Java Stream（java.util.stream.*）从 0 到“进阶实战”一次性梳理出来-洪萨配资

创建流（7 种常用入口）

Stream<String> s1 = Stream.of("a", "b", "c"); Stream<String> s2 = Arrays.stream(arr); Stream<String> s3 = list.stream(); // 集合 Stream<String> s4 = Files.lines(Paths.get("a.txt")); // IO Stream<Integer> s5 = Stream.iterate(0, n -> n + 2); // 无限流 Stream<Integer> s6 = Stream.generate(() -> 1); // 无限流 IntStream s7 = IntStream.range(1, 100); // 基本类型

中间操作（lazy，链式）

stream .filter(x -> x % 2 == 0) // 过滤 .map(String::valueOf) // 1:1 转换 .flatMap(x -> Arrays.stream(x.split(","))) // 1:N 打平 .distinct() // 去重 .sorted(Comparator.comparing(User::getAge).reversed()) .peek(System.out::println) // 调试打印，不会触发消费 .limit(5) // 截断 .skip(3) // 跳过 .takeWhile(x -> x < 50) // Java9+ 遇到第一个 false 停止 .dropWhile(x -> x < 50); // Java9+ 丢掉前缀

终端操作（触发执行）

// 3.1 遍历/消费 forEach(System.out::println); // 3.2 聚合 long cnt = stream.count(); Optional<Integer> max = stream.max(Integer::compare); Integer sum = stream.mapToInt(Integer::intValue).sum(); OptionalDouble avg = stream.mapToInt(Integer::intValue).average(); // 3.3 规约 Integer total = stream.reduce(0, Integer::sum); String joined = stream.reduce("", (a, b) -> a + "," + b); // 3.4 收集（最灵活） List<String> list = stream.collect(Collectors.toList()); Set<String> set = stream.collect(Collectors.toSet()); Map<String, User> map = stream.collect(Collectors.toMap(User::getId, u -> u)); String csv = stream.collect(Collectors.joining("；")); Map<Integer, List<User>> group = stream.collect(Collectors.groupingBy(User::getAge)); Map<Boolean, List<User>> part = stream.collect(Collectors.partitioningBy(u -> u.getAge() >= 18)); // 3.5 匹配/查找 boolean ok1 = stream.allMatch(x -> x > 0); boolean ok2 = stream.anyMatch(x -> x > 0); boolean ok3 = stream.noneMatch(x -> x > 0); Optional<Integer> first = stream.findFirst(); Optional<Integer> any = stream.findAny(); // 3.6 转数组/基本类型流 int[] arr = stream.mapToInt(Integer::intValue).toArray();

基本类型特化流（IntStream / LongStream / DoubleStream）

IntStream.rangeClosed(1, 100) .filter(n -> n % 7 == 0) .average() .ifPresent(System.out::println);

并行流（parallel）与并发注意

long c = LongStream.rangeClosed(1, 1_0000_0000L) .parallel() // 拆分成多段 ForkJoin .filter(n -> n % 103 == 0) .count();

注意：

数据量小别用，线程切换反而慢
避免有状态 lambda、外部共享变量
用Collectors.toConcurrentMap可进一步减少锁

6.自定义收集器（Collector 接口）

Collector<Integer, StringJoiner, String> myJoin = Collector.of( () -> new StringJoiner("、"), (j, s) -> j.add(s.toString()), (j1, j2) -> j1.merge(j2), StringJoiner::toString); String r = Stream.of(1, 2, 3).collect(myJoin); // 1、2、3

7.与 Optional 联动

stream.map(User::getAddress) .filter(Objects::nonNull) .findFirst() .ifPresent(addr -> System.out.println("找到地址：" + addr));

8.实战 10 例（一行流）

// 1. 去重并按字典序取前 3 List<String> top3 = list.stream().distinct().sorted().limit(3).toList(); // 2. 把两个 List 合并后去重 List<String> union = Stream.concat(list1.stream(), list2.stream()) .distinct() .toList(); // 3. 统计每个单词出现次数 Map<String, Long> freq = words.stream() .collect(Collectors.groupingBy(Function.identity(), Collectors.counting())); // 4. 找到工资最高的员工 Optional<Employee> richest = emps.stream() .max(Comparator.comparingDouble(Employee::getSalary)); // 5. 按部门分组后，取每组工资最高的 Map<Department, Optional<Employee>> topInDept = emps.stream() .collect(Collectors.groupingBy(Employee::getDept, Collectors.maxBy(Comparator.comparingDouble(Employee::getSalary)))); // 6. 把 List<User> 转 Map<id, name> Map<Long, String> id2Name = users.stream() .collect(Collectors.toMap(User::getId, User::getName)); // 7. 将字符串列表转整型，跳过非数字 List<Integer> nums = strList.stream() .filter(s -> s.matches("\\d+")) .map(Integer::valueOf) .toList(); // 8. 把多行配置按“键=值”解析成 Map Map<String, String> cfg = lines.stream() .filter(l -> l.contains("=")) .map(l -> l.split("=", 2)) .collect(Collectors.toMap(a -> a[0], a -> a[1])); // 9. 分页：跳过前 (page-1)*size 条，取 size 条 List<Row> page = rows.stream() .skip((pageNo - 1) * pageSize) .limit(pageSize) .toList(); // 10. 递归列出目录下所有 .java 文件 try (Stream<Path> walk = Files.walk(Paths.get("src"))) { List<File> javaFiles = walk.filter(p -> p.toString().endsWith(".java")) .map(Path::toFile) .toList(); }

9.常见坑