PRIMARY KEY, profession varchar(20) ); INSERT INTO profession (profession) SELECT profession FROM my_contacts GROUP BY profession ORDER BY profession; CREATE TABLE profession AS SELECT profession FROM my_contacts GROUP BY profession ORDER BY profession; ALTER TABLE profession ADD COLUMN id INT +INCREMENT PRIMARY KEY, profession varchar(20) ) AS SELECT profession FROM my_contacts GROUP BY profession 短短一个查询语句,就出现了五次“profession”,这五次profession效果各有不同,我们容易弄晕,但是SQL能够很轻易的分辨。 为了能让我们容易分辨,SQL推出了假名功能。
执行SQL : explain select * from tb_user where profession = '软件工程'; 查询走了联合索引。 B. 执行SQL,创建profession的单列索引:create index idx_user_pro on tb_user(profession); C. explain select * from tb_user ignore index(idx_user_pro) where profession = '软件工 程'; 3). force index select * from tb_user ignore index(idx_user_pro) where profession = '软件工 程'; C. force index explain select * from tb_user force index(idx_user_pro_age_sta) where profession = '软件工程';
not null comment '用户名', phone varchar(11) not null comment '手机号', email varchar(100) comment '邮箱', profession 状态', createtime datetime comment '创建时间' ) comment '系统用户表'; INSERT INTO tb_user (name, phone, email, profession lvbu666@163.com', '软件工程', 23, '1', '6', '2001-02-02 00:00:00'); INSERT INTO tb_user (name, phone, email, profession 为profession、age、status创建联合索引。 CREATE INDEX idx_user_pro_age_sta ON tb_user(profession,age,status); D. 为email建立合适的索引来提升查询效率。
to Console" }.Dump(); 支持嵌套和循环引用 var moaid = new Person { FirstName = "Moaid", LastName = "Hathot", Profession = Profession.Software }; var haneeni = new Person { FirstName = "Haneeni", LastName = "Shibli", Profession = Profession.Health }; moaid.Spouse = haneeni; haneeni.Spouse = moaid; moaid.Dump(); //You can define = Profession.Software }; var haneeni = new Person { FirstName = "Haneeni", LastName = "Shibli", Profession = Profession.Health }; moaid.Spouse = haneeni; haneeni.Spouse = moaid; moaid.Dump(typeNames: new TypeNamingConfig
让我们看一看到底是什么是伸缩构造函数 public Hero(Profession profession, String name, HairType hairType, HairColor hairColor ---- 代码实例讲解 此时使用建造者模式就是一个不错的选择 public final class Hero { private final Profession profession; private ; private Armor armor; private Weapon weapon; public Builder(Profession profession, String name) { if (profession == null || name == null) { throw new IllegalArgumentException(" profession and name can not be null"); } this.profession = profession; this.name =
explain select id, profession from tb_user where profession = '软件工程' and age = 31 and status = '0' ; explain select id,profession,age, status from tb_user where profession = '软件工程' and age = 31 and status = '0' ; explain select id,profession,age, status, name from tb_user where profession = '软 件工程' and age = 31 and status = '0' ; explain select * from tb_user where profession = '软件工程' and age = 31 and status 所以当我们查询返回的数据在 id、profession、age、status 之中,则直接走二级索引直接返回数据了。
, concat('插入的数据内容为: id=',new.id,',name=',new.name, ', phone=', NEW.phone, ', email=', NEW.email, ', profession =', NEW.profession)); end; 测试: -- 查看 show triggers ; -- 插入数据到tb_user insert into tb_user(id, name, phone , email, profession, age, gender, status, createtime) VALUES (26,'三皇子','18809091212','erhuangzi@163.com , ', profession=', NEW.profession)); end; 测试: -- 查看 show triggers ; -- 更新 update tb_user set profession =', old.profession)); end; 测试: -- 查看 show triggers ; -- 删除数据 delete from tb_user where id = 26; 测试完毕之后
(id, NAME, phone, email, profession, age, gender, STATUS, createtime) VALUES (26,'三皇子','18809091212' =', old.profession, ' | 更新之后的数据: id=',new.id,',name=',new.name, ', phone=', NEW.phone, ', email=', NEW.email, ', profession=', NEW.profession)); END$$ DELIMITER ; -- 查看触发器 SHOW TRIGGERS ; -- 执行更新操作 UPDATE tb_user SET profession = '会计' WHERE id = 23; UPDATE tb_user SET profession = '会计' WHERE =', old.profession)); END$$ DELIMITER ; -- 查看触发器 SHOW TRIGGERS ;
explain select * from tb_user where profession = '软件工程' and age > 30 and status = '0'; 当范围查询使用> 或 < explain select * from tb_user where profession = '软件工程' and age >= 30 and status = '0'; 当范围查询使用>= 或 < 接下来,我们通过两组示例,来看看对于字符串类型的字段,加单引号与不加单引号的区别: explain select * from tb_user where profession = '软件工程' and age = 31 and status = '0'; explain select * from tb_user where profession = '软件工程' and age = 31 and explain select * from tb_user where profession like '软件%'; explain select * from tb_user where profession
一个主键 id 和一个联合二级索引 profession、age、status。 当执行查询时: SELECT id, profession, age, status FROM tb_user WHERE profession = '软件工程' AND age = 31 AND status 在这个情况下,如果二级索引包含 profession、age、status,并且数据库的存储引擎(如InnoDB)在二级索引的叶子节点中也存储了主键 id,那么这个二级索引就可以作为覆盖索引。 如果发现有索引包含所有需要检索的列(在这个例子中是 id、profession、age、status),那么优化器会选择使用这个索引来执行查询,而不需要进行回表查询。 这个覆盖索引是从哪里来的? **在InnoDB中,二级索引的叶子节点默认包含主键的值,所以如果二级索引的键值包括了 profession、age、status,并且 id 作为主键被存储在二级索引的叶子节点中,那么这个二级索引就可以作为覆盖索引使用
=', NEW.profession)); end; -- 查看 show triggers ; -- 插入数据到tb_user insert into tb_user(id, name, phone , email, profession, age, gender, status,createtime) VALUES (26,'三皇子','18809091212','erhuangzi@163.com =', old.profession, ' | 更新之后的数据: id=',new.id,',name=',new.name, ', phone=',NEW.phone, ', email =', NEW.email, ', profession=', NEW.profession)); end; -- 查看 show triggers ; -- 更新 update tb_user set profession = '会计' where id = 23; update tb_user set profession = '会计' where id <= 5; C.
