阐述
所谓子查询，就是嵌套在其他查询中的查询。
某些情况下，当进行一个查询时，需要的条件或数据要用另外一个 select 语句的结果，这个时候，就要用到子查询。
定义：
执行顺序方面先于当前查询执行的，并且是嵌套在当前查询中的查询叫做子查询。
MySQL 在处理上例的 SELECT 语句时，执行流程为：
先执行子查询，再执行父查询。
子查询应当始终放在括号内。
子查询的支持是从 4.1 版本引入的。MySQL 的早期版本不支持子查询。
子查询分类
按照返回结果的行列数分类
1 标量子查询（结果集只有一行一列）
2 列子查询（结果集只有一列多行）
3 行子查询（结果集有一行多列）
4 表子查询（结果集一般为多列多行）
按子查询出现在主查询的位置分类
select 后面：仅支持标量子查询
from 后面：支持表子查询
where 或者 having 后面：
支持标量子查询（单行单列）、
列子查询（单列多行）、
行子查询（多列多行）
exists后面（即相关子查询）： 表子查询（多行多列）
预备数据
部门表 departments
/*部门表*/
DROP TABLE IF EXISTS `departments`;
CREATE TABLE `departments` (
`department_id` int(4) NOT NULL AUTO_INCREMENT comment '部门id',
`department_name` varchar(3) DEFAULT NULL comment '部门名称',
`manager_id` int(6) DEFAULT NULL comment '管理者id',
`location_id` int(4) DEFAULT NULL comment '部门位置id，来源于表locations中的location_id',
PRIMARY KEY (`department_id`),
KEY `loc_id_fk` (`location_id`)
) ENGINE=InnoDB AUTO_INCREMENT=271 comment '部门表';
INSERT INTO `departments` (
`department_id`,
`department_name`,
`manager_id`,
`location_id`
)
VALUES
(10, 'Adm', 200, 1700),
(20, 'Mar', 201, 1800),
(30, 'Pur', 114, 1700),
(40, 'Hum', 203, 2400),
(50, 'Shi', 121, 1500),
(60, 'IT', 103, 1400),
(70, 'Pub', 204, 2700),
(80, 'Sal', 145, 2500),
(90, 'Exe', 100, 1700),
(100, 'Fin', 108, 1700),
(110, 'Acc', 205, 1700),
(120, 'Tre', NULL, 1700),
(130, 'Cor', NULL, 1700),
(140, 'Con', NULL, 1700),
(150, 'Sha', NULL, 1700),
(160, 'Ben', NULL, 1700),
(170, 'Man', NULL, 1700),
(180, 'Con', NULL, 1700),
(190, 'Con', NULL, 1700),
(200, 'Ope', NULL, 1700),
(210, 'IT ', NULL, 1700),
(220, 'NOC', NULL, 1700),
(230, 'IT ', NULL, 1700),
(240, 'Gov', NULL, 1700),
(250, 'Ret', NULL, 1700),
(260, 'Rec', NULL, 1700),
(270, 'Pay', NULL, 1700);
员工表 employees
/*员工表*/
DROP TABLE IF EXISTS `employees`;
CREATE TABLE `employees` (
`employee_id` int(6) NOT NULL AUTO_INCREMENT comment '员工id',
`first_name` varchar(20) DEFAULT NULL comment '名',
`last_name` varchar(25) DEFAULT NULL comment '姓',
`email` varchar(25) DEFAULT NULL comment '电子邮箱',
`phone_number` varchar(20) DEFAULT NULL comment '手机',
`job_id` varchar(10) DEFAULT NULL comment '职位id，来源于jobs表中的job_id',
`salary` double(10,2) DEFAULT NULL comment '薪水',
`commission_pct` double(4,2) DEFAULT NULL comment '佣金百分比',
`manager_id` int(6) DEFAULT NULL comment '上级id',
`department_id` int(4) DEFAULT NULL comment '所属部门id，来源于departments中的department_id',
`hiredate` datetime DEFAULT NULL comment '入职日期',
PRIMARY KEY (`employee_id`)
) ENGINE=InnoDB AUTO_INCREMENT=207 comment '员工表';
INSERT INTO `employees` (
`employee_id`,
`first_name`,
`last_name`,
`email`,
`phone_number`,
`job_id`,
`salary`,
`commission_pct`,
`manager_id`,
`department_id`,
`hiredate`
)
VALUES
(
100,
'Steven',
'K_ing',
'SKING',
'515.123.4567',
'AD_PRES',
24000.00,
NULL,
NULL,
90,
'1992-04-03 00:00:00'
),
(
101,
'Neena',
'Kochhar',
'NKOCHHAR',
'515.123.4568',
'AD_VP',
17000.00,
NULL,
100,
90,
'1992-04-03 00:00:00'
),
(
102,
'Lex',
'De Haan',
'LDEHAAN',
'515.123.4569',
'AD_VP',
17000.00,
NULL,
100,
90,
'1992-04-03 00:00:00'
),
(
103,
'Alexander',
'Hunold',
'AHUNOLD',
'590.423.4567',
'IT_PROG',
9000.00,
NULL,
102,
60,
'1992-04-03 00:00:00'
),
(
104,
'Bruce',
'Ernst',
'BERNST',
'590.423.4568',
'IT_PROG',
6000.00,
NULL,
103,
60,
'1992-04-03 00:00:00'
),
(
105,
'David',
'Austin',
'DAUSTIN',
