | 163 |
| tx | hangzhou | 1 | 163 |
| 163 | hangzhou | 2 | 163 |
| 9you | shanghai | 2 | 163 |
| baidu | hangzhou | 2 | 163 |
| tx | hangzhou | 2 | 163 |
| 163 | hangzhou | 3 | 9you |
| 9you | shanghai | 3 | 9you |
| baidu | hangzhou | 3 | 9you |
| tx | hangzhou | 3 | 9you |
| 163 | hangzhou | 4 | 9you |
| 9you | shanghai | 4 | 9you |
| baidu | hangzhou | 4 | 9you |
| tx | hangzhou | 4 | 9you |
| 163 | hangzhou | 5 | 9you |
| 9you | shanghai | 5 | 9you |
| baidu | hangzhou | 5 | 9you |
| tx | hangzhou | 5 | 9you |
| 163 | hangzhou | 6 | tx |
| 9you | shanghai | 6 | tx |
| baidu | hangzhou | 6 | tx |
| tx | hangzhou | 6 | tx |
| 163 | hangzhou | 7 | NULL |
| 9you | shanghai | 7 | NULL |
| baidu | hangzhou | 7 | NULL |
| tx | hangzhou | 7 | NULL |
+-------------+----------+----------+-------------+
总共有28(table1的记录条数 * table2的记录条数)条记录。这就是VT1的结果,接下来的操作就在VT1的基础上进行。
2、执行ON过滤
执行完笛卡尔积以后,接着就进行ON a.customer_id = b.customer_id
条件过滤,根据ON
中指定的条件,去掉那些不符合条件的数据,得到VT2表,内容如下:
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| 163 | hangzhou | 1 | 163 |
| 163 | hangzhou | 2 | 163 |
| 9you | shanghai | 3 | 9you |
| 9you | shanghai | 4 | 9you |
| 9you | shanghai | 5 | 9you |
| tx | hangzhou | 6 | tx |
+-------------+----------+----------+-------------+
VT2就是经过ON
条件筛选以后得到的有用数据,而接下来的操作将在VT2的基础上继续进行。
3、添加外部行
这一步只有在连接类型为OUTER JOIN
时才发生,如LEFT OUTER JOIN
、RIGHT OUTER JOIN
和FULL OUTER JOIN
。在大多数的时候,我们都是会省略掉OUTER
关键字的,但OUTER
表示的就是外部行的概念。
LEFT OUTER JOIN
把左表记为保留表,得到的结果为:
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| 163 | hangzhou | 1 | 163 |
| 163 | hangzhou | 2 | 163 |
| 9you | shanghai | 3 | 9you |
| 9you | shanghai | 4 | 9you |
| 9you | shanghai | 5 | 9you |
| tx | hangzhou | 6 | tx |
| baidu | hangzhou | NULL | NULL |
+-------------+----------+----------+-------------+
RIGHT OUTER JOIN
把右表记为保留表,得到的结果为:
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| 163 | hangzhou | 1 | 163 |
| 163 | hangzhou | 2 | 163 |
| 9you | shanghai | 3 | 9you |
| 9you | shanghai | 4 | 9you |
| 9you | shanghai | 5 | 9you |
| tx | hangzhou | 6 | tx |
| NULL | NULL | 7 | NULL |
+-------------+----------+----------+-------------+
FULL OUTER JOIN
把左右表都作为保留表,得到的结果为:
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| 163 | hangzhou | 1 | 163 |
| 163 | hangzhou | 2 | 163 |
| 9you | shanghai | 3 | 9you |
| 9you | shanghai | 4 | 9you |
| 9you | shanghai | 5 | 9you |
| tx | hangzhou | 6 | tx |
| baidu | hangzhou | NULL | NULL |
| NULL | NULL | 7 | NULL |
+-------------+----------+----------+-------------+
添加外部行的工作就是在VT2表的基础上添加保留表中被过滤条件过滤掉的数据,非保留表中的数据被赋予NULL值,最后生成虚拟表VT3。
由于我在准备的测试SQL查询逻辑语句中使用的是LEFT JOIN
,过滤掉了以下这条数据:
| baidu | hangzhou | NULL | NULL |
现在就把这条数据添加到VT2表中,得到的VT3表如下:
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| 163 | hangzhou | 1 | 163 |
| 163 | hangzhou | 2 | 163 |
| 9you | shanghai | 3 | 9you |
| 9you | shanghai | 4 | 9you |
| 9you | shanghai | 5 | 9you |
| tx | hangzhou | 6 | tx |
| baidu | hangzhou | NULL | NULL |
+-------------+----------+----------+-------------+
接下来的操作都会在该VT3表上进行。
4、执行WHERE过滤
对添加外部行得到的VT3进行WHERE过滤,只有符合<where_condition>的记录才会输出到虚拟表VT4中。当我们执行WHERE a.city = 'hangzhou'
的时候,就会得到以下内容,并存在虚拟表VT4中:
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| 163 | hangzhou | 1 | 163 |