: //第 2 次请求 IOptions:name:Charles,age:18,profession:.NET Developer IOptionsSnapshot:name:宿春磊,age:19, profession:.NET 开发者 IOptionsMonitor<T>/ Options 最后,除了IOptionsSnapshot,微软还提供了IOptionsMonitor,这位则是提供 :.NET 开发者 IOptionsSnapshot:name:宿春磊,age:19,profession:.NET 开发者 IOptionsMonitor:name:宿春磊Charles,age:19 ,profession:.NET 开发者 第二次请求的时候,IOptionsSnapshot才取到新值。 //第 2 次请求 IOptions:name:宿春磊,age:19,profession:.NET 开发者 IOptionsSnapshot:name:宿春磊Charles,age:19,profession
CustomTag { private String id; private String name; private Integer age; private String profession ; } public void setProfession(String profession) { this.profession = profession; = element.getAttribute("profession"); String address = element.getAttribute("address"); )) { builder.addPropertyValue("profession", profession); } if (StringUtils.hasText [CDATA[ The customTag profession. ]]></xsd:documentation> </xsd:annotation> </xsd
例如: >>> first_name = "Michael" >>> last_name = "Jordan" >>> age = 20 >>> profession = "writer" >>> num age、profession、last_name、first_name、num,然后通过参数的索引来引用参数。 age 的索引为 0,profession 的索引为 1,以此类推。 >>> first_name = "Michael" >>> last_name = "Jordan" >>> age = 20 >>> profession = "writer" >>> num = }, I have writed {num} novels.".format(age=age,profession=profession,last_name=last_name,first_name=first_name
在 tb_user 表中,有一个联合索引,这个联合索引涉及到三个字段,顺序分别为:profession,age,status。 对于最左前缀法则指的是,查询时,最左变的列,也就是profession必须存在,否则索引全部失效。 而且中间不能跳过某一列,否则该列后面的字段索引将失效。 = '0'; explain select * from tb_user where profession = '软件工程' and age = 31; explain select * from tb_user where profession = '软件工程'; 以上的这三组测试中,我们发现只要联合索引最左边的字段 profession存在,索引就会生效,只不过索引的长度不同。 而且由以上三组测试,我们也可以推测出profession字段索引长度为47、age字段索引长度为2、status字段索引长度为5。
',phone=',NEW.phone,",email=' end ; -- 测试:插入数指型tb_user insert into tb_user(id, name, phone, email, profession 更新之前的数据:id=',old.id,',name=',old.name,',phone=', old.phone,',email=', old.email,',sprofession=',old.profession =', NEW.profession)); end; -- 测试:update影响多少行,就会记录几次 update tb_user set profession='会计'where id = 23; update tb_user set profession='会计'where id <= 5; -- 删除数搭触发器 create trigger tb_user_delete_trigger =', NEW.<em>profession</em>)); end:
1.如果咱们想要确保对象被深冻结,就必须创建一个递归函数来冻结对象类型的每个属性: 2.没有深冻结 let person = { name: “Leonardo”, profession: { name : “developer” } }; Object.freeze(person); person.profession.name = “doctor”; console.log(person); //output { name: ‘Leonardo’, profession: { name: ‘doctor’ } } 3.深冻结 function deepFreeze(object) { let propNames deepFreeze(value) : value; } return Object.freeze(object); } let person = { name: “Leonardo”, profession : { name: “developer” } }; deepFreeze(person); person.profession.name = “doctor”; // TypeError: Cannot
pd.DataFrame({ "name": ["John","Jane","Emily","Lisa","Matt"], "note": [92,94,87,82,90], "profession ("engineer"))] output name note profession date_of_birth group 1 Jane 94 Mechanical engineer 2002-08-14 B 多个条件并集筛选数据 当多个条件是以并集的方式来过滤数据的时候,代码如下 df[(df.note > 90) | (df.profession =="Data scientist")] output name note profession date_of_birth group 0 John 92 "] = np.nan df[df.profession.isna()] output name note profession date_of_birth group 0 John
简而美 字符串前加上f或F就ok了 name = 'chafezhou' gender = 'male' age = 18 profession = 'programmer' company = 'iRain I'm {age} years old," \ f" working at {company} as a {profession}" 没对比就没有伤害 看看 I'm %d years old, working at %s as a %s"\ % (name, gender, age, company, profession) 这么冗长的代码行 I'm {age} years old," \ " working at {company} as a {profession}".format( name=name , gender=gender, age=age, profession=profession, company=company) 这么一比较,f_strings显得更简洁,更易阅读,所见即所得,对于多个参数的更是无需关心
C#例子 public class People { private readonly string _profession; private readonly _profession = builder.Profession; this._name = builder.Name; this. hairColor,_hairType); } } public sealed class Builder { public string Profession , string name) { if (profession == null || name == null) { throw new Exception("profession 和 name 不能为空"); } this.Profession = profession