'590.423.4569',
'IT_PROG',
4800.00,
NULL,
103,
60,
'1998-03-03 00:00:00'
),
(
106,
'Valli',
'Pataballa',
'VPATABAL',
'590.423.4560',
'IT_PROG',
4800.00,
NULL,
103,
60,
'1998-03-03 00:00:00'
),
(
107,
'Diana',
'Lorentz',
'DLORENTZ',
'590.423.5567',
'IT_PROG',
4200.00,
NULL,
103,
60,
'1998-03-03 00:00:00'
),
(
108,
'Nancy',
'Greenberg',
'NGREENBE',
'515.124.4569',
'FI_MGR',
12000.00,
NULL,
101,
100,
'1998-03-03 00:00:00'
),
(
109,
'Daniel',
'Faviet',
'DFAVIET',
'515.124.4169',
'FI_ACCOUNT',
9000.00,
NULL,
108,
100,
'1998-03-03 00:00:00'
),
(
110,
'John',
'Chen',
'JCHEN',
'515.124.4269',
'FI_ACCOUNT',
8200.00,
NULL,
108,
100,
'2000-09-09 00:00:00'
),
(
111,
'Ismael',
'Sciarra',
'ISCIARRA',
'515.124.4369',
'FI_ACCOUNT',
7700.00,
NULL,
108,
100,
'2000-09-09 00:00:00'
),
(
112,
'Jose Manuel',
'Urman',
'JMURMAN',
'515.124.4469',
'FI_ACCOUNT',
7800.00,
NULL,
108,
100,
'2000-09-09 00:00:00'
),
(
113,
'Luis',
'Popp',
'LPOPP',
'515.124.4567',
'FI_ACCOUNT',
6900.00,
NULL,
108,
100,
'2000-09-09 00:00:00'
),
(
114,
'Den',
'Raphaely',
'DRAPHEAL',
'515.127.4561',
'PU_MAN',
11000.00,
NULL,
100,
30,
'2000-09-09 00:00:00'
),
(
115,
'Alexander',
'Khoo',
'AKHOO',
'515.127.4562',
'PU_CLERK',
3100.00,
NULL,
114,
30,
'2000-09-09 00:00:00'
),
(
116,
'Shelli',
'Baida',
'SBAIDA',
'515.127.4563',
'PU_CLERK',
2900.00,
NULL,
114,
30,
'2000-09-09 00:00:00'
),
(
117,
'Sigal',
'Tobias',
'STOBIAS',
'515.127.4564',
'PU_CLERK',
2800.00,
NULL,
114,
30,
'2000-09-09 00:00:00'
),
(
118,
'Guy',
'Himuro',
'GHIMURO',
'515.127.4565',
'PU_CLERK',
2600.00,
NULL,
114,
30,
'2000-09-09 00:00:00'
),
(
119,
'Karen',
'Colmenares',
'KCOLMENA',
'515.127.4566',
'PU_CLERK',
2500.00,
NULL,
114,
30,
'2000-09-09 00:00:00'
),
(
120,
'Matthew',
'Weiss',
'MWEISS',
'650.123.1234',
'ST_MAN',
8000.00,
NULL,
100,
50,
'2004-02-06 00:00:00'
),
(
121,
'Adam',
'Fripp',
'AFRIPP',
'650.123.2234',
'ST_MAN',
8200.00,
NULL,
100,
50,
'2004-02-06 00:00:00'
),
(
122,
'Payam',
'Kaufling',
'PKAUFLIN',
'650.123.3234',
'ST_MAN',
7900.00,
NULL,
100,
50,
'2004-02-06 00:00:00'
),
(
123,
'Shanta',
'Vollman',
'SVOLLMAN',
'650.123.4234',
'ST_MAN',
6500.00,
NULL,
100,
50,
'2004-02-06 00:00:00'
),
(
124,
'Kevin',
'Mourgos',
'KMOURGOS',
'650.123.5234',
'ST_MAN',
5800.00,
NULL,
100,
50,
'2004-02-06 00:00:00'
),
(
125,
'Julia',
'Nayer',
'JNAYER',
'650.124.1214',
'ST_CLERK',
3200.00,
NULL,
120,
50,
'2004-02-06 00:00:00'
),
(
126,
'Irene',
'Mikkilineni',
'IMIKKILI',
'650.124.1224',
'ST_CLERK',
2700.00,
NULL,
120,
50,
'2004-02-06 00:00:00'
),
(
127,
'James',
'Landry',
'JLANDRY',
'650.124.1334',
'ST_CLERK',
2400.00,
NULL,
120,
50,
'2004-02-06 00:00:00'
),
(
128,
'Steven',
'Markle',
'SMARKLE',
'650.124.1434',
'ST_CLERK',
2200.00,
NULL,
120,
50,
'2004-02-06 00:00:00'
),
(
129,
'Laura',
'Bissot',
'LBISSOT',
'650.124.5234',
'ST_CLERK',
3300.00,
NULL,
121,
50,
'2004-02-06 00:00:00'
),
(
130,
'Mozhe',
'Atkinson',
'MATKINSO',
'650.124.6234',
'ST_CLERK',
2800.00,
NULL,
121,
50,
'2004-02-06 00:00:00'
),
(
131,
'James',
'Marlow',
'JAMRLOW',
'650.124.7234',
'ST_CLERK',
2500.00,
NULL,
121,
50,
'2004-02-06 00:00:00'
),
(
132,
'TJ',
'Olson',
'TJOLSON',
'650.124.8234',
'ST_CLERK',
2100.00,
NULL,
121,
50,
'2004-02-06 00:00:00'
),
(
133,
'Jason',
'Mallin',
'JMALLIN',
'650.127.1934',
'ST_CLERK',
3300.00,
NULL,
