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1、过滤数据 filter()

List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5);List<Integer> numberFilter = numbers.stream().filter(n -> n % 2 == 0) // 过滤偶数.collect(Collectors.toList()); // [2, 4]

2、转换元素 map()

List<String> words = Arrays.asList("apple", "banana");List<Integer> lengths = words.stream().map(String::length) // 转换为单词长度.collect(Collectors.toList()); // [5, 6]

3、排序 sorted()

List<String> list = Arrays.asList("a", "b", "c");List<String> sortedList = list.stream().sorted() // 自然排序（字典序）.collect(Collectors.toList());// [a, b, c]

3.1、自定义排序规则

List<Integer> nums = Arrays.asList(3, 1, 4);List<Integer> customSorted = nums.stream().sorted((a, b) -> b - a) // 降序排序.collect(Collectors.toList()); // [4, 3, 1]

4、去重 distinct()

List<Integer> num2 = Arrays.asList(1, 2, 2, 3);List<Integer> unique = num2.stream().distinct() // 去重.collect(Collectors.toList()); // [1, 2, 3]

5、限制元素数量 limit()

List<Integer> number2 = Arrays.asList(1, 2, 3, 4, 5);List<Integer> firstThree = number2.stream().limit(3) // 取前3个元素.collect(Collectors.toList()); // [1, 2, 3]

6、收集结果 collect()

6.1、收集为List

List<String> list3 = Arrays.asList("a", "b", "ac", "d", "a", "c");List<String> filteredList = list3.stream().filter(s -> s.startsWith("a")).collect(Collectors.toList()); // 收集为List  过滤出以a开头的  [a, ac, a]

6.2、收集为Set

Set<String> set = list3.stream().collect(Collectors.toSet()); // 收集为Set  去重  [a, b, ac, c, d]

6.3、转为Map

Map<String, Integer> map = set.stream().collect(Collectors.toMap(s -> s, String::length)); // 转为Map   每个元素的长度  {a=1, ac=2, b=1, c=1, d=1}

6.4、基本用法（注意键冲突会抛异常）

实体类

@Data
public class Employee {//部门private String dept;//姓名private String name;//薪水private int salary;public Employee(String dept, String name) {this.dept = dept;this.name = name;}public Employee(String dept, String name, int salary) {this.dept = dept;this.name = name;this.salary = salary;}
}

List<Employee> employeeList = new ArrayList<>();
employeeList.add(new Employee("1", "张三", 6000));
employeeList.add(new Employee("2", "李四", 6000));
employeeList.add(new Employee("3", "王五", 6000));Map<String, Integer> map2 = employeeList.stream().collect(Collectors.toMap(Employee::getDept,   // 键的提取函数Employee::getSalary  // 值的提取函数)
);
//{1=6000, 2=6000, 3=6000}

6.5、处理键冲突（例如，取后出现的值）

通过第三个参数（合并函数）解决键冲突：

List<Employee> employeeList = new ArrayList<>();
employeeList.add(new Employee("1", "张三", 6000));
employeeList.add(new Employee("2", "李四", 6000));
employeeList.add(new Employee("3", "王五", 6000));
employeeList.add(new Employee("3", "赵六", 6000));Map<String, String> map3 = employeeList.stream().collect(Collectors.toMap(Employee::getDept,          // 键的提取函数Employee::getName,          // 值的提取函数(oldVal, newVal) -> newVal  // 解决冲突的函数（取新的value）)
);
//{1=张三, 2=李四, 3=赵六}

6.6、指定 Map 实现类

/***  指定 Map 实现类*  通过第四个参数自定义 Map 类型（如 LinkedHashMap 保持插入顺序）：*/
Map<String, String> idToName = employeeList.stream().collect(Collectors.toMap(Employee::getDept,Employee::getName,(oldVal, newVal) -> newVal,LinkedHashMap::new  // 指定Map实现));
//{1=张三, 2=李四, 3=赵六}

6.7、将对象自身作为值

/*** 将对象自身作为值   （注意键冲突会抛异常）* 使用  Function.identity()  直接引用元素作为值*/
List<Employee> employeeList222 = new ArrayList<>();
employeeList222.add(new Employee("1", "张三", 6000));
employeeList222.add(new Employee("2", "李四", 6000));
employeeList222.add(new Employee("3", "王五", 6000));
Map<String, Employee> idToPerson = employeeList222.stream().collect(Collectors.toMap(Employee::getDept,Function.identity()  // 值=对象本身));
// {
// 1=Employee(dept=1, name=张三, salary=6000),
// 2=Employee(dept=2, name=李四, salary=6000),
// 3=Employee(dept=3, name=王五, salary=6000)
// }

