DQL-数据查询语言

时间：2021-01-01 12:01:39 阅读：0 评论：0 收藏：0 [点我收藏+]

标签：加法 +++ insert miss rip rop gate abr style

1、准备数据源

/*
SQLyog Ultimate v10.00 Beta1
MySQL - 5.5.15 : Database - myemployees
*********************************************************************
*/


/*!40101 SET NAMES utf8 */;

/*!40101 SET SQL_MODE=‘‘*/;

/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;
/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;
/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE=‘NO_AUTO_VALUE_ON_ZERO‘ */;
/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;
CREATE DATABASE /*!32312 IF NOT EXISTS*/`myemployees` /*!40100 DEFAULT CHARACTER SET gb2312 */;

USE `myemployees`;

/*Table structure for table `departments` */

DROP TABLE IF EXISTS `departments`;

CREATE TABLE `departments` (
  `department_id` int(4) NOT NULL AUTO_INCREMENT,
  `department_name` varchar(3) DEFAULT NULL,
  `manager_id` int(6) DEFAULT NULL,
  `location_id` int(4) DEFAULT NULL,
  PRIMARY KEY (`department_id`),
  KEY `loc_id_fk` (`location_id`),
  CONSTRAINT `loc_id_fk` FOREIGN KEY (`location_id`) REFERENCES `locations` (`location_id`)
) ENGINE=InnoDB AUTO_INCREMENT=271 DEFAULT CHARSET=gb2312;

/*Data for the table `departments` */

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);

/*Table structure for table `employees` */

DROP TABLE IF EXISTS `employees`;

CREATE TABLE `employees` (
  `employee_id` int(6) NOT NULL AUTO_INCREMENT,
  `first_name` varchar(20) DEFAULT NULL,
  `last_name` varchar(25) DEFAULT NULL,
  `email` varchar(25) DEFAULT NULL,
  `phone_number` varchar(20) DEFAULT NULL,
  `job_id` varchar(10) DEFAULT NULL,
  `salary` double(10,2) DEFAULT NULL,
  `commission_pct` double(4,2) DEFAULT NULL,
  `manager_id` int(6) DEFAULT NULL,
  `department_id` int(4) DEFAULT NULL,
  `hiredate` datetime DEFAULT NULL,
  PRIMARY KEY (`employee_id`),
  KEY `dept_id_fk` (`department_id`),
  KEY `job_id_fk` (`job_id`),
  CONSTRAINT `dept_id_fk` FOREIGN KEY (`department_id`) REFERENCES `departments` (`department_id`),
  CONSTRAINT `job_id_fk` FOREIGN KEY (`job_id`) REFERENCES `jobs` (`job_id`)
) ENGINE=InnoDB AUTO_INCREMENT=207 DEFAULT CHARSET=gb2312;

/*Data for the table `employees` */

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‘);

/*Table structure for table `jobs` */

DROP TABLE IF EXISTS `jobs`;

CREATE TABLE `jobs` (
  `job_id` varchar(10) NOT NULL,
  `job_title` varchar(35) DEFAULT NULL,
  `min_salary` int(6) DEFAULT NULL,
  `max_salary` int(6) DEFAULT NULL,
  PRIMARY KEY (`job_id`)
) ENGINE=InnoDB DEFAULT CHARSET=gb2312; 

/*Data for the table `jobs` */

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);

/*Table structure for table `locations` */

DROP TABLE IF EXISTS `locations`;

CREATE TABLE `locations` (
  `location_id` int(11) NOT NULL AUTO_INCREMENT,
  `street_address` varchar(40) DEFAULT NULL,
  `postal_code` varchar(12) DEFAULT NULL,
  `city` varchar(30) DEFAULT NULL,
  `state_province` varchar(25) DEFAULT NULL,
  `country_id` varchar(2) DEFAULT NULL,
  PRIMARY KEY (`location_id`)
) ENGINE=InnoDB AUTO_INCREMENT=3201 DEFAULT CHARSET=gb2312;