122,
50,
'2004-02-06 00:00:00'
),
(
134,
'Michael',
'Rogers',
'MROGERS',
'650.127.1834',
'ST_CLERK',
2900.00,
NULL,
122,
50,
'2002-12-23 00:00:00'
),
(
135,
'Ki',
'Gee',
'KGEE',
'650.127.1734',
'ST_CLERK',
2400.00,
NULL,
122,
50,
'2002-12-23 00:00:00'
),
(
136,
'Hazel',
'Philtanker',
'HPHILTAN',
'650.127.1634',
'ST_CLERK',
2200.00,
NULL,
122,
50,
'2002-12-23 00:00:00'
),
(
137,
'Renske',
'Ladwig',
'RLADWIG',
'650.121.1234',
'ST_CLERK',
3600.00,
NULL,
123,
50,
'2002-12-23 00:00:00'
),
(
138,
'Stephen',
'Stiles',
'SSTILES',
'650.121.2034',
'ST_CLERK',
3200.00,
NULL,
123,
50,
'2002-12-23 00:00:00'
),
(
139,
'John',
'Seo',
'JSEO',
'650.121.2019',
'ST_CLERK',
2700.00,
NULL,
123,
50,
'2002-12-23 00:00:00'
),
(
140,
'Joshua',
'Patel',
'JPATEL',
'650.121.1834',
'ST_CLERK',
2500.00,
NULL,
123,
50,
'2002-12-23 00:00:00'
),
(
141,
'Trenna',
'Rajs',
'TRAJS',
'650.121.8009',
'ST_CLERK',
3500.00,
NULL,
124,
50,
'2002-12-23 00:00:00'
),
(
142,
'Curtis',
'Davies',
'CDAVIES',
'650.121.2994',
'ST_CLERK',
3100.00,
NULL,
124,
50,
'2002-12-23 00:00:00'
),
(
143,
'Randall',
'Matos',
'RMATOS',
'650.121.2874',
'ST_CLERK',
2600.00,
NULL,
124,
50,
'2002-12-23 00:00:00'
),
(
144,
'Peter',
'Vargas',
'PVARGAS',
'650.121.2004',
'ST_CLERK',
2500.00,
NULL,
124,
50,
'2002-12-23 00:00:00'
),
(
145,
'John',
'Russell',
'JRUSSEL',
'011.44.1344.429268',
'SA_MAN',
14000.00,
0.40,
100,
80,
'2002-12-23 00:00:00'
),
(
146,
'Karen',
'Partners',
'KPARTNER',
'011.44.1344.467268',
'SA_MAN',
13500.00,
0.30,
100,
80,
'2002-12-23 00:00:00'
),
(
147,
'Alberto',
'Errazuriz',
'AERRAZUR',
'011.44.1344.429278',
'SA_MAN',
12000.00,
0.30,
100,
80,
'2002-12-23 00:00:00'
),
(
148,
'Gerald',
'Cambrault',
'GCAMBRAU',
'011.44.1344.619268',
'SA_MAN',
11000.00,
0.30,
100,
80,
'2002-12-23 00:00:00'
),
(
149,
'Eleni',
'Zlotkey',
'EZLOTKEY',
'011.44.1344.429018',
'SA_MAN',
10500.00,
0.20,
100,
80,
'2002-12-23 00:00:00'
),
(
150,
'Peter',
'Tucker',
'PTUCKER',
'011.44.1344.129268',
'SA_REP',
10000.00,
0.30,
145,
80,
'2014-03-05 00:00:00'
),
(
151,
'David',
'Bernstein',
'DBERNSTE',
'011.44.1344.345268',
'SA_REP',
9500.00,
0.25,
145,
80,
'2014-03-05 00:00:00'
),
(
152,
'Peter',
'Hall',
'PHALL',
'011.44.1344.478968',
'SA_REP',
9000.00,
0.25,
145,
80,
'2014-03-05 00:00:00'
),
(
153,
'Christopher',
'Olsen',
'COLSEN',
'011.44.1344.498718',
'SA_REP',
8000.00,
0.20,
145,
80,
'2014-03-05 00:00:00'
),
(
154,
'Nanette',
'Cambrault',
'NCAMBRAU',
'011.44.1344.987668',
'SA_REP',
7500.00,
0.20,
145,
80,
'2014-03-05 00:00:00'
),
(
155,
'Oliver',
'Tuvault',
'OTUVAULT',
'011.44.1344.486508',
'SA_REP',
7000.00,
0.15,
145,
80,
'2014-03-05 00:00:00'
),
(
156,
'Janette',
'K_ing',
'JKING',
'011.44.1345.429268',
'SA_REP',
10000.00,
0.35,
146,
80,
'2014-03-05 00:00:00'
),
(
157,
'Patrick',
'Sully',
'PSULLY',
'011.44.1345.929268',
'SA_REP',
9500.00,
0.35,
146,
80,
'2014-03-05 00:00:00'
),
(
158,
'Allan',
'McEwen',
'AMCEWEN',
'011.44.1345.829268',
'SA_REP',
9000.00,
0.35,
146,
80,
'2014-03-05 00:00:00'
),
(
159,
'Lindsey',
'Smith',
'LSMITH',
'011.44.1345.729268',
'SA_REP',
8000.00,
0.30,
146,
80,
'2014-03-05 00:00:00'
),
(
160,
'Louise',
'Doran',
'LDORAN',
'011.44.1345.629268',
'SA_REP',
7500.00,
0.30,
146,
80,
'2014-03-05 00:00:00'
),
(
161,
'Sarath',
'Sewall',
'SSEWALL',
'011.44.1345.529268',
