3.3.4 Retrieving Information from a Table
Select 命令从表格中取回信息
SELECT what_to_select
FROM which_table
WHERE conditions_to_satisfy
;
what_to_select 是你想看到的结果,可以是一些列,也可以是“*“表示所有列,which_table是你想查找信息的目标表格,where是可选的,如果选了,
conditions_to_satisfy是一个或者多个必须满足的条件。
3.3.4.1 Selecting All Data
查找所有资料:
mysql>SELECT * FROM pet;
Select 命令能方便的查看整个表格,例如,当你从你的源始资料加载到数据库中,你可能突然想到,生日可能不太正确,
生日应该是1989年,而不是1979年
最少有两种种方法可以做到
a.修改pet.txt,然后删除pet 表,再load data
mysql>DELETE FROM pet;
mysql>LOAD DATA LOCAL INFILE 'pet.txt' INTO TABLE pet;
b.Update
只修改错误的信息
mysql>
UPDATE pet SET birth = '1989-08-31' WHERE name = 'Bowser';
u
pdate只修改不符合要求的数据,不用重新加载源数据
3.3.4.2 Selecting Particular Rows
选择指定的行
从前面的情况可以看到,检索整个列表是很简单,只是忽略了Where关键字。但是比较常见的情况是你不需要整个表格,特别是当数据库很大的时候 。
相反,你只需要查看某一特定信息。
下面是查看Bowser的生日信息
mysql> SELECT * FROM pet WHERE name = 'Bowser';
从结果可以看出来,birth改成了1989年,不再是1979年。
注意,字符串类型是大小写不敏感的,bowser
写成BOWSER结果是一样的。
其他任意行的信息你都可以检索,比如:
mysql> SELECT * FROM pet WHERE birth >= '1998-1-1';
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可以使用And语句:mysql> SELECT * FROM pet WHERE species = 'dog' AND sex = 'f';
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AND是逻辑操作符,还有OR
AND 和OR可以混全使用,但AND优先级比OR高,所以用括号是比较好的选择
![](data:image/png;base64,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)
3.3.4.3 Selecting Particular Columns
选择特定的列
有时你不想看整个表,而只对其中的一列信息感兴趣,比如,你只想看宠物的出生,
mysql> SELECT name, birth FROM pet;
![](data:image/png;base64,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)
查看宠物的主人
mysql> SELECT owner FROM pet;
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这条语句只是简单的查出了主人,而有的却出现了多次,为了最小化输出,只需要加入Distinct
mysql>SELECT DISTINCT owner FROM pet;
你可以通过Where子句来组全行和列。
例如,只得到狗和猫的出生日期
3.3.4.4 Sorting Rows
对列排序
有时你需要排序,需要用到Order By命令
下面是对出生日期的排序
mysql>SELECT name, birth FROM pet ORDER BY birth;
默认排序是升序,如果你需要降序,需要加上DESC
mysql>SELECT name, species, birth FROM pet
->ORDER BY species, birth DESC;
事实 上,上面的DESC只根据birth列而对species没有影响。
3.3.4.5 Date Calculations
日期计算
mysql提供了一些函数,方便你计算日期,
计算宠物的寿命,使用TimeStampdiff()函数,参数是你想得到结果的单位,这里是年
mysql>SELECT name, birth, CURDATE(),
->TIMESTAMPDIFF(YEAR,birth,CURDATE()) AS age
->FROM pet;
目的是达到了,但是如果结果能按一定的顺序进行排序 ,会比较方便进行查找 ,我们加入Order By试试
mysql> SELECT name, birth, CURDATE(),
-> TIMESTAMPDIFF(YEAR,birth,CURDATE()) AS age
-> FROM pet ORDER BY name;
如果需要按年龄排序,只需要简单修改下:
mysql> SELECT name, birth, CURDATE(),
-> TIMESTAMPDIFF(YEAR,birth,CURDATE()) AS age
-> FROM pet ORDER BY age;
一条相似的语句可以查看哪些宠物已经死去,
这里因为,我这数据库建立时默认生成的death 是0000-00-00所以也是非NULL,否则应该只有最后一行数据Bowser
如果你想知道哪个动物下个月过生日怎么办?要做这种计算,年和日都是相关的,你只想提取月份数据,mysql提供了几个函数去提取日期中的一部分,比如year(),month(),dayofmonth()
