Java8中Stream之collect()方法大全

Stream流常见的操作种类

 创建、处理、收集

UserDTO.java

@Data
public class UserDTO {
 
    /**
     * 姓名
     */
    private  String name;
    /**
     * 年龄
     */
    private  Integer age;
    /**
     * 性别
     */
    private  String sex;
    /**
     * 是否有方向
     */
    private  Boolean hasOrientation;
 
}

生成一个list数据

    private static List<UserDTO> getUserList() {
        UserDTO userDTO = new UserDTO();
        userDTO.setName("java265");
        userDTO.setAge(21);
        userDTO.setSex("男");
        userDTO.setHasOrientation(false);
        UserDTO userDTO2 = new UserDTO();
        userDTO2.setName("猫猫");
        userDTO2.setAge(30);
        userDTO2.setSex("男");
        userDTO2.setHasOrientation(true);
        UserDTO userDTO3 = new UserDTO();
        userDTO3.setName("奥迪满");
        userDTO3.setAge(18);
        userDTO3.setSex("女");
        userDTO3.setHasOrientation(true);
        List<UserDTO> userList = new ArrayList<>();
        userList.add(userDTO);
        userList.add(userDTO2);
        userList.add(userDTO3);
        return userList;
    }

Stream.collect() 转换成其他集合/数组

        List<UserDTO> userList = getUserList();
 
        Stream<UserDTO> usersStream = userList.stream();
 
        HashSet<UserDTO> usersHashSet = usersStream.collect(Collectors.toCollection(HashSet::new));
       转成  Set<UserDTO> usersSet ：

        List<UserDTO> userList = getUserList();
 
        Stream<UserDTO> usersStream = userList.stream();
 
        Set<UserDTO> usersSet = usersStream.collect(Collectors.toSet());

转成ArrayList<UserDTO>

        List<UserDTO> userList = getUserList();
 
        Stream<UserDTO> usersStream = userList.stream();
        
        ArrayList<UserDTO> usersArrayList = usersStream.collect(Collectors.toCollection(ArrayList::new));

转成Object[] objects

        List<UserDTO> userList = getUserList();
 
        Stream<UserDTO> usersStream = userList.stream();
 
        Object[] objects = usersStream.toArray();

转成 UserDTO[] users

        List<UserDTO> userList = getUserList();
 
        Stream<UserDTO> usersStream = userList.stream();
 
        UserDTO[] users = usersStream.toArray(UserDTO[]::new);
        for (UserDTO user : users) {
            System.out.println(user.toString());
        }

聚合（求和、最小、最大、平均值、分组）
找出年龄最大
stream.max()

写法1:

List<UserDTO> userList = getUserList();
Stream<UserDTO> usersStream = userList.stream();
Optional<UserDTO> maxUserOptional = 
        usersStream.max((s1, s2) -> s1.getAge() - s2.getAge());
if (maxUserOptional.isPresent()) {
    UserDTO masUser = maxUserOptional.get();
    System.out.println(masUser.toString());
}

写法2:

List<UserDTO> userList = getUserList(); Stream<UserDTO> usersStream = userList.stream();
Optional<UserDTO> maxUserOptionalNew = usersStream.max(Comparator.comparingInt(UserDTO::getAge));
if (maxUserOptionalNew.isPresent()) {
    UserDTO masUser = maxUserOptionalNew.get();
    System.out.println(masUser.toString());
}
 
------运行以上代码，将输出以下信息------

UserDTO(name=猫猫, age=30, sex=男, hasOrientation=true)


找出年龄最小
stream.min()
 
Optional<UserDTO> minUserOptional = usersStream.min(Comparator.comparingInt(UserDTO::getAge));
if (minUserOptional.isPresent()) {
    UserDTO minUser = minUserOptional.get();
    System.out.println(minUser.toString());
}

写法2： 
 Optional<UserDTO> min = usersStream.collect(Collectors.minBy((s1, s2) -> s1.getAge() - s2.getAge()));

求平均值
List<UserDTO> userList = getUserList();
Stream<UserDTO> usersStream = userList.stream();
Double avgScore = usersStream.collect(Collectors.averagingInt(UserDTO::getAge));

求和
写法1

   Integer reduceAgeSum = usersStream.map(UserDTO::getAge).reduce(0, Integer::sum);

写法2：

   int ageSumNew = usersStream.mapToInt(UserDTO::getAge).sum();


统计数量
 
long countNew = usersStream.count();


简单分组
按照具体年龄分组
//按照具体年龄分组
Map<Integer, List<UserDTO>> ageGroupMap = usersStream.collect(Collectors.groupingBy((UserDTO::getAge)));
 
 

分组过程加写判断逻辑
//按照性别 分为"男"一组  "女"一组
Map<Integer, List<UserDTO>> groupMap = usersStream.collect(Collectors.groupingBy(s -> {
    if (s.getSex().equals("男")) {
        return 1;
    } else {
        return 0;
    }
}));
 
多级复杂分组
//多级分组
// 1.先根据年龄分组
// 2.然后再根据性别分组
Map<Integer, Map<String, Map<Integer, List<UserDTO>>>> moreGroupMap = usersStream.collect(Collectors.groupingBy(

        //1.KEY(Integer)             VALUE (Map<String, Map<Integer, List<UserDTO>>)
        UserDTO::getAge, Collectors.groupingBy(
                //2.KEY(String)             VALUE (Map<Integer, List<UserDTO>>)
                UserDTO::getSex, Collectors.groupingBy((userDTO) -> {
                    if (userDTO.getSex().equals("男")) {
                        return 1;
                    } else {
                        return 0;
                    }
                }))));