'SA_REP',
7000.00,
0.25,
146,
80,
'2014-03-05 00:00:00'
),
(
162,
'Clara',
'Vishney',
'CVISHNEY',
'011.44.1346.129268',
'SA_REP',
10500.00,
0.25,
147,
80,
'2014-03-05 00:00:00'
),
(
163,
'Danielle',
'Greene',
'DGREENE',
'011.44.1346.229268',
'SA_REP',
9500.00,
0.15,
147,
80,
'2014-03-05 00:00:00'
),
(
164,
'Mattea',
'Marvins',
'MMARVINS',
'011.44.1346.329268',
'SA_REP',
7200.00,
0.10,
147,
80,
'2014-03-05 00:00:00'
),
(
165,
'David',
'Lee',
'DLEE',
'011.44.1346.529268',
'SA_REP',
6800.00,
0.10,
147,
80,
'2014-03-05 00:00:00'
),
(
166,
'Sundar',
'Ande',
'SANDE',
'011.44.1346.629268',
'SA_REP',
6400.00,
0.10,
147,
80,
'2014-03-05 00:00:00'
),
(
167,
'Amit',
'Banda',
'ABANDA',
'011.44.1346.729268',
'SA_REP',
6200.00,
0.10,
147,
80,
'2014-03-05 00:00:00'
),
(
168,
'Lisa',
'Ozer',
'LOZER',
'011.44.1343.929268',
'SA_REP',
11500.00,
0.25,
148,
80,
'2014-03-05 00:00:00'
),
(
169,
'Harrison',
'Bloom',
'HBLOOM',
'011.44.1343.829268',
'SA_REP',
10000.00,
0.20,
148,
80,
'2014-03-05 00:00:00'
),
(
170,
'Tayler',
'Fox',
'TFOX',
'011.44.1343.729268',
'SA_REP',
9600.00,
0.20,
148,
80,
'2014-03-05 00:00:00'
),
(
171,
'William',
'Smith',
'WSMITH',
'011.44.1343.629268',
'SA_REP',
7400.00,
0.15,
148,
80,
'2014-03-05 00:00:00'
),
(
172,
'Elizabeth',
'Bates',
'EBATES',
'011.44.1343.529268',
'SA_REP',
7300.00,
0.15,
148,
80,
'2014-03-05 00:00:00'
),
(
173,
'Sundita',
'Kumar',
'SKUMAR',
'011.44.1343.329268',
'SA_REP',
6100.00,
0.10,
148,
80,
'2014-03-05 00:00:00'
),
(
174,
'Ellen',
'Abel',
'EABEL',
'011.44.1644.429267',
'SA_REP',
11000.00,
0.30,
149,
80,
'2014-03-05 00:00:00'
),
(
175,
'Alyssa',
'Hutton',
'AHUTTON',
'011.44.1644.429266',
'SA_REP',
8800.00,
0.25,
149,
80,
'2014-03-05 00:00:00'
),
(
176,
'Jonathon',
'Taylor',
'JTAYLOR',
'011.44.1644.429265',
'SA_REP',
8600.00,
0.20,
149,
80,
'2014-03-05 00:00:00'
),
(
177,
'Jack',
'Livingston',
'JLIVINGS',
'011.44.1644.429264',
'SA_REP',
8400.00,
0.20,
149,
80,
'2014-03-05 00:00:00'
),
(
178,
'Kimberely',
'Grant',
'KGRANT',
'011.44.1644.429263',
'SA_REP',
7000.00,
0.15,
149,
NULL,
'2014-03-05 00:00:00'
),
(
179,
'Charles',
'Johnson',
'CJOHNSON',
'011.44.1644.429262',
'SA_REP',
6200.00,
0.10,
149,
80,
'2014-03-05 00:00:00'
),
(
180,
'Winston',
'Taylor',
'WTAYLOR',
'650.507.9876',
'SH_CLERK',
3200.00,
NULL,
120,
50,
'2014-03-05 00:00:00'
),
(
181,
'Jean',
'Fleaur',
'JFLEAUR',
'650.507.9877',
'SH_CLERK',
3100.00,
NULL,
120,
50,
'2014-03-05 00:00:00'
),
(
182,
'Martha',
'Sullivan',
'MSULLIVA',
'650.507.9878',
'SH_CLERK',
2500.00,
NULL,
120,
50,
'2014-03-05 00:00:00'
),
(
183,
'Girard',
'Geoni',
'GGEONI',
'650.507.9879',
'SH_CLERK',
2800.00,
NULL,
120,
50,
'2014-03-05 00:00:00'
),
(
184,
'Nandita',
'Sarchand',
'NSARCHAN',
'650.509.1876',
'SH_CLERK',
4200.00,
NULL,
121,
50,
'2014-03-05 00:00:00'
),
(
185,
'Alexis',
'Bull',
'ABULL',
'650.509.2876',
'SH_CLERK',
4100.00,
NULL,
121,
50,
'2014-03-05 00:00:00'
),
(
186,
'Julia',
'Dellinger',
'JDELLING',
'650.509.3876',
'SH_CLERK',
3400.00,
NULL,
121,
50,
'2014-03-05 00:00:00'
),
(
187,
'Anthony',
'Cabrio',
'ACABRIO',
'650.509.4876',
'SH_CLERK',
3000.00,
NULL,
121,
50,
'2014-03-05 00:00:00'
),
(
188,
'Kelly',
'Chung',
'KCHUNG',
'650.505.1876',
'SH_CLERK',
3800.00,
NULL,
122,
50,
'2014-03-05 00:00:00'
),
(
189,
'Jennifer',
'Dilly',