6.8、统计总和

例如部门工资总和

List<Employee> employeeList333 = new ArrayList<>();
employeeList333.add(new Employee("1", "张三", 6000));
employeeList333.add(new Employee("2", "李四", 6000));
employeeList333.add(new Employee("3", "王五", 6000));
employeeList333.add(new Employee("1", "张三1", 7000));
employeeList333.add(new Employee("2", "李四1", 8000));
employeeList333.add(new Employee("3", "王五1", 9000));
Map<String, Integer> deptSalarySum = employeeList333.stream().collect(Collectors.toMap(Employee::getDept,Employee::getSalary,Integer::sum  // 合并时累加工资));
//{1=13000, 2=14000, 3=15000}

6.9、字符串拼接

/**  字符串拼接  */
List<String> str = Arrays.asList("a", "b", "c");
String join1 = str.stream().collect(Collectors.joining());  //abc
String join2 = str.stream().collect(Collectors.joining(", "));  //a, b, c
String join3 = str.stream().collect(Collectors.joining(", ", "[", "]"));    //[a, b, c]

7、遍历元素

List<String> list3 = Arrays.asList("a", "b", "ac", "d", "a", "c");
list3.stream().forEach(System.out::println); // 打印每个元素

8、匹配检查

/***  匹配检查： anyMatch()   allMatch()   noneMatch()*/List<String> list4 = Arrays.asList("a", "b", "c");
boolean hasA = list4.stream().anyMatch(s -> s.contains("a")); // 是否存在包含"a"的元素  trueList<Integer> numbers4 = Arrays.asList(1, 2, 3, 4, 5);
boolean allPositive = numbers4.stream().allMatch(n -> n > 0); // 是否所有元素都大于0     true

9、分组 Grouping By

List<Employee> employees = new ArrayList<>();
employees.add(new Employee("1","张三", 5000));
employees.add(new Employee("1","李四", 5500));
employees.add(new Employee("2","王五", 6000));
employees.add(new Employee("2","赵六", 7000));
employees.add(new Employee("3","韩七", 5800));
employees.add(new Employee("4","魏八", 9000));// 按属性分组（按照部门分组）
Map<String, List<Employee>> groupByDept = employees.stream().collect(Collectors.groupingBy(Employee::getDept));
// {
// 1=[Employee(dept=1, name=张三, salary=5000), Employee(dept=1, name=李四, salary=5500)],
// 2=[Employee(dept=2, name=王五, salary=6000), Employee(dept=2, name=赵六, salary=4000)],
// 3=[Employee(dept=3, name=韩七, salary=5800)],
// 4=[Employee(dept=4, name=魏八, salary=9000)]
// }// 分组后对值进一步处理（如求数量：统计每个部门的人数）
Map<String, Long> countByDept = employees.stream().collect(Collectors.groupingBy(Employee::getDept, Collectors.counting()));
//{1=2, 2=2, 3=1, 4=1}// 多级分组（嵌套分组） 先按照每个部门分组，然后按照薪水是否超过5000分组
Map<String, Map<Boolean, List<Employee>>> multiGroup = employees.stream().collect(Collectors.groupingBy(Employee::getDept,Collectors.groupingBy(e -> e.getSalary() > 5000)));
// {
// 1={false=[Employee(dept=1, name=张三, salary=5000)], true=[Employee(dept=1, name=李四, salary=5500)]},
// 2={true=[Employee(dept=2, name=王五, salary=6000), Employee(dept=2, name=赵六, salary=7000)]},
// 3={true=[Employee(dept=3, name=韩七, salary=5800)]},
// 4={true=[Employee(dept=4, name=魏八, salary=9000)]}
// }

10、聚合操作 reduce()

List<Integer> numbers5 = Arrays.asList(1, 2, 3, 4, 5);
Optional<Integer> sum = numbers5.stream().reduce((a, b) -> a + b); // 求和（返回Optional）   Optional[15]int total = numbers5.stream().reduce(0, Integer::sum); // 初始值为0的求和   15

11、统计

/*** 统计： count()   min()   max()*/List<String> list5 = Arrays.asList("a", "b", "c");
long count = list5.stream().count(); // 元素总数  3List<Integer> numbers6 = Arrays.asList(1, 2, 3, 4, 5);
Optional<Integer> max = numbers6.stream().max(Integer::compare); // 最大值  Optional[5]
Optional<Integer> min = numbers6.stream().min(Integer::compare); // 最小值  Optional[1]

12、跳过元素 skip()

List<Integer> numbers7 = Arrays.asList(1, 2, 3, 4, 5);
List<Integer> skipped = numbers7.stream().skip(2) // 跳过前2个元素.collect(Collectors.toList()); //[3, 4, 5]