下面是视频（优酷的清晰度有限）：还是建议大家去B站观看：[B站观看地址](https://www.bilibili.com/video/BV1sE411P7C1/)。如果您觉得我做的工作对您有帮助，请去B站点赞、关注、转发、收藏，您的支持是我不竭的创作动力！ ```[youku] XNDU5Nzk4MTgwMA ``` ## 一、基础代码准备 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<Employee> employees = Arrays.asList(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10); List<Employee> 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)] ~~~ ## 二、什么是谓词逻辑？ 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。  ### **什么是谓词？（百度百科）**  ### 什么是谓词逻辑？ 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' ~~~ ## 三、谓词逻辑的复用 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<Employee> ageGreaterThan70 = x -> x.getAge() >70; public static Predicate<Employee> genderM = x -> x.getGender().equals("M"); ~~~ ### 3.1.and语法（交集） ~~~ List<Employee> 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)] ~~~ ### 3.2.or语法（并集） ~~~ List<Employee> 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)] ~~~ ### 3.3.negate语法（取反） ~~~ List<Employee> 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)] ~~~