/*Data for the table `locations` */

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‘);

2、基础查询

　　类似system.out.print("打印东西");

　　特点：

　　a. 查询列表可以是：表中的字段、常量值、表达式、函数

　　b. 查询的结果可以是一个虚拟表格。

USE myemployees;

#1.查询表中的单个字段
SELECT last_name FROM employees;

#2.查询表中多个字段
SELECT last_name,salary,email FROM employees;

#3.查询表中的所有字段
SELECT * FROM employees;

#4.查询常量
# select 常量值;
# 注意：字符型和日期型的常量值必须用单引号引起来，数值型不需要
SELECT 100;
SELECT ‘join‘;

#5.查询函数
#select 函数名(实参列表);
SELECT VERSION();

#6.查询表达式 
SELECT 100%98;

#7.起别名
/*
1.便于理解
2.如果要查询的字段有重名的情况,使用别名区分
*/
#方式一:使用AS
SELECT 100%98 AS 结果;
SELECT last_name AS 姓,first_name AS 名 FROM employees;

#方式二:使用空格
SELECT last_name 姓,first_name 名 FROM employees;

#案例:查询salary,结果显示 out put
SELECT salary AS "out put" FROM employees;

#8.去重
# select distinct 字段名 from 表名;
#案例:查询员工表中涉及的所有部门编号
SELECT DISTINCT department_id FROM employees;

#9.+号的作用
#案例:查询员工的名和姓,并显示为姓名
/*
java中的+号:
1.运算符:两个操作数都为数据型
2.连接符:只要有一个操作数为字符串


mysql中的+号:
只能作为运算符

select 100+90; 两个操作数都为数值型,做加法运算
select ‘123+90‘;其中一方为字符型,试图将字符型数值转换为数值型
        如果转换成功,则继续做加法运算
select ‘john‘+90; 如果转换失败,则将字符型数值转换成0

select null+0; 只要其中一方为null,则结果肯定为null.
*/
SELECT last_name+first_name AS 姓名 FROM employees; 

#10.【补充】concat函数 
/*
功能：拼接字符
select concat(字符1，字符2，字符3,...);
*/
SELECT CONCAT(‘a‘,‘b‘,‘c‘) AS 结果 FROM employees;

SELECT CONCAT(last_name,first_name) AS 姓名 FROM employees;

#11.【补充】ifnull函数
#功能：判断某字段或表达式是否为null，如果为null 返回指定的值，否则返回原本的值

SELECT IFNULL(commission_pct,0) FROM employees;

#12.【补充】isnull函数
#功能：判断某字段或表达式是否为null，如果是，则返回1，否则返回0

3、条件查询

1、按条件表达式筛选
条件运算符:> < = != <> >= <= <=>安全等于
2、按逻辑表达式筛选
逻辑运算符:&& || |
and or not
&& 和 and:两个条件都为true，结果为true，反之为false
|| 和 or:只要有一个条件为true，结果为true，反之为false
! 或 not:如果连接的条件本身为false，结果为true，反之为false
3、模糊查询
like:一般搭配通配符使用，可以判断字符型或数值型
通配符：%任意多个字符，_任意单个字符
like、between and、in、is null

实例

#一.按条件表达式筛选

#案例1:查询工资>12000的员工信息
SELECT * FROM employees WHERE salary>12000;

#案例2:查询部门编号不等于90号的员工名和部门编号
SELECT last_name,department_id FROM employees WHERE department_id <> 90;

#二、按逻辑表达式筛选

#案例1:查询工资z在10000到20000之间的员工名、工资及奖金
SELECT last_name,salary,commission_pct FROM employees WHERE salary>=10000 AND salary<=20000;

#案例2:查询部门编号不是在90-110之间,或者工资高于15000的员工信息
SELECT * FROM employees WHERE department_id <90 OR department_id>110 OR salary>15000;