'JDILLY',
'650.505.2876',
'SH_CLERK',
3600.00,
NULL,
122,
50,
'2014-03-05 00:00:00'
),
(
190,
'Timothy',
'Gates',
'TGATES',
'650.505.3876',
'SH_CLERK',
2900.00,
NULL,
122,
50,
'2014-03-05 00:00:00'
),
(
191,
'Randall',
'Perkins',
'RPERKINS',
'650.505.4876',
'SH_CLERK',
2500.00,
NULL,
122,
50,
'2014-03-05 00:00:00'
),
(
192,
'Sarah',
'Bell',
'SBELL',
'650.501.1876',
'SH_CLERK',
4000.00,
NULL,
123,
50,
'2014-03-05 00:00:00'
),
(
193,
'Britney',
'Everett',
'BEVERETT',
'650.501.2876',
'SH_CLERK',
3900.00,
NULL,
123,
50,
'2014-03-05 00:00:00'
),
(
194,
'Samuel',
'McCain',
'SMCCAIN',
'650.501.3876',
'SH_CLERK',
3200.00,
NULL,
123,
50,
'2014-03-05 00:00:00'
),
(
195,
'Vance',
'Jones',
'VJONES',
'650.501.4876',
'SH_CLERK',
2800.00,
NULL,
123,
50,
'2014-03-05 00:00:00'
),
(
196,
'Alana',
'Walsh',
'AWALSH',
'650.507.9811',
'SH_CLERK',
3100.00,
NULL,
124,
50,
'2014-03-05 00:00:00'
),
(
197,
'Kevin',
'Feeney',
'KFEENEY',
'650.507.9822',
'SH_CLERK',
3000.00,
NULL,
124,
50,
'2014-03-05 00:00:00'
),
(
198,
'Donald',
'OConnell',
'DOCONNEL',
'650.507.9833',
'SH_CLERK',
2600.00,
NULL,
124,
50,
'2014-03-05 00:00:00'
),
(
199,
'Douglas',
'Grant',
'DGRANT',
'650.507.9844',
'SH_CLERK',
2600.00,
NULL,
124,
50,
'2014-03-05 00:00:00'
),
(
200,
'Jennifer',
'Whalen',
'JWHALEN',
'515.123.4444',
'AD_ASST',
4400.00,
NULL,
101,
10,
'2016-03-03 00:00:00'
),
(
201,
'Michael',
'Hartstein',
'MHARTSTE',
'515.123.5555',
'MK_MAN',
13000.00,
NULL,
100,
20,
'2016-03-03 00:00:00'
),
(
202,
'Pat',
'Fay',
'PFAY',
'603.123.6666',
'MK_REP',
6000.00,
NULL,
201,
20,
'2016-03-03 00:00:00'
),
(
203,
'Susan',
'Mavris',
'SMAVRIS',
'515.123.7777',
'HR_REP',
6500.00,
NULL,
101,
40,
'2016-03-03 00:00:00'
),
(
204,
'Hermann',
'Baer',
'HBAER',
'515.123.8888',
'PR_REP',
10000.00,
NULL,
101,
70,
'2016-03-03 00:00:00'
),
(
205,
'Shelley',
'Higgins',
'SHIGGINS',
'515.123.8080',
'AC_MGR',
12000.00,
NULL,
101,
110,
'2016-03-03 00:00:00'
),
(
206,
'William',
'Gietz',
'WGIETZ',
'515.123.8181',
'AC_ACCOUNT',
8300.00,
NULL,
205,
110,
'2016-03-03 00:00:00'
);
职位信息表 jobs
/*职位信息表*/
DROP TABLE IF EXISTS `jobs`;
CREATE TABLE `jobs` (
`job_id` varchar(10) NOT NULL comment '职位id',
`job_title` varchar(35) DEFAULT NULL comment '职位名称',
`min_salary` int(6) DEFAULT NULL comment '薪资范围最小值',
`max_salary` int(6) DEFAULT NULL comment '薪资范围最大值',
PRIMARY KEY (`job_id`)
) ENGINE=InnoDB comment '职位id';
INSERT INTO `jobs` (
`job_id`,
`job_title`,
`min_salary`,
`max_salary`
)
VALUES
(
'AC_ACCOUNT',
'Public Accountant',
4200,
9000
),
(
'AC_MGR',
'Accounting Manager',
8200,
16000
),
(
'AD_ASST',
'Administration Assistant',
3000,
6000
),
(
'AD_PRES',
'President',
20000,
40000
),
(
'AD_VP',
'Administration Vice President',
15000,
30000
),
(
'FI_ACCOUNT',
'Accountant',
4200,
9000
),
(
'FI_MGR',
'Finance Manager',
8200,
16000
),
(
'HR_REP',
'Human Resources Representative',
4000,
9000
),
(
'IT_PROG',
'Programmer',
4000,
10000
),
(
'MK_MAN',
'Marketing Manager',
9000,
15000
),
(
'MK_REP',
'Marketing Representative',
4000,
9000
),
(
'PR_REP',
'Public Relations Representative',
4500,
10500
),
(
'PU_CLERK',
'Purchasing Clerk',
2500,
5500
),
(
'PU_MAN',
'Purchasing Manager',
8000,
15000
),
(
'SA_MAN',
'Sales Manager',
10000,
20000
),
(
'SA_REP',
'Sales Representative',
6000,
12000
),
(
'SH_CLERK',
