Java 8 Stream 之 collect()

xlc520
  • Java
  • Java
大约 3 分钟约 780 字

Java 8 Stream 之 collect()

前言

本身我是一个比较偏向少使用Streamopen in new window的人,因为调试比较不方便。

但是, 不得不说,stream确实会给我们编码带来便捷。

所以还是忍不住想分享一些奇技淫巧。

正文

Stream流 其实操作分三大块 :

创建 处理 收集

我今天想分享的是 收集 这part的玩法。

img

OK,开始结合代码示例一起玩下:

lombok依赖引入,代码简洁一点:

        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.20</version>
            <scope>compile</scope>
        </dependency>	

准备一个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;
 
}

准备一个模拟获取List的函数:

    private static List<UserDTO> getUserList() {
        UserDTO userDTO = new UserDTO();
        userDTO.setName("小冬");
        userDTO.setAge(18);
        userDTO.setSex("男");
        userDTO.setHasOrientation(false);
        UserDTO userDTO2 = new UserDTO();
        userDTO2.setName("小秋");
        userDTO2.setAge(30);
        userDTO2.setSex("男");
        userDTO2.setHasOrientation(true);
        UserDTO userDTO3 = new UserDTO();
        userDTO3.setName("春");
        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;
    }

第一个小玩法

将集合通过Stream.collect() 转换成其他集合/数组:

现在拿List<UserDTO> 做例子

转成 HashSet<UserDTO>

        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());
        }

第二个小玩法

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 = sersStream.max(Comparator.comparingInt(UserDTO::getAge));
if (maxUserOptionalNew.isPresent()) {
    UserDTO masUser = maxUserOptionalNew.get();
    System.out.println(masUser.toString());
}

效果:

img

输出:

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

找出年龄最小:

stream.min()

写法 1:

Optional<UserDTO> minUserOptional = sersStream.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() - 2.getAge()));

求平均值:

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

效果:

img

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)));

效果:

img

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;
    }
}));

效果:

img

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;
                   }
                }))));

效果:

img