#三、模糊查询

#1.like

#案例1:查询员工名中包含字符a的员工信息
SELECT * FROM employees WHERE last_name LIKE ‘%a%‘;

#案例2:查询员工名中第三个字符为b，第五个字符为a的员工名和工资
SELECT last_name,salary FROM employees WHERE last_name LIKE ‘__b_a%‘;

#案例3:查询员工名种第二个字符为_的员工名
SELECT last_name FROM employees WHERE last_name LIKE ‘_\_%‘;

#2.between and

#案例1:查询员工编号在100到120之间的员工信息
SELECT * FROM employees WHERE employee_id>=100 AND employee_id<=120;

SELECT * FROM employees WHERE employee_id BETWEEN 100 AND 120;

/*注意事项：
1.提高语句简洁度
2.包含临界值
3.两个临界值不能调换顺序
*/

#3.in
/*
含义:判断某字段的值是否属于in列表中的某一项
特点:
 1.使用in提高语句简洁度
 2.in列表的值类型必须一致或兼容
*/
#案例1:查询员工的工种编号是IT_PROG、AD_VP、AD_PRES中的一个员工名和工种编号

SELECT last_name,job_id FROM employees WHERE job_id=‘IT_PROG‘ OR job_id=‘AD_PRES‘ OR job_id=‘AD_VP‘;

SELECT last_name,job_id FROM employees WHERE job_id IN(‘IT_PROG‘,‘AD_PRES‘,‘AD_VP‘);

#4.is null
/*
=或<>不能用于判断null值
is null 或 is not null 可以判断null值
*/
#案例1:查询没有奖金的员工名和奖金率

SELECT last_name,commission_pct FROM employees WHERE commission_pct IS NULL;

SELECT last_name,commission_pct FROM employees WHERE commission_pct IS NOT NULL;

#安全等于<=>

#案例1:查询没有奖金的员工名和奖金率

SELECT last_name,commission_pct FROM employees WHERE commission_pct <=> NULL;

#案例2:查询工资为12000的员工信息
SELECT last_name,commission_pct FROM employees WHERE salary <=> 12000;

#is null PK <=>
#          普通类型的数值    null值        可读性
# is null    ×          √          √
# <=>        √          √          ×

4、排序查询

1.asc代表的是升序，desc代表降序，不写默认为升序
2.order by子句中可以支持单个字段、多个字段、表达式、函数、别名
3.order by子句一般是放在查询语句的最后面,limit子句除外

#案例1:查询员工信息,要求工资从高到低排序
SELECT * FROM employees ORDER BY salary DESC;
SELECT * FROM employees ORDER BY salary;

#案例2:查询部门编号是>=90，按入职时间的先后进行排序
SELECT * FROM employees WHERE department_id>=90 ORDER BY hiredate ASC;

#案例3:按年薪的高低显示员工的信息和年薪【按表达式排序】
SELECT *,salary*12*(1+IFNULL(commission_pct,0)) 年薪 FROM employees 
ORDER BY salary*12*(1+IFNULL(commission_pct,0)) DESC; 

#案例4:按年薪的高低显示员工的信息和年薪【按别名排序】
SELECT *,salary*12*(1+IFNULL(commission_pct,0)) 年薪 FROM employees 
ORDER BY salary*12*(1+IFNULL(commission_pct,0)) 年薪 DESC; 

#案例5:按姓名的长度显示员工的姓名和工资【按函数排序】
SELECT LENGTH(last_name) 字节长度,last_name,salary
FROM employees
ORDER BY LENGTH(last_name) DESC;

#案例6:查询员工共信息,要求按工资排序，再按员工编号排序【按多个字段排序】
SELECT * FROM employees
ORDER BY salary ASC,employee_id DESC;

5、常见函数

单行函数

单行函数分类：字符函数、数学函数、日期函数、其他函数、流程控制函数

字符函数具体案例：
#一.字符函数
#1.length 获取参数值的字节值
SELECT LENGTH(‘subei‘);
SELECT LENGTH(‘鬼谷子qwe‘);