'Shipping Clerk',
2500,
5500
),
(
'ST_CLERK',
'Stock Clerk',
2000,
5000
),
(
'ST_MAN',
'Stock Manager',
5500,
8500
);
位置表 locations
/*位置表*/
DROP TABLE IF EXISTS `locations`;
CREATE TABLE `locations` (
`location_id` int(11) NOT NULL AUTO_INCREMENT comment '位置id',
`street_address` varchar(40) DEFAULT NULL comment '街道地址',
`postal_code` varchar(12) DEFAULT NULL comment '邮编',
`city` varchar(30) DEFAULT NULL comment '城市名称',
`state_province` varchar(25) DEFAULT NULL comment '省',
`country_id` varchar(2) DEFAULT NULL comment '国家编号',
PRIMARY KEY (`location_id`)
) ENGINE=InnoDB AUTO_INCREMENT=3201 comment '位置表';
INSERT INTO `locations` (
`location_id`,
`street_address`,
`postal_code`,
`city`,
`state_province`,
`country_id`
)
VALUES
(
1000,
'1297 Via Cola di Rie',
'00989',
'Roma',
NULL,
'IT'
),
(
1100,
'93091 Calle della Testa',
'10934',
'Venice',
NULL,
'IT'
),
(
1200,
'2017 Shinjuku-ku',
'1689',
'Tokyo',
'Tokyo Prefecture',
'JP'
),
(
1300,
'9450 Kamiya-cho',
'6823',
'Hiroshima',
NULL,
'JP'
),
(
1400,
'2014 Jabberwocky Rd',
'26192',
'Southlake',
'Texas',
'US'
),
(
1500,
'2011 Interiors Blvd',
'99236',
'South San Francisco',
'California',
'US'
),
(
1600,
'2007 Zagora St',
'50090',
'South Brunswick',
'New Jersey',
'US'
),
(
1700,
'2004 Charade Rd',
'98199',
'Seattle',
'Washington',
'US'
),
(
1800,
'147 Spadina Ave',
'M5V 2L7',
'Toronto',
'Ontario',
'CA'
),
(
1900,
'6092 Boxwood St',
'YSW 9T2',
'Whitehorse',
'Yukon',
'CA'
),
(
2000,
'40-5-12 Laogianggen',
'190518',
'Beijing',
NULL,
'CN'
),
(
2100,
'1298 Vileparle (E)',
'490231',
'Bombay',
'Maharashtra',
'IN'
),
(
2200,
'12-98 Victoria Street',
'2901',
'Sydney',
'New South Wales',
'AU'
),
(
2300,
'198 Clementi North',
'540198',
'Singapore',
NULL,
'SG'
),
(
2400,
'8204 Arthur St',
NULL,
'London',
NULL,
'UK'
),
(
2500,
'Magdalen Centre, The Oxford Science Park',
'OX9 9ZB',
'Oxford',
'Oxford',
'UK'
),
(
2600,
'9702 Chester Road',
'09629850293',
'Stretford',
'Manchester',
'UK'
),
(
2700,
'Schwanthalerstr. 7031',
'80925',
'Munich',
'Bavaria',
'DE'
),
(
2800,
'Rua Frei Caneca 1360 ',
'01307-002',
'Sao Paulo',
'Sao Paulo',
'BR'
),
(
2900,
'20 Rue des Corps-Saints',
'1730',
'Geneva',
'Geneve',
'CH'
),
(
3000,
'Murtenstrasse 921',
'3095',
'Bern',
'BE',
'CH'
),
(
3100,
'Pieter Breughelstraat 837',
'3029SK',
'Utrecht',
'Utrecht',
'NL'
),
(
3200,
'Mariano Escobedo 9991',
'11932',
'Mexico City',
'Distrito Federal,',
'MX'
);
薪资等级表 job_grades
/*薪资等级表*/
DROP TABLE IF EXISTS `job_grades`;
CREATE TABLE `job_grades`(
`grade_level` varchar(3) comment '等级',
`lowest_sal` int comment '薪资最低值',
`highest_sal` int comment '薪资最高值',
PRIMARY KEY (`grade_level`)
) comment '薪资等级表';
INSERT INTO job_grades
VALUES
('A', 1000, 2999),
('B', 3000, 5999),
('C', 6000, 9999),
('D', 10000, 14999),
('E', 15000, 24999),
('F', 25000, 40000);
查询数据示例
1 标量子查询（结果集只有一行一列）
2 列子查询（结果集只有一列多行）
3 行子查询（结果集有一行多列）
4 表子查询（结果集一般为多列多行）
select 后面：仅支持标量子查询
from 后面：支持表子查询
where 或者 having 后面：
支持标量子查询（单行单列）、
列子查询（单列多行）、
行子查询（多列多行）
exists后面（即相关子查询）： 表子查询（多行多列）
select 后面的子查询
子查询位于 select 后面的，仅仅支持标量子查询。
示例1 查询每个部门员工人数
SELECT
a.*, (
SELECT
count(*)
FROM
employees b
WHERE
b.department_id = a.department_id
) AS 员工人数
FROM
departments a;
示例2 查询员工号 =102 的部门名称
select (
select a.department_name
from departments a,employees b
