python-day43--单表查询之关键字执行优先级（重点）
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一、关键字的执行优先级(重点)
1.关键字执行优先级
from where #约束条件（在数据产生之前执行） group by #分组 没有分组则默认一组 按照select后的字段取得一张新的虚拟表，有聚合函数则执行聚合函数 having #过滤条件 ,having 是根据内存中的虚拟表 过滤的 ；出现having的时候肯定是有group by，不然没意义 distinct #去重 #在select 之后执行 忽略先后顺序 order by #排序 #在select 之后执行 忽略先后顺序 limit #限制查询的记录数 #在select 之后执行 忽略先后顺序
1.找到表:from
2.拿着where指定的约束条件，去文件/表中取出一条条记录
3.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
4.按照select后的字段得到一张新的虚拟表，如果有聚合函数，则将组内数据进行聚合
5.将4的结果过滤：having
6.查出结果：select
7.去重
8.将结果按条件排序：order by
9.限制结果的显示条数
2.having与where不一样的地方在于!!!!!!
#！！！执行优先级从高到低：where > group by > 聚合函数 > having #1. Where 是一个约束声明，使用Where约束来自数据库的数据，Where是在结果返回之前起作用的（先找到表，按照where的约束条件，从表(文件)中取出数据），Where中不能使用聚合函数。 #2. Having是一个过滤声明，是在查询返回结果集以后对查询结果进行的过滤操作（先找到表，按照where的约束条件，从表(文件)中取出数据，然后group by分组，如果没有group by则所有记录整体为一组，然后执行聚合函数，然后使用having对聚合的结果进行过滤），在Having中可以使用聚合函数。 #3. having可以放到group by之后，而where只能放到group by之前 #4. 在查询过程中聚合语句(sum,min,max,avg,count)要比having子句优先执行。而where子句在查询过程中执行优先级高于聚合语句。
验证不同之处：
#验证之前再次强调：执行优先级从高到低：where > group by > 聚合函数 > having select count(id) from employee where salary > 10000; #正确，分析：where先执行，后执行聚合count(id),然后select出结果 select count(id) from employee having salary > 10000;#错误，分析：先执行聚合count(id)，后执行having过滤，无法对id进行salary>10000的过滤
以上两条sql的顺序是：
1:找到表employee--->用where过滤---->没有分组则默认一组执行聚合count(id)--->select执行查看组内id数目
2:找到表employee--->没有分组则默认一组执行聚合count(id)---->having 基于上一步聚合的结果（此时只有count(id)字段了）进行salary>10000的过滤，很明显，根本无法获取到salary字段
其他需要注意的问题：
select post,group_concat(name) from employee group by post having salary > 10000;#错误，分组后无法直接取到salary字段 select post,group_concat(name) from employee group by post having avg(salary) > 10000; 正确
例子：
例子： select max(salary) from t1 where id > 2 group by depart_id having count(id) > 2; 执行顺序： from t1 ——> where id >2 ——> group by depart_id ——> max(salary)和count(id) 然后才执行 having count(id) >2 最后打印 辅助理解：select 333333333 from t1 where id > 2 group by depart_id having 4 > 2;
练习：
#练习 select post,count(id),group_concat(name) from emp group by post having count(id) < 2; #可以运行 select count(id) from emp having id > 15; #运行错误
3.命名
命名： select post,avg(salary) as 平均工资 from emp group by post having avg(salary) > 10000; select post 岗位名,avg(salary) 平均工资 from emp group by post having avg(salary) > 10000; #命名时 as 可以省略
4.order by关键字
select * from emp order by salary; #默认就是升序 select * from emp order by salary asc; #升序 select * from emp order by salary desc; #降序 select * from emp order by age asc,salary desc; #先按照年龄从小到大排，如果年龄分不出胜负（即值相同）再按照salary从大到小排。
练习：
select * from emp order by age asc,hire_date desc; select post 岗位名,avg(salary) 平均工资 from emp group by post having avg(salary) > 10000 order by avg(salary) asc;
5.通过四则运算查询：
select name, salary*12 AS Annual_salary FROM employee; select name, salary*12 Annual_salary FROM employee; select * from employee where id%2=1;
6.定义显示格式：
1.CONCAT() 函数用于连接字符串 SELECT CONCAT(‘姓名: ‘,name,‘ 年薪: ‘, salary*12) AS Annual_salary FROM employee; mysql> SELECT CONCAT(‘姓名: ‘,name,‘ 年薪: ‘, salary*12) AS Annual_salary -> FROM emp; +---------------------------------------+ | Annual_salary | +---------------------------------------+ | 姓名: egon 年薪: 87603.96 | | 姓名: alex 年薪: 12000003.72 | | 姓名: wupeiqi 年薪: 99600.00 | | 姓名: yuanhao 年薪: 42000.00 | | 姓名: liwenzhou 年薪: 25200.00 | | 姓名: jingliyang 年薪: 108000.00 | | 姓名: jinxin 年薪: 360000.00 | | 姓名: 成龙 年薪: 120000.00 | | 姓名: 歪歪 年薪: 36001.56 | | 姓名: 丫丫 年薪: 24004.20 | | 姓名: 丁丁 年薪: 12004.44 | | 姓名: 星星 年薪: 36003.48 | | 姓名: 格格 年薪: 48003.96 | | 姓名: 张野 年薪: 120001.56 | | 姓名: 程咬金 年薪: 240000.00 | | 姓名: 程咬银 年薪: 228000.00 | | 姓名: 程咬铜 年薪: 216000.00 | | 姓名: 程咬铁 年薪: 204000.00 | +---------------------------------------+ 18 rows in set (0.00 sec) 2.CONCAT_WS() 第一个参数为分隔符 SELECT CONCAT_WS(‘:‘,name,salary*12) AS Annual_salary FROM employee; mysql> SELECT CONCAT_WS(‘:‘,name,salary*12) AS Annual_salary -> FROM emp; +----------------------+ | Annual_salary | +----------------------+ | egon:87603.96 | | alex:12000003.72 | | wupeiqi:99600.00 | | yuanhao:42000.00 | | liwenzhou:25200.00 | | jingliyang:108000.00 | | jinxin:360000.00 | | 成龙:120000.00 | | 歪歪:36001.56 | | 丫丫:24004.20 | | 丁丁:12004.44 | | 星星:36003.48 | | 格格:48003.96 | | 张野:120001.56 | | 程咬金:240000.00 | | 程咬银:228000.00 | | 程咬铜:216000.00 | | 程咬铁:204000.00 | +----------------------+ 18 rows in set (0.00 sec)
