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基础篇:JAVA.Stream函数,优雅的数据流操作

[db:作者]

1月 6, 2022

前言

平时操作集合数据,我们一般都是for或者iterator去遍历,不是很好看。java提供了Stream的概念,它可以让我们把集合数据当做一个个元素在处理,并且提供多线程模式

  • 流的创建
  • 流的各种数据操作
  • 流的终止操作
  • 流的聚合处理
  • 并发流和CompletableFuture的配合使用

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github地址,感谢star

1 stream的构造方式

stream内置的构造方法

public static<T> Stream<T> iterate(final T seed, final UnaryOperator<T> f)
public static <T> Stream<T> concat(Stream<? extends T> a, Stream<? extends T> b)
public static<T> Builder<T> builder()
public static<T> Stream<T> of(T t)
public static<T> Stream<T> empty()
public static<T> Stream<T> generate(Supplier<T> s)

Collection声明的stream函数

default Stream<E> stream()
  • Collection声明了stream转化函数,也就是说,任意Collection子类都存在官方替我们实现的由Collection转为Stream的方法
  • 示例,List转Stream

    public static void main(String[] args){
    List<String> demo = Arrays.asList("a","b","c");
    long count = demo.stream().peek(System.out::println).count();
    System.out.println(count);
    }
    -------result--------
    a
    b
    c
    3

2 接口stream对元素的操作方法定义

过滤 filter

Stream<T> filter(Predicate<? super T> predicate)
  • Predicate是函数式接口,可以直接用lambda代替;如果有复杂的过滤逻辑,则用or、and、negate方法组合
  • 示例

    List<String> demo = Arrays.asList("a", "b", "c");
    Predicate<String> f1 = item -> item.equals("a");
    Predicate<String> f2 = item -> item.equals("b");
    demo.stream().filter(f1.or(f2)).forEach(System.out::println);
    -------result--------
    a
    b

映射转化 map

<R> Stream<R> map(Function<? super T, ? extends R> mapper)
IntStream mapToInt(ToIntFunction<? super T> mapper);
LongStream mapToLong(ToLongFunction<? super T> mapper);
DoubleStream mapToDouble(ToDoubleFunction<? super T> mapper);
  • 示例

    static class User{
    public User(Integer id){this.id = id; }
    Integer id; public Integer getId() { return id; }
    }
    public static void main(String[] args) {
    List<User> demo = Arrays.asList(new User(1), new User(2), new User(3));
    // User 转为 Integer(id)
    demo.stream().map(User::getId).forEach(System.out::println);
    }
    -------result--------
    1
    2
    3

    数据处理 peek

    Stream<T> peek(Consumer<? super T> action);
  • 与map的区别是其无返回值
  • 示例

    static class User{
    public User(Integer id){this.id = id; }
    Integer id;
    public Integer getId() { return id; }
    public void setId(Integer id) { this.id = id; }
    }
    public static void main(String[] args) {
    List<User> demo = Arrays.asList(new User(1), new User(2), new User(3));
    // id平方,User 转为 Integer(id)
    demo.stream().peek(user -> user.setId(user.id * user.id)).map(User::getId).forEach(System.out::println);
    }
    -------result--------
    1
    4
    9

映射撵平 flatMap

<R> Stream<R> flatMap(Function<? super T, ? extends Stream<? extends R>> mapper);
IntStream flatMapToInt(Function<? super T, ? extends IntStream> mapper);
LongStream flatMapToLong(Function<? super T, ? extends LongStream> mapper);
DoubleStream flatMapToDouble(Function<? super T, ? extends DoubleStream> mapper);
  • flatMap:将元素为Stream\<T>类型的流撵平成一个元素类型为T的Stream流
  • 示例

    public static void main(String[] args) {
    List<Stream<Integer>> demo = Arrays.asList(Stream.of(5), Stream.of(2), Stream.of(1));
    demo.stream().flatMap(Function.identity()).forEach(System.out::println);
    }
    -------result--------
    5
    2
    1

去重 distinct

Stream<T> distinct();
  • 示例

    List<Integer> demo = Arrays.asList(1, 1, 2);
    demo.stream().distinct().forEach(System.out::println);
    -------result--------
    1
    2

排序 sorted

Stream<T> sorted();
Stream<T> sorted(Comparator<? super T> comparator);
  • 示例

    List<Integer> demo = Arrays.asList(5, 1, 2);
    //默认升序
    demo.stream().sorted().forEach(System.out::println);
    //降序
    Comparator<Integer> comparator = Comparator.<Integer, Integer>comparing(item -> item).reversed();
    demo.stream().sorted(comparator).forEach(System.out::println);
    -------默认升序 result--------
    1
    2
    5
    -------降序 result--------
    5
    2
    1

个数限制limit和跳过skip

//截取前maxSize个元素
Stream<T> limit(long maxSize);
//跳过前n个流
Stream<T> skip(long n);
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 3, 4, 5, 6);
    //跳过前两个,然后限制截取两个
    demo.stream().skip(2).limit(2).forEach(System.out::println);
    -------result--------
    3
    4