这里要用到的是month函数
mysql> SELECT name, birth, MONTH(birth) FROM pet;
现在要找出下个月过生的宠物也是很简单的,比如 ,现在 4月,那么下个月就是五月
mysql> SELECT name, birth FROM pet WHERE MONTH(birth) = 5;
注意,如果现在是12月,要记住你该查找的是1月而不是13月
你可以通过下面的语句来查询结果,而不用在意现在是哪个月,所以你不要使用特定的月份。Date add()函数能加一定的时间到给定的时间,如果你身Curdate()加一定时间
然后就可以通过month()函数提取你想找的下个月过后日的宠物了。
mysql> SELECT name, birth FROM pet
-> WHERE MONTH(birth) = MONTH(DATE_ADD(CURDATE(),INTERVAL 1 MONTH));
另一种方法是对当前月份取余,对12的余数如果是0,那么加1
mysql> SELECT name, birth FROM pet
-> WHERE MONTH(birth) = MOD(MONTH(CURDATE()), 12) + 1;
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Month()返回1~12的数,Mod()返回0~11的数,所以1应该放在mod()后面,否则,就会从11月直接跳到了1月。
3.3.4.6 Working with NULL Values
处理空值
i当你使用Null时一定会”大吃一斤“,因为Null意味着未知的值,而与其他值有些不同
可以通过is null 和is not null操作符
mysql>SELECT 1 IS NULL, 1 IS NOT NULL;
不能使用大于号,小于号来操作,看下面
mysql> SELECT 1 = NULL, 1 <> NULL, 1 < NULL, 1 > NULL;
因为任意一个数与Null运算的结果都是Null,你无法得到任何有意义的结果
在mysql中,0或者Null表示False,任何其他值为True,真值的默认Boolean值为1
两个Null被认为是相等 的,
如果你进行升序排序,Null会排在第一个,降序排在最后一个
一个比较常见的错误是,假想以为不能向一个定义为非空的列中插入0或者空字符串,但事实并不是这样的,可以通过下面的测试看出来
mysql>SELECT 0 IS NULL, 0 IS NOT NULL, '' IS NULL, '' IS NOT NULL;
上面的图说明 可以向一个定义为not null的列中插入0 或者空的字符串。
3.3.4.7 Pattern Matching
模式匹配
mysql提供标准的类似sql的匹配模式,同时支持正则表达式扩展,类似unix上的vi ,grep,sed。
- 匹配任意单个字符
% 匹配任意个字符,包括0个
在mysql中,sql模式默认是大小写不敏感的,下面是一些例子,不要用= 和<>,e用Like , Not Like
查找以b开头的名字:
mysql>SELECT * FROM pet WHERE name LIKE 'b%';
以fy结尾的名字
mysql> SELECT * FROM pet WHERE name LIKE '%fy';
![](data:image/png;base64,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)
包含字母w的名字
mysql> SELECT * FROM pet WHERE name LIKE '%w%';
![](data:image/png;base64,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)
包含5个字母 的名字,用5个 _
mysql>SELECT * FROM pet WHERE name LIKE '_____';
注意,这是5个下划线,
![](data:image/png;base64,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)
其他一些用了正则的匹配模式,当你使用时应该加上REGEXP
或者 NOT REGEXP(也可以用RLike 或者Not RLkie)
下面是一些扩展支持的正则的语法:
. 匹配任意单字符
[...] 匹配括号内的任意单字符,如:[abc],会匹配a,b,或者c,用横杠表示一个范围,如:[a-z]会匹配任意字母(因为大小写不敏感),[0-9]会匹配任意数字
* 会匹配一个或多个它前面的表达式,如x* 会匹配任意个x,[0-9]*会匹配任意个数字,.*会匹配任意个任何字符
Regexp 只要是这种模式就会匹配成功,而Like则必须匹配完整的值。
^,$ ^匹配以某某开头,而$匹配以某某结尾
下面是匹配以字母 b开头的名字
mysql> SELECT * FROM pet WHERE name REGEXP '^b';
![](data:image/png;base64,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)
假如你的强迫症又犯了,你说,我就是要大小写敏感,那么也是可以的,用BINARY关键字,使其转化成二进制字符串
下面这条语句 查询以小写的b开头的名字
mysql>SELECT * FROM pet WHERE name REGEXP BINARY '^b';
用$符匹配以fy结尾 的名字:
mysql>SELECT * FROM pet WHERE name REGEXP 'fy$';
包含字母w的人名:
mysql>SELECT * FROM pet WHERE name REGEXP 'w';
之所以能匹配成功是因为只要模式相同,正则就能匹配成功,而不用前面那样要在两端加上%通配符
要匹配五个字符的名字,用^来限制开头,$限制结尾,在两者之间加上5个.
