Java8 Stream教程之collect()的技巧
作者:小目标青年
Java8引入了全新的Stream API,这里的Stream和I/O流不同,它更像具有Iterable的集合类,但行为和集合类又有所不同,下面这篇文章主要给大家介绍了关于Java8 Stream教程之collect()的技巧,需要的朋友可以参考下
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。
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:
总结
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