| 163 | hangzhou | 2 | 163 |
| tx | hangzhou | 6 | tx |
| baidu | hangzhou | NULL | NULL |
+-------------+----------+----------+-------------+
复制代码
但是在使用WHERE子句时,需要注意以下两点:
- 由于数据还没有分组,因此现在还不能在WHERE过滤器中使用
where_condition=MIN(col)
这类对分组统计的过滤; - 由于还没有进行列的选取操作,因此在SELECT中使用列的别名也是不被允许的,如:
SELECT city as c FROM t WHERE c='shanghai';
是不允许出现的。
5、执行GROUP BY分组
GROU BY
子句主要是对使用WHERE
子句得到的虚拟表进行分组操作。我们执行测试语句中的GROUP BY a.customer_id
,就会得到以下内容(默认只显示组内第一条):
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| 163 | hangzhou | 1 | 163 |
| baidu | hangzhou | NULL | NULL |
| tx | hangzhou | 6 | tx |
+-------------+----------+----------+-------------+
得到的内容会存入虚拟表VT5中,此时,我们就得到了一个VT5虚拟表,接下来的操作都会在该表上完成。
6、执行HAVING过滤
HAVING
子句主要和GROUP BY
子句配合使用,对分组得到的VT5虚拟表进行条件过滤。当我执行测试语句中的HAVING count(b.order_id) < 2
时,将得到以下内容:
+-------------+----------+----------+-------------+
| customer_id | city | order_id | customer_id |
+-------------+----------+----------+-------------+
| baidu | hangzhou | NULL | NULL |
| tx | hangzhou | 6 | tx |
+-------------+----------+----------+-------------+
这就是虚拟表VT6。
7、SELECT列表
现在才会执行到SELECT
子句,不要以为SELECT
子句被写在第一行,就是第一个被执行的。
我们执行测试语句中的SELECT a.customer_id, COUNT(b.order_id) as total_orders
,从虚拟表VT6中选择出我们需要的内容。我们将得到以下内容:
+-------------+--------------+
| customer_id | total_orders |
+-------------+--------------+
| baidu | 0 |
| tx | 1 |
+-------------+--------------+
还没有完,这只是虚拟表VT7。
8、执行DISTINCT子句
如果在查询中指定了DISTINCT
子句,则会创建一张内存临时表(如果内存放不下,就需要存放在硬盘了)。这张临时表的表结构和上一步产生的虚拟表VT7是一样的,不同的是对进行DISTINCT操作的列增加了一个唯一索引,以此来除重复数据。
由于我的测试SQL语句中并没有使用DISTINCT,所以,在该查询中,这一步不会生成一个虚拟表。
9、执行ORDER BY子句
对虚拟表中的内容按照指定的列进行排序,然后返回一个新的虚拟表,我们执行测试SQL语句中的ORDER BY total_orders DESC
,就会得到以下内容:
+-------------+--------------+
| customer_id | total_orders |
+-------------+--------------+
| tx | 1 |
| baidu | 0 |
+-------------+--------------+
可以看到这是对total_orders列进行降序排列的。上述结果会存储在VT8中。
10、执行LIMIT子句
LIMIT
子句从上一步得到的VT8虚拟表中选出从指定位置开始的指定行数据。对于没有应用ORDER BY的LIMIT子句,得到的结果同样是无序的,所以,很多时候,我们都会看到LIMIT子句会和ORDER BY子句一起使用。
MySQL数据库的LIMIT支持如下形式的选择:
LIMIT n, m
表示从第n条记录开始选择m条记录。而很多开发人员喜欢使用该语句来解决分页问题。对于小数据,使用LIMIT子句没有任何问题,当数据量非常大的时候,使用LIMIT n, m
是非常低效的。因为LIMIT的机制是每次都是从头开始扫描,如果需要从第60万行开始,读取3条数据,就需要先扫描定位到60万行,然后再进行读取,而扫描的过程是一个非常低效的过程。所以,对于大数据处理时,是非常有必要在应用层建立一定的缓存机制(现在的大数据处理,大都使用缓存)
八、sql语句的具体用法
1、 简单查询
company.employee
员工id id int
姓名 emp_name varchar
性别 sex enum
年龄 age int
入职日期 hire_date date
岗位 post varchar
职位描述 post_comment varchar
薪水 salary double
办公室 office int
部门编号 depart_id int
#创建表
create table employee(
id int not null unique auto_increment,
name varchar(20) not null,
sex enum('male','female') not null default 'male', #大部分是男的
age int(3) unsigned not null default 28,
hire_date date not null,
post varchar(50),
post_comment varchar(100),
salary double(15,2),
office int, #一个部门一个屋子
depart_id int
);
#查看表结构
mysql> desc employee;
+--------------+-----------------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+--------------+-----------------------+------+-----+---------+----------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| name | varchar(20) | NO | | NULL | |
| sex | enum('male','female') | NO | | male | |
| age | int(3) unsigned | NO | | 28 | |
| hire_date | date | NO | | NULL | |
| post | varchar(50) | YES | | NULL | |