SHOW VARIABLES LIKE ‘%char%‘;

#2.concat 拼接字符串
SELECT CONCAT(last_name,‘_‘,first_name) 姓名 FROM employees;

#3.upper:变大写、lower：变小写

SELECT UPPER(‘ton‘);
SELECT LOWER(‘ton‘);

#示例：将姓变大写，名变小写，然后拼接
SELECT CONCAT(UPPER(last_name),LOWER(first_name)) 姓名 FROM employees;


#4.substr、substring
#注意:索引从1开始

#截取从指定所有处后面的所以字符
SELECT SUBSTR(‘吴刚伐桂在天上‘,4) out_put;

#截取从指定索引处指定字符长度的字符
SELECT SUBSTR(‘吴刚伐桂在天上‘,1,2) out_put;

#案例:姓名中首字符大写,其他字符小写，然后用_拼接,显示出来
SELECT CONCAT(UPPER(SUBSTR(last_name,1,1)),‘_‘,LOWER(SUBSTR(last_name,2))) out_put FROM employees;

#5.instr:获取子串第一次出现的索引,找不到返回0
SELECT INSTR(‘MySQL技术进阶‘,‘技术‘) AS out_put;

#6.trim:去前后空格

SELECT LENGTH(TRIM(‘    霍山    ‘)) AS out_put;

SELECT TRIM(‘+‘ FROM ‘++++李刚+++刘邦+++‘) AS out_put;

#7.lpad:用指定的字符实现左填充指定长度
SELECT LPAD(‘梅林‘,8,‘+‘) AS out_put;

#8.rpad:用指定的字符实现右填充指定长度
SELECT RPAD(‘梅林‘,5,‘&‘) AS out_put;

#9.replace:替换
SELECT REPLACE(‘莉莉伊万斯的青梅竹马是詹姆‘,‘詹姆‘,‘斯内普‘) AS out_put;

数学函数具体案例：
#1.round:四舍五入
SELECT ROUND(1.45);
SELECT ROUND(1.567,2);

#2.ceil:向上取整,返回>=该参数的最小整数
SELECT CEIL(1.005);
SELECT CEIL(-1.002);

#3.floor:向下取整,返回<=该参数的最大整数
SELECT FLOOR(-9.99);

#4.truncate:截断
SELECT TRUNCATE(1.65,1);

#5.mod:取余
SELECT MOD(10,3);

#6.rand:获取随机数，返回0-1之间的小数
SELECT RAND();
?
日期函数具体案例：
#1.now:返回当前系统时间+日期
SELECT NOW();

#2.year:返回年
SELECT YEAR(NOW());
SELECT YEAR(hiredate) 年 FROM employees;

#3.month:返回月
#MONTHNAME:以英文形式返回月
SELECT MONTH(NOW());
SELECT MONTHNAME(NOW());

#4.day:返回日
#DATEDIFF:返回两个日期相差的天数
SELECT DAY(NOW());
SELECT DATEDIFF(‘2020/06/30‘,‘2020/06/21‘);

#5.str_to_date:将字符通过指定格式转换成日期
SELECT STR_TO_DATE(‘2020-5-13‘,‘%Y-%c-%d‘) AS out_put;

#6.date_format:将日期转换成字符
SELECT DATE_FORMAT(‘2020/6/6‘,‘%Y年%m月%d日‘) AS out_put;
SELECT DATE_FORMAT(NOW(),‘%Y年%m月%d日‘) AS out_put;

#7.curdate:返回当前日期
SELECT CURDATE();

#8.curtime:返回当前时间
SELECT CURTIME();

其他函数具体案例：
#version 当前数据库服务器的版本
SELECT VERSION();

#database 当前打开的数据库
SELECT DATABASE();

#user当前用户
SELECT USER();

#password(‘字符‘)：返回该字符的密码形式
SELECT PASSWORD(‘a‘);

