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java stream

stream 是 java 8 的一大亮点，专注于了集合的处理，包括抽取，过滤，转化，聚合，化简等，借助新的 lambda 表达式，极大地简化了流式处理的流程，提高了代码的可读性，同时支持并发和串行两种模式，可以很容易地写出高性能的代码

基本概念

stream 相当于一个高级版本的 iterator，iterator 只有简单的遍历功能，而 stream 在这个基础上提供了抽取，过滤，转化，聚合，化简等丰富的流处理功能

常见的操作主要中间操作和终止操作，一个流处理可以会经过若干个中间操作和一个终止操作，中间操作返回的依然是一个流，终止操作获得一个特定的结果，中间操作是惰性的，在遇到终止操作之前，中间操作只是记录一个操作步骤，不会从流中读取任何数据

创建流

// 从容器中创建
Stream<Integer> stream1 = List.of(1, 2, 3, 4, 5).stream();
Stream<Integer> stream2 = Stream.of(1, 2, 3, 4, 5);

// 创建随机数
Stream<Integer> stream3 = new Random().ints().limit(10).boxed();
Stream<Integer> stream4 = ThreadLocalRandom.current().ints().limit(10).boxed();

// 文件流
Stream<String> stream5 = new BufferedReader(new FileReader("/tmp/test.txt")).lines();

// IntStream
Stream<Integer> stream6 = IntStream.range(1, 10).boxed();

// generate
Stream<Integer> stream7 = Stream.generate(() -> (int) System.currentTimeMillis()).limit(10);

// iterate
Stream<Integer> stream8 = Stream.iterate(1, x -> x + 1).limit(10);
Stream<Integer> stream9 = Stream.iterate(1, x -> x < 10, x -> x + 1);

终止操作

终止操作代表一个计算的结果，常见的终止操作如下:

forEach : 遍历元素
forEachOrdered : 按顺序遍历元素，主要在并发场景下使用
toArray : 将流输出到数组中
collect : 收集流，可以输出到指定的 Collector 中， Collectors 中自带了 toList ， toSet 等 Collector
anyMatch : 任意元素满足谓词，返回 true
allMatch : 所有元素满足谓词，返回 true
noneMatch : 所有元素都不满足谓词，返回 true
count : 元素数量
findFirst : 返回第一个元素
findAny : 返回任意一个元素
min : 返回最小元素
max : 返回最大元素
reduce : 聚合，将流中所有的数据聚合成一个值

Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).forEach(System.out::print);
Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).forEachOrdered(System.out::print);
assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).toArray(), equalTo(new Integer[]{1, 2, 3, 4, 5, 6, 7, 8, 9}));
assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).collect(Collectors.toList()), equalTo(List.of(1, 2, 3, 4, 5, 6, 7, 8, 9)));
assertFalse(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).anyMatch(x -> x > 10));
assertTrue(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).allMatch(x -> x < 10));
assertTrue(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).noneMatch(x -> x > 10));
assertEquals(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).count(), 9);
assertEquals(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).findFirst().orElse(0), Integer.valueOf(1));
assertEquals(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).findAny().orElse(0), Integer.valueOf(1));
assertEquals(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).iterator().next(), Integer.valueOf(1));
assertEquals(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).max(Comparator.comparingInt(x -> x)).orElse(0), Integer.valueOf(9));
assertEquals(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).min(Comparator.comparingInt(x -> x)).orElse(0), Integer.valueOf(1));
assertEquals(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).reduce((x, y) -> x + y).orElse(0), Integer.valueOf(45));

中间操作

map
filter
distinct
sorted
limit
skip
peek
takeWhile
dropWhile

assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).map(x -> x * x).collect(Collectors.toList()), equalTo(List.of(
        1, 4, 9, 16, 25, 36, 49, 64, 81
)));
assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).filter(x -> x % 2 == 0).collect(Collectors.toList()), equalTo(List.of(
        2, 4, 6, 8
)));
assertThat(Stream.of(4, 1, 2, 1, 2, 4, 3, 3).distinct().collect(Collectors.toList()), equalTo(List.of(
        4, 1, 2, 3
)));
assertThat(Stream.of(6, 4, 7, 3, 2, 9, 1, 5, 8).sorted().collect(Collectors.toList()), equalTo(List.of(
        1, 2, 3, 4, 5, 6, 7, 8, 9
)));
assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).limit(3).collect(Collectors.toList()), equalTo(List.of(
        1, 2, 3
)));
assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).skip(6).collect(Collectors.toList()), equalTo(List.of(
        7, 8, 9
)));
assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).peek(System.out::println).collect(Collectors.toList()), equalTo(List.of(
        1, 2, 3, 4, 5, 6, 7, 8, 9
)));
assertEquals(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).mapToInt(x -> x * x).sum(), 285);
assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).takeWhile(x -> x < 5).collect(Collectors.toList()), equalTo(List.of(
        1, 2, 3, 4
)));
assertThat(Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9).dropWhile(x -> x < 5).collect(Collectors.toList()), equalTo(List.of(
        5, 6, 7, 8, 9
)));

group by

类似于数据库中 group by 的操作，流处理也支持 group by ，能根据对象中的某个字段聚合

class Student {
    private final String grade;
    private final String name;
    private final int chinese;
    private final int english;

    private Student(String grade, String name, int chinese, int english) {
        this.grade = grade;
        this.name = name;
        this.chinese = chinese;
        this.english = english;
    }

    @Override
    public String toString() {
        return "[" + grade + " " + name + " " + chinese + " " + english + "]";
    }
}

Random random = new Random();
Stream<Student> stream = Stream.generate(() ->
        new Student(
                "grade" + Math.abs(random.nextInt() % 4 + 1),
                "student" + Math.abs(random.nextInt() % 1000),
                Math.abs(random.nextInt() % 30 + 70),
                Math.abs(random.nextInt() % 30 + 70)
        )
);

Map<String, List<Student>> map = stream.limit(10).collect(Collectors.groupingBy(
        x -> x.grade, Collectors.toList()
));

map.forEach((k, v) -> {
    System.out.println(k + " => " + v);
});