| post_comment | varchar(100) | YES | | NULL | |
| salary | double(15,2) | YES | | NULL | |
| office | int(11) | YES | | NULL | |
| depart_id | int(11) | YES | | NULL | |
+--------------+-----------------------+------+-----+---------+----------------+
#插入记录
#三个部门:教学,销售,运营
insert into employee(name,sex,age,hire_date,post,salary,office,depart_id) values
('egon','male',18,'20170301','老男孩驻沙河办事处外交大使',7300.33,401,1), #以下是教学部
('alex','male',78,'20150302','teacher',1000000.31,401,1),
('wupeiqi','male',81,'20130305','teacher',8300,401,1),
('yuanhao','male',73,'20140701','teacher',3500,401,1),
('liwenzhou','male',28,'20121101','teacher',2100,401,1),
('jingliyang','female',18,'20110211','teacher',9000,401,1),
('jinxin','male',18,'19000301','teacher',30000,401,1),
('成龙','male',48,'20101111','teacher',10000,401,1),
('歪歪','female',48,'20150311','sale',3000.13,402,2),#以下是销售部门
('丫丫','female',38,'20101101','sale',2000.35,402,2),
('丁丁','female',18,'20110312','sale
',1000.37,402,2),
('星星','female',18,'20160513','sale',3000.29,402,2),
('格格','female',28,'20170127','sale',4000.33,402,2),
('张野','male',28,'20160311','operation',10000.13,403,3), #以下是运营部门
('程咬金','male',18,'19970312','operation',20000,403,3),
('程咬银','female',18,'20130311','operation',19000,403,3),
('程咬铜','male',18,'20150411','operation',18000,403,3),
('程咬铁','female',18,'20140512','operation',17000,403,3)
;
#ps:如果在windows系统中,插入中文字符,select的结果为空白,可以将所有字符编码统一设置成g
#简单查询
SELECT id,name,sex,age,hire_date,post,post_comment,salary,office,depart_id
FROM employee;
SELECT * FROM employee;
SELECT name,salary FROM employee;
#避免重复DISTINCT
SELECT DISTINCT post FROM employee;
#通过四则运算查询
SELECT name, salary*12 FROM employee;
SELECT name, salary*12 AS Annual_salary FROM employee;
SELECT name, salary*12 Annual_salary FROM employee;
#定义显示格式
CONCAT() 函数用于连接字符串
SELECT CONCAT('姓名: ',name,' 年薪: ', salary*12) AS Annual_salary
FROM employee;
CONCAT_WS() 第一个参数为分隔符
SELECT CONCAT_WS(':',name,salary*12) AS Annual_salary
FROM employee;
2、WHERE约束
where字句中可以使用:
1. 比较运算符:> < >= <= <> !=
2. between 80 and 100 值在10到20之间
3. in(80,90,100) 值是10或20或30
4. like 'egon%'
pattern可以是%或_,
%表示任意多字符
_表示一个字符
5. 逻辑运算符:在多个条件直接可以使用逻辑运算符 and or not
#1:单条件查询
SELECT name FROM employee
WHERE post='sale';
#2:多条件查询
SELECT name,salary FROM employee
WHERE post='teacher' AND salary>10000;
#3:关键字BETWEEN AND
SELECT name,salary FROM employee
WHERE salary BETWEEN 10000 AND 20000;
SELECT name,salary FROM employee
WHERE salary NOT BETWEEN 10000 AND 20000;
#4:关键字IS NULL(判断某个字段是否为NULL不能用等号,需要用IS)
SELECT name,post_comment FROM employee
WHERE post_comment IS NULL;
SELECT name,post_comment FROM employee
WHERE post_comment IS NOT NULL;
SELECT name,post_comment FROM employee
WHERE post_comment=''; 注意''是空字符串,不是null
ps:
执行
update employee set post_comment='' where id=2;
再用上条查看,就会有结果了
#5:关键字IN集合查询
SELECT name,salary FROM employee
WHERE salary=3000 OR salary=3500 OR salary=4000 OR salary=9000 ;
SELECT name,salary FROM employee
WHERE salary IN (3000,3500,4000,9000) ;
SELECT name,salary FROM employee
WHERE salary NOT IN (3000,3500,4000,9000) ;
#6:关键字LIKE模糊查询
通配符’%’
SELECT * FROM employee
WHERE name LIKE 'eg%';
通配符’_’
SELECT * FROM employee
WHERE name LIKE 'al__';
3、 分组查询:GROUP BY
1、 什么是分组?为什么要分组?