#md5(‘字符‘):返回该字符的md5加密形式
SELECT MD5(‘a‘);
?
流程控制函数具体案例：
#1.if函数: if else效果

SELECT IF(10<5,‘大‘,‘小‘);

SELECT last_name,commission_pct,IF(commission_pct IS NULL,‘没奖金！！！‘,‘有奖金!!!‘) 备注 FROM employees;

#2.case函数
#使用一:switch case 的效果
/*
java中
switch(变量或表达式){
    case 常量1:语句1;break;
    ...
    default:语句n;break;
}

mysql中

case 要判断的变量或表达式
when 常量1 then 要显示的值1或语句1
when 常量2 then 要显示的值2或语句2
...
else 要显示的值n或语句n
end

#案例:查询员工的工资,要求:

部门号=30,显示的工资为1.1倍
部门号=40,显示的工资为1.2倍
部门号=50,显示的工资为1.3倍
其他部门,显示的工资为原工资

*/

SELECT salary 原始工资,department_id,
CASE department_id
WHEN 30 THEN salary*1.1
WHEN 40 THEN salary*1.2
WHEN 50 THEN salary*1.3
ELSE salary
END AS 新工资
FROM employees;

#3.case函数的使用二:类似于多重if
/*
java中:
if(条件1){
    语句1;
}else if(条件2){
    语句2;
}
...
else{
    语句n;
}    

mysql中:
case 
when 条件1 then 要显示的值1或语句1
when 条件2 then 要显示的值2或语句2
...
else 要显示的值n或语句n
end

*/

#案例:查询员工的工资的情况
/*
如果工资>20000，显示A级别
如果工资>15000，显示B级别
如果工资>10000，显示c级别
否则，显示D级别
*/

SELECT salary,
CASE
WHEN salary>20000 THEN ‘A‘
WHEN salary>15000 THEN ‘B‘
WHEN salary>10000 THEN ‘C‘
ELSE ‘D‘
END AS 工资等级
FROM employees;

分组函数

功能：用作统计使用，又称为聚合函数或统计函数或组函数
分类：sum 求和、avg 平均值、max 最大值、min最小值count 计算个数
特点:
- 1.sum和avg一般用于处理数值型
  max、min、count可以处理任何数据类型
- 2.以上分组函数都忽略null
- 3.都可以搭配distinct使用，实现去重的统计
  select sum(distinct 字段) from 表;
- 4.count函数
  count(字段)：统计该字段非空值的个数
  count(*):统计结果集的行数
- 5.和分组函数一同查询的字段，要求是group by后出现的字段

#1.简单使用
SELECT SUM(salary) FROM employees;
SELECT AVG(salary) FROM employees;
SELECT MAX(salary) FROM employees;
SELECT MIN(salary) FROM employees;
SELECT COUNT(salary) FROM employees;

SELECT SUM(salary) 和,ROUND(AVG(salary),2) 平均,MAX(salary) 最高,MIN(salary) 最低,COUNT(salary) 个数
FROM employees;

#2.参数支持哪些数据类型

SELECT SUM(last_name),AVG(last_name) FROM employees;
SELECT SUM(hiredate),AVG(hiredate) FROM employees;

SELECT MAX(last_name),MIN(last_name) FROM employees;
SELECT MAX(hiredate),MIN(hiredate) FROM employees;

SELECT COUNT(commission_pct) FROM employees;
SELECT COUNT(last_name) FROM employees;

#3.是否忽略null

SELECT SUM(commission_pct),AVG(commission_pct) FROM employees;

SELECT commission_pct FROM employees;

SELECT SUM(commission_pct),AVG(commission_pct),SUM(commission_pct)/35,AVG(commission_pct)/107 FROM employees;

SELECT MAX(commission_pct),MIN(commission_pct) FROM employees;

SELECT COUNT(commission_pct) FROM employees;

#4.和distinct搭配

SELECT SUM(DISTINCT salary),SUM(salary) FROM employees;

