Java Stream 流式操作举例

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🚀 Java Stream 流式操作实战示例

1. 基础示例：过滤、映射、收集

List<String> fruits = List.of("apple", "banana", "cherry", "avocado");List<String> result = fruits.stream().filter(f -> f.startsWith("a"))   // 过滤出 a 开头.map(String::toUpperCase)         // 转大写.toList();                        // 收集到 ListSystem.out.println(result); // [APPLE, AVOCADO]

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2. Map 流式操作：找出成绩优秀的同学

Map<String, Integer> scores = Map.of("Tom", 90, "Jerry", 80, "Anna", 95);List<String> topStudents = scores.entrySet().stream().filter(e -> e.getValue() > 85)      // 过滤成绩 > 85.map(Map.Entry::getKey)              // 提取姓名.toList();System.out.println(topStudents); // [Tom, Anna]

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3. 排序与去重

List<Integer> numbers = List.of(5, 3, 9, 1, 3, 5);List<Integer> result = numbers.stream().distinct()                          // 去重.sorted()                            // 排序.toList();System.out.println(result); // [1, 3, 5, 9]

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4. 聚合计算：reduce

List<Integer> numbers = List.of(1, 2, 3, 4, 5);// 求和
int sum = numbers.stream().reduce(0, Integer::sum);System.out.println(sum); // 15

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5. 分组统计：groupingBy

List<String> words = List.of("apple", "ant", "banana", "bat", "cherry");Map<Character, List<String>> grouped = words.stream().collect(Collectors.groupingBy(w -> w.charAt(0))); // 按首字母分组System.out.println(grouped);
// {a=[apple, ant], b=[banana, bat], c=[cherry]}

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6. 统计操作

List<Integer> numbers = List.of(10, 20, 30, 40);IntSummaryStatistics stats = numbers.stream().mapToInt(Integer::intValue).summaryStatistics();System.out.println(stats.getCount()); // 4
System.out.println(stats.getSum());   // 100
System.out.println(stats.getAverage());// 25.0

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7. 扁平化 flatMap

List<List<String>> nested = List.of(List.of("a", "b"),List.of("c", "d", "e")
);List<String> flat = nested.stream().flatMap(List::stream)   // 展平成单一流.toList();System.out.println(flat); // [a, b, c, d, e]

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8. 文件流式处理（行处理）

try (Stream<String> lines = Files.lines(Path.of("data.txt"))) {long count = lines.filter(line -> line.contains("error")).count();System.out.println("包含 error 的行数: " + count);
} catch (IOException e) {e.printStackTrace();
}

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9. 无限流：生成数据流

List<Integer> first10Even = Stream.iterate(0, n -> n + 2) // 从0开始，每次+2.limit(10)                                        // 取前10个.toList();System.out.println(first10Even); // [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

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10. 并行流：加速处理大集合

List<Integer> numbers = IntStream.range(1, 1_000_000).boxed().toList();long count = numbers.parallelStream()  // 并行流.filter(n -> n % 2 == 0).count();System.out.println("偶数个数: " + count); // 500000

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✨ 总结

• 集合流：stream()，常用 filter/map/sorted/distinct
• Map流：从 entrySet()/keySet()/values() 开始
• 聚合统计：reduce、Collectors.groupingBy、summaryStatistics
• 文件流：Files.lines()，边读边处理，避免内存爆炸
• 无限流：Stream.iterate/generate，适合模拟数据流
• 并行流：parallelStream()，多核加速大数据处理

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