#1、首先明确一点:分组发生在where之后,即分组是基于where之后得到的记录而进行的
#2、分组指的是:将所有记录按照某个相同字段进行归类,比如针对员工信息表的职位分组,或者按照性别进行分组等
#3、为何要分组呢?
取每个部门的最高工资
取每个部门的员工数
取男人数和女人数
小窍门:‘每’这个字后面的字段,就是我们分组的依据
#4、大前提:
可以按照任意字段分组,但是分组完毕后,比如group by post,只能查看post字段,如果想查看组内信息,需要借助于聚合函数
2、ONLY_FULL_GROUP_BY
#查看MySQL 5.7默认的sql_mode如下:
mysql> select @@global.sql_mode;
ONLY_FULL_GROUP_BY,STRICT_TRANS_TABLES,NO_ZERO_IN_DATE,NO_ZERO_DATE,ERROR_FOR_DIVISION_BY_ZERO,NO_AUTO_CREATE_USER,NO_ENGINE_SUBSTITUTION
#!!!注意
ONLY_FULL_GROUP_BY的语义就是确定select target list中的所有列的值都是明确语义,简单的说来,在ONLY_FULL_GROUP_BY模式下,target list中的值要么是来自于聚集函数的结果,要么是来自于group by list中的表达式的值。
#设置sql_mole如下操作(我们可以去掉ONLY_FULL_GROUP_BY模式):
mysql> set global sql_mode='STRICT_TRANS_TABLES,NO_ZERO_IN_DATE,NO_ZERO_DATE,ERROR_FOR_DIVISION_BY_ZERO,NO_AUTO_CREATE_USER,NO_ENGINE_SUBSTITUTION';
复制代码
mysql> select @@global.sql_mode;
+-------------------+
| @@global.sql_mode |
+-------------------+
| |
+-------------------+
1 row in set (0.00 sec)
mysql> select * from emp group by post;
+----+------+--------+-----+------------+----------------------------+--------------+------------+--------+-----------+
| id | name | sex | age | hire_date | post | post_comment | salary | office | depart_id |
+----+------+--------+-----+------------+----------------------------+--------------+------------+--------+-----------+
| 14 | 张野 | male | 28 | 2016-03-11 | operation | NULL | 10000.13 | 403 | 3 |
| 9 | 歪歪 | female | 48 | 2015-03-11 | sale | NULL | 3000.13 | 402 | 2 |
| 2 | alex | male | 78 | 2015-03-02 | teacher | NULL | 1000000.31 | 401 | 1 |
| 1 | egon | male | 18 | 2017-03-01 | 老男孩驻沙河办事处外交大使 | NULL | 7300.33 | 401 | 1 |
+----+------+--------+-----+------------+----------------------------+--------------+------------+--------+-----------+
4 rows in set (0.00 sec)
#由于没有设置ONLY_FULL_GROUP_BY,于是也可以有结果,默认都是组内的第一条记录,但其实这是没有意义的
mysql> set global sql_mode='ONLY_FULL_GROUP_BY';
Query OK, 0 rows affected (0.00 sec)
mysql> quit #设置成功后,一定要退出,然后重新登录方可生效
Bye
mysql> use db1;
Database changed
mysql> select * from emp group by post; #报错
ERROR 1055 (42000): 'db1.emp.id' isn't in GROUP BY
mysql> select post,count(id) from emp group by post; #只能查看分组依据和使用聚合函数
+----------------------------+-----------+
| post | count(id) |
+----------------------------+-----------+
| operation | 5 |
| sale | 5 |
| teacher | 7 |
| 老男孩驻沙河办事处外交大使 | 1 |
+----------------------------+-----------+
4 rows in set (0.00 sec)
复制代码
3、 GROUP BY
单独使用GROUP BY关键字分组
SELECT post FROM employee GROUP BY post;
注意:我们按照post字段分组,那么select查询的字段只能是post,想要获取组内的其他相关信息,需要借助函数
GROUP BY关键字和GROUP_CONCAT()函数一起使用
SELECT post,GROUP_CONCAT(name) FROM employee GROUP BY post;#按照岗位分组,并查看组内成员名
SELECT post,GROUP_CONCAT(name) as emp_members FROM employee GROUP BY post;
GROUP BY与聚合函数一起使用
select post,count(id) as count from employee group by post;#按照岗位分组,并查看每个组有多少人