SELECT COUNT(DISTINCT salary),COUNT(salary) FROM employees;

#5.count函数详解

SELECT COUNT(salary) FROM employees;
SELECT COUNT(*) FROM employees;
SELECT COUNT(1) FROM employees;
/*
效率上：
MyISAM存储引擎，count(*)最高
InnoDB存储引擎，count(*)和count(1)效率>count(字段)
*/

#6.和分组函数一同查询的字段有限制

SELECT AVG(salary),employee_id FROM employees;

6、分组查询

注意:查询列表必须特殊,要求是分组函数和group by后出现的字段

特点:

1.分组查询中的筛选条件分为两类

?            使用关键字    筛选的表    位置
?分组前筛选    where        原始表        group by的前面
?分组后筛选    having        分组后的结果    group by的后面
?1.分组函数做条件肯定是放在having子句中
?2.能用分组前筛选的，就优先考虑使用分组前筛选

2.group by子句支持单个字段分组，多个字段分组(多个字段之间用逗号隔开没有顺序要求),表达式或函数(使用较少)
3.也可以添加排序(排序放在整个分组查询的最后)

#引入:查询每个部门的平均工资
SELECT AVG(salary) FROM employees;

#案例1:查询每个工种的最高工资
SELECT MAX(salary),job_id FROM employees 
GROUP BY job_id;


#案例2:查询每个位置上的部门个数
SELECT COUNT(*),location_id
FROM departments
GROUP BY location_id;

#添加筛选条件
#案例1:查询邮箱中包含a字符的，每个部门的平均工资
SELECT AVG(salary),department_id FROM employees
WHERE email LIKE ‘%a%‘ GROUP BY department_id;

#案例2:查询有奖金的每个领导手下员工的最高工资
SELECT MAX(salary),manager_id FROM employees
WHERE commission_pct IS NOT NULL
GROUP BY manager_id;

#添加复杂的筛选条件
#案例1:查询哪个部门的员工个数>2
#1.查询每个部门的员工个数
SELECT COUNT(*),department_id FROM employees
GROUP BY department_id;

#2.根据1的结果进行筛选，查询哪个部门的员工个数大于2
SELECT COUNT(*),department_id FROM employees
GROUP BY department_id HAVING COUNT(*)>2;


#案例2:查询每个工种有奖金的员工的最高工资>12000的工种编号和最高工资 
#1.查询每个工种有奖金的员工的最高工资 
SELECT MAX(salary),job_id FROM employees 
WHERE commission_pct IS NOT NULL GROUP BY job_id; 

#2.根据结果继续筛选，最高工资>12000 

SELECT MAX(salary), job_id FROM employees 
WHERE commission_pct IS NOT NULL GROUP BY job_id 
HAVING MAX(salary)>12000; 

#按表达式或函数分组

#案例:按员工姓名的长度分组,查询每一组的员工个数,筛选员工个数>5

#1.查询每个长度的员工个数 
SELECT COUNT(*),LENGTH(last_name) len_name 
FROM employees GROUP BY LENGTH(last_name); 

#2.添加筛选条件
SELECT COUNT(*) c,LENGTH(last_name) len_name 
FROM employees GROUP BY len_name HAVING c>5;

#按多个字段查询
#案例:查询每个部门每个工种的员工的平均工资

SELECT AVG(salary),department_id,job_id
FROM employees GROUP BY department_id,job_id;

#添加排序
#案例:查询每个部门每个工种的员工的平均工资,按平均工资的高低查询

SELECT AVG(salary),department_id,job_id
FROM employees GROUP BY department_id,job_id
ORDER BY AVG(salary) DESC;

连接查看另一篇博文

7、子查询

含义:出现在其他语句中的select语句,称为子查询或内查询外部的查询语句，称为主查询或外查询。

#一、where或having后面
/*
1、标量子查询（单行子查询）
2、列子查询（多行子查询）
3、行子查询（多列多行）

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，一般搭配着单行操作符使用
> < >= <= = <>

列子查询，一般搭配着多行操作符使用
in、any/some、all

④子查询的执行优先于主查询执行，主查询的条件用到了子查询的结果
*/

#1.标量子查询★

#案例1：谁的工资比 Abel 高?