mysql>SELECT * FROM pet WHERE name REGEXP '^.....$';
你也可以用{n}操作符,表示重复n次
mysql> SELECT * FROM pet WHERE name REGEXP '^.{5}$';
![](data:image/png;base64,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)
3.3.4.8 Counting Rows
计算行数
建立数据库当然是为了用,比如你想知道,谁谁谁有几只宠物,或者你共有几只宠物,其实这只需要知道这个表有多少行就行了,因为一行一只宠物
Count(*)函数统计表有多少行,这样你就知道你有多少宠物了
mysql>SELECT COUNT(*) FROM pet;
如果你想知道每个人有几只宠物,
mysql>SELECT owner, COUNT(*) FROM pet GROUP BY owner;
这里用了Group By 让每个主人的宠物组合在一起
下面是统计不同种类动物的数量
mysql>SELECT species, COUNT(*) FROM pet GROUP BY species;
不同性别的动物
mysql>SELECT sex, COUNT(*) FROM pet GROUP BY sex;
第一行空白,表示性别未知。
每种动物,不同性别数量
mysql> SELECT species, sex, COUNT(*) FROM pet GROUP BY species, sex;
有时你并不想知道整个列表的数据,比如你只想看看dog和cat的情况:
mysql> SELECT species, sex, COUNT(*) FROM pet
-> WHERE species = 'dog' OR species = 'cat'
-> GROUP BY species, sex;
或者你想忽略那些性别未知的动物
mysql> SELECT species, sex, COUNT(*) FROM pet
-> WHERE sex IS NOT NULL
-> GROUP BY species, sex;
那么,这里有一点问题,上面的bird那个空白怎么解释呢,如果你记得前面的内容,空字符串与Null是不相等的。
如果你只选择其中的一列,那么Group By也就选择对方列,否则下面的情况就会发生如果选择了完全匹配分组模式
mysql> SET sql_mode = 'ONLY_FULL_GROUP_BY';
下面的语句就会报错:
mysql> SELECT owner, COUNT(*) FROM pet;
ERROR 1140 (42000): Mixing of GROUP columns (MIN(),MAX(),COUNT()...)
with no GROUP columns is illegal if there is no GROUP BY clause
如果没有选择完全匹配分组模式:
这条语句就会把整个表当成一个组,然后选择的内容可能是任意一列的内容,就是说结果是随机的
mysql> SET sql_mode = '';
Query OK, 0 rows affected (0.00 sec)
mysql> SELECT owner, COUNT(*) FROM pet;
![](data:image/png;base64,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3.3.4.9 Using More Than one Table
使用多张表格
这张表只保存了现在这些宠物的部分信息,如果你想保存别的信息,或者又有小宠物出生blablabla
那么再建一张表吧
mysql>CREATE TABLE event (name VARCHAR(20), date DATE,
->type VARCHAR(15), remark VARCHAR(255));
一定要加上路径:
mysql>SELECT pet.name,
->(YEAR(date)-YEAR(birth)) - (RIGHT(date,5)<RIGHT(birth,5)) AS age,
->remark
->FROM pet INNER JOIN event
->ON pet.name = event.name
->WHERE event.type = 'litter';
![](data:image/png;base64,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当从多张表格组合(join)信息时,你需要确认有多少记录可以匹配到另外一张表,这里很容易实现是因为都有name这一列,这里用了on子句去匹配 ,因为两张表里面都有name这一列
组合两张不同的表格是不必要的,有时你只需要在一张表格里面组合就行了(此处翻译可能有错误,请根据结果揣摩一下)
例如,你需要知道哪些可以繁殖配对。
mysql>SELECT p1.name, p1.sex, p2.name, p2.sex, p1.species
->FROM pet AS p1 INNER JOIN pet AS p2
->ON p1.species = p2.species AND p1.sex = 'f' AND p2.sex = 'm';
![](data:image/png;base64,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)
在这条语句中,我们对列指定了别名,然后直接使用相对应的列。