强调:
如果我们用unique的字段作为分组的依据,则每一条记录自成一组,这种分组没有意义
多条记录之间的某个字段值相同,该字段通常用来作为分组的依据
四 聚合函数
#强调:聚合函数聚合的是组的内容,若是没有分组,则默认一组
示例:
SELECT COUNT(*) FROM employee;
SELECT COUNT(*) FROM employee WHERE depart_id=1;
SELECT MAX(salary) FROM employee;
SELECT MIN(salary) FROM employee;
SELECT AVG(salary) FROM employee;
SELECT SUM(salary) FROM employee;
SELECT SUM(salary) FROM employee WHERE depart_id=3;
六 HAVING过滤
HAVING与WHERE不一样的地方在于!!!!!!
#!!!执行优先级从高到低:where > group by > having
#1. Where 发生在分组group by之前,因而Where中可以有任意字段,但是绝对不能使用聚合函数。
#2. Having发生在分组group by之后,因而Having中可以使用分组的字段,无法直接取到其他字段,可以使用聚合函数
mysql> select @@sql_mode; +--------------------+ | @@sql_mode | +--------------------+ | ONLY_FULL_GROUP_BY | +--------------------+ 1 row in set (0.00 sec) mysql> select * from emp where salary > 100000; +----+------+------+-----+------------+---------+--------------+------------+--------+-----------+ | id | name | sex | age | hire_date | post | post_comment | salary | office | depart_id | +----+------+------+-----+------------+---------+--------------+------------+--------+-----------+ | 2 | alex | male | 78 | 2015-03-02 | teacher | NULL | 1000000.31 | 401 | 1 | +----+------+------+-----+------------+---------+--------------+------------+--------+-----------+ 1 row in set (0.00 sec) mysql> select * from emp having salary > 100000; ERROR 1463 (42000): Non-grouping field 'salary' is used in HAVING clause mysql> select post,group_concat(name) from emp group by post having salary > 10000;#错误,分组后无法直接取到salary字段 ERROR 1054 (42S22): Unknown column 'salary' in 'having clause' mysql> select post,group_concat(name) from emp group by post having avg(salary) > 10000; +-----------+-------------------------------------------------------+ | post | group_concat(name) | +-----------+-------------------------------------------------------+ | operation | 程咬铁,程咬铜,程咬银,程咬金,张野 | | teacher | 成龙,jinxin,jingliyang,liwenzhou,yuanhao,wupeiqi,alex | +-----------+-------------------------------------------------------+ 2 rows in set (0.00 sec)
七 查询排序:ORDER BY
按单列排序
SELECT * FROM employee ORDER BY salary;
SELECT * FROM employee ORDER BY salary ASC;
SELECT * FROM employee ORDER BY salary DESC;
按多列排序:先按照age排序,如果年纪相同,则按照薪资排序
SELECT * from employee
ORDER BY age,
salary DESC;
八 限制查询的记录数:LIMIT
示例:
SELECT * FROM employee ORDER BY salary DESC
LIMIT 3; #默认初始位置为0
SELECT * FROM employee ORDER BY salary DESC
LIMIT 0,5; #从第0开始,即先查询出第一条,然后包含这一条在内往后查5条
SELECT * FROM employee ORDER BY salary DESC
LIMIT 5,5; #从第5开始,即先查询出第6条,然后包含这一条在内往后查5条
九 使用正则表达式查询
SELECT * FROM employee WHERE name REGEXP '^ale';
SELECT * FROM employee WHERE name REGEXP 'on$';
SELECT * FROM employee WHERE name REGEXP 'm{2}';
小结:对字符串匹配的方式
WHERE name = 'egon';
WHERE name LIKE 'yua%';
WHERE name REGEXP 'on$';