#①查询Abel的工资
SELECT salary
FROM employees
WHERE last_name = ‘Abel‘;

#②查询员工的信息，满足 salary>①结果
SELECT *
FROM employees
WHERE salary>(

    SELECT salary
    FROM employees
    WHERE last_name = ‘Abel‘
    
);

#案例2：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

#①查询141号员工的job_id
SELECT job_id FROM employees
WHERE employee_id = 141;

#②查询143号员工的salary
SELECT salary FROM employees
WHERE employee_id = 143;

#③查询员工的姓名，job_id 和工资，要求job_id=①并且salary>②

SELECT last_name,job_id,salary
FROM employees
WHERE job_id = (
    SELECT job_id
    FROM employees
    WHERE employee_id = 141
) AND salary>(
    SELECT salary
    FROM employees
    WHERE employee_id = 143

);

#案例3：返回公司工资最少的员工的last_name,job_id和salary

#①查询公司的最低工资
SELECT MIN(salary) FROM employees;

#②查询last_name,job_id和salary，要求salary=①
SELECT last_name,job_id,salary
FROM employees
WHERE salary=(
    SELECT MIN(salary)
    FROM employees
);

#案例4：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

#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基础上筛选，满足min(salary)>①
SELECT MIN(salary),department_id
FROM employees
GROUP BY department_id
HAVING MIN(salary)>(
    SELECT  MIN(salary)
    FROM employees
    WHERE department_id = 50
);

#非法使用标量子查询

SELECT MIN(salary),department_id
FROM employees
GROUP BY department_id
HAVING MIN(salary)>(
    SELECT  salary
    FROM employees
    WHERE department_id = 250
);

#2.列子查询（多行子查询）★
#案例1：返回location_id是1400或1700的部门中的所有员工姓名

#①查询location_id是1400或1700的部门编号
SELECT DISTINCT department_id
FROM departments
WHERE location_id IN(1400,1700);

#②查询员工姓名，要求部门号是①列表中的某一个

SELECT last_name
FROM employees
WHERE department_id  <>ALL(
    SELECT DISTINCT department_id
    FROM departments
    WHERE location_id IN(1400,1700)
);


#案例2：返回其它工种中比job_id为‘IT_PROG’工种任一工资低的员工的员工号、姓名、job_id 以及salary

#①查询job_id为‘IT_PROG’部门任一工资

SELECT DISTINCT salary FROM employees
WHERE job_id = ‘IT_PROG‘;

#②查询员工号、姓名、job_id 以及salary，salary<(①)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‘

) AND job_id<>‘IT_PROG‘;

#或
SELECT last_name,employee_id,job_id,salary
FROM employees
WHERE salary<(
    SELECT MAX(salary)
    FROM employees
    WHERE job_id = ‘IT_PROG‘

) AND job_id<>‘IT_PROG‘;


#案例3：返回其它部门中比job_id为‘IT_PROG’部门所有工资都低的员工   的员工号、姓名、job_id 以及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‘;

#3、行子查询（结果集一行多列或多行多列）

#案例：查询员工编号最小并且工资最高的员工信息

SELECT * FROM employees
WHERE (employee_id,salary)=(
    SELECT MIN(employee_id),MAX(salary)
    FROM employees
);

#①查询最小的员工编号
SELECT MIN(employee_id) FROM employees;

#②查询最高工资
SELECT MAX(salary) FROM employees;

#③查询员工信息
SELECT * FROM employees
WHERE employee_id=(
    SELECT MIN(employee_id)
    FROM employees
)AND salary=(
    SELECT MAX(salary)
    FROM employees
);


