前言

本身我是一个比较偏向少使用Stream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。

 

 

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 

/** * @Author: JCccc * @Date: 2022-9-20 01:25 * @Description: */ @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 =
usersStream.max(Comparator.comparingInt(UserDTO::getAge)); if
(maxUserOptionalNew.isPresent()) { UserDTO masUser = maxUserOptionalNew.get();
System.out.println(masUser.toString()); }
效果:

 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 =
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));
效果:

 

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: 

 

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:

 

 

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:

 

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