恕我直言你可能真的不会java第3篇：Stream的Filter与谓词逻辑

建立一个实体类，该实体类有五个属性。下面的代码使用了lombok的注解Data、AllArgsConstructor，这样我们就不用写get、set方法和全参构造函数了。lombok会帮助我们在编译期生成这些模式化的代码。

@Data
@AllArgsConstructor
public class Employee {

   private Integer id;
   private Integer age;   //年龄
   private String gender;  //性别
   private String firstName;  
   private String lastName;
}

写一个测试类，这个测试类的内容也很简单，新建十个Employee 对象

public class StreamFilterPredicate {
    
    public static void main(String[] args){
 Employee e1 = new Employee(1,23,"M","Rick","Beethovan");
 Employee e2 = new Employee(2,13,"F","Martina","Hengis");
 Employee e3 = new Employee(3,43,"M","Ricky","Martin");
 Employee e4 = new Employee(4,26,"M","Jon","Lowman");
 Employee e5 = new Employee(5,19,"F","Cristine","Maria");
 Employee e6 = new Employee(6,15,"M","David","Feezor");
 Employee e7 = new Employee(7,68,"F","Melissa","Roy");
 Employee e8 = new Employee(8,79,"M","Alex","Gussin");
 Employee e9 = new Employee(9,15,"F","Neetu","Singh");
 Employee e10 = new Employee(10,45,"M","Naveen","Jain");


 List employees = Arrays.asList(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10);

 List filtered = employees.stream()
  .filter(e -> e.getAge() > 70 && e.getGender().equals("M"))
  .collect(Collectors.toList());

 System.out.println(filtered);

    }

}

需要注意的是上面的filter传入了lambda表达式(之前的章节我们已经讲过了)，表达过滤年龄大于70并且男性的Employee员工。输出如下：

[Employee(id=8, age=79, gender=M, firstName=Alex, lastName=Gussin)]

下面要说我们的重点了，通过之前的章节的讲解，我们已经知道lambda表达式表达的是一个匿名接口函数的实现。那具体到Stream.filter()中，它表达的是什么呢？看下图：可以看出它表达的是一个Predicate接口，在英语中这个单词的意思是：谓词。

WHERe 和 AND 限定了主语employee是什么，那么WHERe和AND语句所代表的逻辑就是谓词逻辑

SELECt *
FROM employee
WHERe age > 70
AND gender = 'M'

通常情况下，filter函数中lambda表达式为一次性使用的谓词逻辑。如果我们的谓词逻辑需要被多处、多场景、多代码中使用，通常将它抽取出来单独定义到它所限定的主语实体中。
比如：将下面的谓词逻辑定义在Employee实体class中。

   public static Predicate ageGreaterThan70 = x -> x.getAge() >70;
   public static Predicate genderM = x -> x.getGender().equals("M");

List filtered = employees.stream()
 .filter(Employee.ageGreaterThan70.and(Employee.genderM))
 .collect(Collectors.toList());

输出如下：

[Employee(id=8, age=79, gender=M, firstName=Alex, lastName=Gussin)]

List filtered = employees.stream()
 .filter(Employee.ageGreaterThan70.or(Employee.genderM))
 .collect(Collectors.toList());

输出如下：实际上就是年龄大于70的和所有的男性（由于79的那位也是男性，所以就是所有的男性）

[Employee(id=1, age=23, gender=M, firstName=Rick, lastName=Beethovan), Employee(id=3, age=43, gender=M, firstName=Ricky, lastName=Martin), Employee(id=4, age=26, gender=M, firstName=Jon, lastName=Lowman), Employee(id=6, age=15, gender=M, firstName=David, lastName=Feezor), Employee(id=8, age=79, gender=M, firstName=Alex, lastName=Gussin), Employee(id=10, age=45, gender=M, firstName=Naveen, lastName=Jain)]

List filtered = employees.stream()
 .filter(Employee.ageGreaterThan70.or(Employee.genderM).negate())
 .collect(Collectors.toList());

输出如下：把上一小节代码的结果取反，实际上就是所有的女性

[Employee(id=2, age=13, gender=F, firstName=Martina, lastName=Hengis), Employee(id=5, age=19, gender=F, firstName=Cristine, lastName=Maria), Employee(id=7, age=68, gender=F, firstName=Melissa, lastName=Roy), Employee(id=9, age=15, gender=F, firstName=Neetu, lastName=Singh)]

• 本文转载注明出处（必须带连接，不能只转文字）：字母哥博客。