#二、select后面
/*
仅仅支持标量子查询
*/

#案例：查询每个部门的员工个数

SELECT d.*,(
    SELECT COUNT(*)
    FROM employees e
    WHERE e.department_id = d.`department_id`
 ) 个数
 FROM departments d;
 
 
#案例2：查询员工号=102的部门名
 
SELECT (
    SELECT department_name,e.department_id
    FROM departments d
    INNER JOIN employees e
    ON d.department_id=e.department_id
    WHERE e.employee_id=102
    
) 部门名;

#三、from后面
/*
将子查询结果充当一张表，要求必须起别名
*/

#案例：查询每个部门的平均工资的工资等级
#①查询每个部门的平均工资
SELECT AVG(salary),department_id
FROM employees GROUP BY department_id;

SELECT * FROM job_grades;

#②连接①的结果集和job_grades表，筛选条件平均工资 between lowest_sal and highest_sal

SELECT  ag_dep.*,g.`grade_level`
FROM (
    SELECT AVG(salary) ag,department_id
    FROM employees
    GROUP BY department_id
) ag_dep
INNER JOIN job_grades g
ON ag_dep.ag BETWEEN lowest_sal AND highest_sal;

#四、exists后面（相关子查询）
/*
语法：
exists(完整的查询语句)
结果：
1或0
*/
SELECT EXISTS(SELECT employee_id FROM employees WHERE salary=300000);

#案例1：查询有员工的部门名

#in
SELECT department_name
FROM departments d
WHERE d.`department_id` IN(
    SELECT department_id
    FROM employees
);

#exists
SELECT department_name
FROM departments d
WHERE EXISTS(
    SELECT *
    FROM employees e
    WHERE d.`department_id`=e.`department_id`
);

#案例2：查询没有女朋友的男神信息

#in
SELECT bo.*
FROM boys bo
WHERE bo.id NOT IN(
    SELECT boyfriend_id
    FROM beauty
);

#exists
SELECT bo.*
FROM boys bo
WHERE NOT EXISTS(
    SELECT boyfriend_id
    FROM beauty b
    WHERE bo.`id`=b.`boyfriend_id`
);

8、分页查询

· 应用场景：当要显示的数据，一页显示不全，需要分页提交sql请求。

①limit语句放在查询语句的最后
②公式
要显示的页数 page，每页的条目数size
 

select 查询列表 from 表
limit (page-1)*size,size;
 

size=10
page  
1 0
2   10
3 20

案例

#案例1：查询前五条员工信息
SELECT * FROM  employees LIMIT 0,5;
SELECT * FROM  employees LIMIT 5;

#案例2：查询第11条——第25条
SELECT * FROM employees LIMIT 10,15;

#案例3：有奖金的员工信息，并且工资较高的前10名显示出来
SELECT * FROM employees 
WHERE commission_pct IS NOT NULL 
ORDER BY salary DESC LIMIT 10 ;

9、联合查询

含义：union (联合、合并)：将多条查询语句的结果合并成一个结果。

· 语法

查询语句1

union 【all】

查询语句2

union 【all】

· 意义

· 1、将一条比较复杂的查询语句拆分成多条语句

· 2、适用于查询多个表的时候，查询的列基本是一致。

特点

· 1、要求多条查询语句的查询列数是一致的！

· 2、要求多条查询语句的查询的每一列的类型和顺序最好一致

· 3、union关键字默认去重，如果使用union all 可以包含重复项

案例

#引入的案例：查询部门编号>90或邮箱包含a的员工信息

SELECT * FROM employees WHERE email LIKE ‘%a%‘ OR department_id>90;

SELECT * FROM employees  WHERE email LIKE ‘%a%‘
UNION
SELECT * FROM employees  WHERE department_id>90;


#案例：查询中国用户中男性的信息以及外国用户中年男性的用户信息

SELECT id,cname,csex FROM t_ca WHERE csex=‘男‘
UNION
SELECT t_id,tName,tGender FROM t_ua WHERE tGender=‘male‘;