where a.department_id = b.department_id and b.employee_id = 102
)
as 部门名称
from 后面的子查询
将子查询的结果集充当一张表，要求必须起别名，否者这个表找不到。
然后将真实的表和子查询结果表进行连接查询。
示例1 查询每个部门平均工资的薪资等级
-- 查询每个部门的平均工资
SELECT department_id,AVG(a.salary)
FROM employees a
GROUP BY a.department_id
-- 薪资等级表
SELECT * from job_grades;
-- 将上面2个结果连接查询，筛选条件:平均工资 between lowest_sal and highest_sal;
SELECT
t1.department_id,
sa AS '平均工资',
t2.grade_level
FROM (SELECT department_id,AVG(a.salary) sa
FROM employees a
GROUP BY a.department_id
) t1, job_grades t2
WHERE
t1.sa BETWEEN t2.lowest_sal AND t2.highest_sal;
where 和 having 后面的子查询
1.标量子查询（单行单列行子查询）
2.列子查询（单列多行子查询）
3.行子查询（一行多列）
一些特点
1 子查询放在小括号内
2 子查询一般放在条件的右侧
3 标量子查询，一般搭配着单行单列相关的操作符使用 >、<、>=、<=、<>、!=
4 列子查询，一般搭配多行操作符使用
in(not in)：列表中的“任意一个”
any 或 some (some 是 any 的别名)：
和子查询返回的“某一个值”比较，比如 a>some(10,20,30)，
a 大于子查询中任意一个即可，a 大于子查询中最小值即可，等同于 a > min(10,20,30)
all：
和子查询返回的“所有值”比较，比如 a > all(10,20,30)，
a 大于子查询中所有值，换句话说，a 大于子查询中最大值即可满足查询条件，
等同于 a > max(10,20,30);
5 子查询的执行优先于主查询执行，因为主查询的条件用到了子查询的结果。
in、any、some、all
in，any，some，all 分别是子查询关键词之一。
in：in 常用于 where 表达式中，其作用是查询某个范围内的数据。
any 和 some 一样：
可以与 =、>、>=、<、<=、<> 结合起来使用，
分别表示等于、大于、大于等于、小于、小于等于、不等于其中的任何一个数据。
all：
可以与 =、>、>=、<、<=、<> 结合是来使用，
分别表示等于、大于、大于等于、小于、小于等于、不等于其中的其中的所有数据。
标量子查询
一般标量子查询，查询谁的工资比 Abel 高
/*首先查出 Abel 的工资*/
SELECT salary from employees where last_name = 'Abel'
/*查询员工信息表，查找 salary 满足大于 Abel 工资的结果*/
SELECT
*
FROM
employees a
WHERE
a.salary > (
SELECT
salary
FROM
employees
WHERE
last_name = 'Abel'
);
多个标量子查询，返回 job_id 与 141 号员工相同，salary 比143 号员工多的员工、姓名、job_id 和工资
/*查询 141 号员工的 job_id */
SELECT job_id from employees where employee_id = 141;
/*查询 143 号员工的 salary */
SELECT salary from employees where employee_id = 143;
/*查询员工的姓名、job_id 和工资，
要求与 141 号员工的 job_id 相等，比 143 号员工的工资高 */
SELECT
a.last_name 姓名,
a.job_id,
a.salary 工资
FROM
employees a
WHERE
a.job_id = (
SELECT
job_id
FROM
employees
WHERE
employee_id = 141
)
AND a.salary > (
SELECT
salary
FROM
employees
WHERE
employee_id = 143
);
子查询加分组查询，查询最低工资大于 50 号部门最低工资的部门 id 和其最低工资【having】
查询最低工资大于 50 号部门最低工资的部门 id 和其最低工资【having】
① 查询 50 号部门的最低工资
SELECT MIN(salary) FROM employees where department_id = 50
② 查询每个部门的最低工资
SELECT MIN(salary),department_id FROM employees GROUP BY department_id
③ 在 ② 的基础上筛选，满足 min(salary) > ①
SELECT
MIN(a.salary) minsalary,
department_id
FROM
employees a
GROUP BY
a.department_id
HAVING
MIN(a.salary) > (
SELECT
MIN(salary)
FROM
employees
WHERE
department_id = 50
)
列子查询
列子查询结果集一列多行。
列子查询需要搭配多行操作符使用：in(not in)、any/some、all
为了提升效率，最好去重一下 distinct 关键字。
示例1，返回 location_id 是 1400 或 1700 的部门中的所有员工姓名
方式 1
① 先从 depatments 表中查出 location_id 是 1400 或 1700 的部门编号
SELECT DISTINCT
department_id
FROM
departments
WHERE
location_id IN (1400, 1700);
查询员工姓名，要求其中员工 department_id 在 ① 中
SELECT
a.last_name
FROM
employees a
WHERE
a.department_id IN (
SELECT DISTINCT
department_id
FROM
departments
WHERE
location_id IN (1400, 1700)
)
方式 2：使用 any 实现 (注意符号)
SELECT
a.last_name
FROM
employees a
WHERE
a.department_id = ANY (
SELECT DISTINCT
department_id
FROM
departments
WHERE
location_id IN (1400, 1700)
)
<> ALL 等同于 not in
SELECT
a.last_name
FROM
employees a
WHERE
a.department_id <> ALL (
SELECT DISTINCT
department_id
FROM
departments
WHERE
location_id IN (1400, 1700)
)
示例2，返回其他工种中比 job_id 为 ‘IT_PROG’ 工种任意工资低的员工的员工号、姓名、job_id、salary