7. limit 限制查询的记录数
1. select * from emp limit 10; #第一条到第十条记录
mysql> select * from emp limit 10; +----+------------+--------+-----+------------+-----------------------------------------+--------------+------------+--------+-----------+ | id | name | sex | age | hire_date | post | post_comment | salary | office | depart_id | +----+------------+--------+-----+------------+-----------------------------------------+--------------+------------+--------+-----------+ | 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 | | 2 | alex | male | 78 | 2015-03-02 | teacher | NULL | 1000000.31 | 401 | 1 | | 3 | wupeiqi | male | 81 | 2013-03-05 | teacher | NULL | 8300.00 | 401 | 1 | | 4 | yuanhao | male | 73 | 2014-07-01 | teacher | NULL | 3500.00 | 401 | 1 | | 5 | liwenzhou | male | 28 | 2012-11-01 | teacher | NULL | 2100.00 | 401 | 1 | | 6 | jingliyang | female | 18 | 2011-02-11 | teacher | NULL | 9000.00 | 401 | 1 | | 7 | jinxin | male | 18 | 1900-03-01 | teacher | NULL | 30000.00 | 401 | 1 | | 8 | 成龙 | male | 48 | 2010-11-11 | teacher | NULL | 10000.00 | 401 | 1 | | 9 | 歪歪 | female | 48 | 2015-03-11 | sale | NULL | 3000.13 | 402 | 2 | | 10 | 丫丫 | female | 38 | 2010-11-01 | sale | NULL | 2000.35 | 402 | 2 | +----+------------+--------+-----+------------+-----------------------------------------+--------------+------------+--------+-----------+ 10 rows in set (0.00 sec)
2.从哪开始，往后取几条 第一个数字表示的是索引，第二个代表步距
select * from emp limit 0,3; #第一条开始往后三条
select * from emp limit 0,3; #第一条开始往后三条 +----+---------+------+-----+------------+-----------------------------------------+--------------+------------+--------+-----------+ | id | name | sex | age | hire_date | post | post_comment | salary | office | depart_id | +----+---------+------+-----+------------+-----------------------------------------+--------------+------------+--------+-----------+ | 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 | | 2 | alex | male | 78 | 2015-03-02 | teacher | NULL | 1000000.31 | 401 | 1 | | 3 | wupeiqi | male | 81 | 2013-03-05 | teacher | NULL | 8300.00 | 401 | 1 | +----+---------+------+-----+------------+-----------------------------------------+--------------+------------+--------+-----------+ 3 rows in set (0.00 sec) select * from emp limit 3,3; +----+------------+--------+-----+------------+---------+--------------+---------+--------+-----------+ | id | name | sex | age | hire_date | post | post_comment | salary | office | depart_id | +----+------------+--------+-----+------------+---------+--------------+---------+--------+-----------+ | 4 | yuanhao | male | 73 | 2014-07-01 | teacher | NULL | 3500.00 | 401 | 1 | | 5 | liwenzhou | male | 28 | 2012-11-01 | teacher | NULL | 2100.00 | 401 | 1 | | 6 | jingliyang | female | 18 | 2011-02-11 | teacher | NULL | 9000.00 | 401 | 1 | +----+------------+--------+-----+------------+---------+--------------+---------+--------+-----------+ 3 rows in set (0.00 sec) select * from emp limit 6,3; +----+--------+--------+-----+------------+---------+--------------+----------+--------+-----------+ | id | name | sex | age | hire_date | post | post_comment | salary | office | depart_id | +----+--------+--------+-----+------------+---------+--------------+----------+--------+-----------+ | 7 | jinxin | male | 18 | 1900-03-01 | teacher | NULL | 30000.00 | 401 | 1 | | 8 | 成龙 | male | 48 | 2010-11-11 | teacher | NULL | 10000.00 | 401 | 1 | | 9 | 歪歪 | female | 48 | 2015-03-11 | sale | NULL | 3000.13 | 402 | 2 | +----+--------+--------+-----+------------+---------+--------------+----------+--------+-----------+ 3 rows in set (0.00 sec)
8.distinct 去重
select distinct sex from emp; +--------+ | sex | +--------+ | male | | female | +--------+ 2 rows in set (0.00 sec)
9.where name 正则
SELECT * FROM employee WHERE name REGEXP ‘^ale‘; SELECT * FROM employee WHERE name REGEXP ‘on$‘; SELECT * FROM employee WHERE name REGEXP ‘m{2}‘; 小结：对字符串匹配的方式 WHERE name = ‘egon‘; WHERE name LIKE ‘yua%‘; WHERE name REGEXP ‘on$‘;
小练习：查看所有员工中名字是jin开头，n或者g结果的员工信息
select * from employee where name regexp ‘^jin.*[gn]$‘;
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