1 查询出 job_id 为 IT_ROG 工种的所有工资
SELECT salary FROM employees WHERE job_id = 'IT_PROG'
也就是说要小于上面查询出的工资列表中的任意一个。
查询员工的员工号、姓名、job_id、salary，且 salary 小于 1 中的任意一个。
SELECT
last_name,
employee_id,
job_id,
salary
FROM
employees
WHERE
salary < ANY (
SELECT DISTINCT
salary
FROM
employees
WHERE
job_id = 'IT_PROG'
)
或者说 salary 小于 1 表 salary 的最大值。
SELECT
last_name,
employee_id,
job_id,
salary
FROM
employees
WHERE
salary < (
SELECT
MAX(salary)
FROM
employees
WHERE
job_id = 'IT_PROG'
)
示例3，返回其他工种中比 job_id 为 ‘IT_PROG’ 部门所有工资低的员工的员工号、姓名、job_id、salary
SELECT
last_name,
employee_id,
job_id,
salary
FROM
employees
WHERE
salary < ALL (
SELECT DISTINCT
salary
FROM
employees
WHERE
job_id = 'IT_PROG'
)
AND job_id != 'IT_PROG';
或
SELECT
last_name,
employee_id,
job_id,
salary
FROM
employees
WHERE
salary < (
SELECT
MIN(salary)
FROM
employees
WHERE
job_id = 'IT_PROG'
)
AND job_id != 'IT_PROG';
查询结果和上面一样。
行子查询
子查询结果集为一行多列。
示例，查询员工编号最小并且工资最高的员工信息，3 种方式。
1 先查询出最小的员工编号
SELECT MIN(employee_id) FROM employees
2 查询工资最高的员工信息
SELECT MAX(salary)from employees
方法 1
SELECT
*
FROM
employees a
WHERE
a.employee_id = (
SELECT
MIN(employee_id)
FROM
employees
)
AND salary = (
SELECT
MAX(salary)
FROM
employees
)
方法 2
SELECT
*
FROM
employees a
WHERE
(a.employee_id, a.salary) = (
SELECT
MIN(employee_id),
MAX(salary)
FROM
employees
)
方法 3
SELECT
*
FROM
employees a
WHERE
(a.employee_id, a.salary) IN (
SELECT
MIN(employee_id),
MAX(salary)
FROM
employees
)
exists 后面（也叫相关子查询）
exists | not exists
当 exists 在 where 后面时，
用于判断子查询的结果集是否为空，
若子查询的结果集不为空，返回 TRUE，否则返回 FALSE；
若使用关键字 NOT，则返回的值正好相反。
1 语法：exists (完整的查询语句)
2 exists 查询结果：1 或 0，exists 的结果用来判断
3 一般来说，能用 exists 的子查询，绝对都能用 in 代替，所以 exists 用的少
4 和前面的查询不同，这先执行主查询，然后主查询查询的结果，在根据子查询进行过滤，子查询中涉及到主查询中用到的字段，所以叫相关子查询。
示例1
SELECT
EXISTS (
SELECT
employee_id
FROM
employees
WHERE
salary = 300000
) AS 'exists返回1或者0';
示例2，查询所有员工的部门名称
SELECT
department_name
FROM
departments a
WHERE
EXISTS (
SELECT
1
FROM
employees b
WHERE
a.department_id = b.department_id
)
用 in 实现
SELECT
department_name
FROM
departments a
WHERE
a.department_id IN (
SELECT
department_id
FROM
employees
);
示例3，查询没有员工的部门
exists 实现
SELECT
*
FROM
departments a
WHERE
NOT EXISTS (
SELECT
1
FROM
employees b
WHERE
a.department_id = b.department_id
AND b.department_id IS NOT NULL
);
in 实现
SELECT
*
FROM
departments a
WHERE
a.department_id NOT IN (
SELECT
department_id
FROM
employees b
WHERE
b.department_id IS NOT NULL
);
注意 null 值
MySQL 提供了 IS NULL 关键字，用来判断字段的值是否为空值（NULL）。
空值不同于 0，也不同于空字符串。
如果字段的值是空值，则满足查询条件，该记录将被查询出来。
如果字段的值不是空值，则不满足查询条件。
注意：
IS NULL 是一个整体，不能将 IS 换成 “=”。
如果将 IS 换成 “=” 将不能查询出任何结果，数据库系统会出现 “Empty set(0.00 sec)” 这样的提示。同理，IS NOT NULL 中的 IS NOT 不能换成 “!=” 或 “<>”。
例如下面的例子，在使用 not in 方式查询没有员工的部门时，
因为子查询的结果中包含了 null 值，导致外查询的结果为空。
使用 not in 查询没有员工的部门
SELECT
*
FROM
departments a
WHERE
a.department_id NOT IN (
SELECT
department_id
FROM
employees b
);
-- 查询结果
mysql> SELECT * FROM departments a WHERE a.department_id NOT IN (SELECT department_id FROM employees b);
Empty set (0.00 sec)
not in 的情况下，子查询中列的值为 NULL 的时候，外查询的结果为空。
小结
在 MySQL 中，NULL 值意味着未知值。
NULL 值不是零或空字符” 值。
NULL 值不等于其自身值。
如果将 NULL 值与另一个 NULL 值或任何其他值进行比较，则结果为 NULL，因为每个NULL 值的值都是未知的。
通常，使用 NULL 值来表示数据丢失，未知或不适用。
当创建表时，可以通过使用 NOT NULL 约束来指定列是否接受 NULL 值。
NULL 在相关排序时类似无穷小的值，所以，不建议使用 NULL 作为列默认值。