JDK9提供的新操作

  • 和filter的区别,takeWhile是取满足条件的元素,直到不满足为止;dropWhile是丢弃满足条件的元素,直到不满足为止

    default Stream<T> takeWhile(Predicate<? super T> predicate);
    default Stream<T> dropWhile(Predicate<? super T> predicate);

3 stream的终止操作action

遍历消费

//遍历消费
void forEach(Consumer<? super T> action);
//顺序遍历消费,和forEach的区别是forEachOrdered在多线程parallelStream执行,其顺序也不会乱
void forEachOrdered(Consumer<? super T> action);
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 3);
    demo.parallelStream().forEach(System.out::println);
    demo.parallelStream().forEachOrdered(System.out::println);
    -------forEach result--------
    2
    3
    1
    -------forEachOrdered result--------
    1
    2
    3

获取数组结果

//流转成Object数组
Object[] toArray();
//流转成A[]数组,指定类型A
<A> A[] toArray(IntFunction<A[]> generator)
  • 示例

    List<String> demo = Arrays.asList("1", "2", "3");
    //<A> A[] toArray(IntFunction<A[]> generator)
    String[] data = demo.stream().toArray(String[]::new);

最大最小值

//获取最小值
Optional<T> min(Comparator<? super T> comparator)
//获取最大值
Optional<T> max(Comparator<? super T> comparator)
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 3);
    Optional<Integer> min = demo.stream().min(Comparator.comparing(item->item));
    Optional<Integer> max = demo.stream().max(Comparator.comparing(item->item));
    System.out.println(min.get()+"-"+max.get());
    -------result--------
    1-3

查找匹配

//任意一个匹配
boolean anyMatch(Predicate<? super T> predicate)
//全部匹配
boolean allMatch(Predicate<? super T> predicate)
//不匹配
boolean noneMatch(Predicate<? super T> predicate)
//查找第一个
Optional<T> findFirst();
//任意一个
Optional<T> findAny();

归约合并

//两两合并
Optional<T> reduce(BinaryOperator<T> accumulator)
//两两合并,带初始值的
T reduce(T identity, BinaryOperator<T> accumulator)
//先转化元素类型再两两合并,带初始值的
<U> U reduce(U identity, BiFunction<U, ? super T, U> accumulator, BinaryOperator<U> combiner)
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8);
    //数字转化为字符串,然后使用“-”拼接起来
    String data = demo.stream().reduce("0", (u, t) -> u + "-" + t, (s1, s2) -> s1 + "-" + s2);
    System.out.println(data);
    -------result--------
    0-1-2-3-4-5-6-7-8

计算元素个数

long count()
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 3, 4, 5, 6);
    System.out.println(demo.stream().count());
    -------result--------
    6

对流的聚合处理

/**
* supplier:返回结果类型的生产者
* accumulator:元素消费者(处理并加入R)
* combiner: 返回结果 R 怎么组合(多线程执行时,会产生多个返回值R,需要合并)
*/
<R> R collect(Supplier<R> supplier, BiConsumer<R, ? super T> accumulator, BiConsumer<R, R> combiner);
/**
* collector一般是由 supplier、accumulator、combiner、finisher、characteristics组合成的聚合类
* Collectors 可提供一些内置的聚合类或者方法
*/
<R, A> R collect(Collector<? super T, A, R> collector);
  • 示例,看下面

4 Collector(聚合类)的工具类集Collectors

接口Collector和实现类CollectorImpl

//返回值类型的生产者
Supplier<A> supplier();
//流元素消费者
BiConsumer<A, T> accumulator();
//返回值合并器(多个线程操作时,会产生多个返回值,需要合并)
BinaryOperator<A> combiner();
//返回值转化器(最后一步处理,实际返回结果,一般原样返回)
Function<A, R> finisher();
//流的特性
Set<Characteristics> characteristics();
public static<T, A, R> Collector<T, A, R> of(Supplier<A> supplier,
BiConsumer<A, T> accumulator, BinaryOperator<A> combiner,
Function<A, R> finisher, Characteristics... characteristics)

流聚合转换成List, Set

//流转化成List
public static <T> Collector<T, ?, List<T>> toList()
//流转化成Set
public static <T> Collector<T, ?, Set<T>> toSet()
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 3);
    List<Integer> col = demo.stream().collect(Collectors.toList());
    Set<Integer> set = demo.stream().collect(Collectors.toSet());

流聚合转化成Map

//流转化成Map
public static <T, K, U> Collector<T, ?, Map<K,U>> toMap(
Function<? super T, ? extends K> keyMapper,
Function<? super T, ? extends U> valueMapper)
/**
* mergeFunction:相同的key,值怎么合并
*/
public static <T, K, U> Collector<T, ?, Map<K,U>> toMap(
Function<? super T, ? extends K> keyMapper,
Function<? super T, ? extends U> valueMapper,
BinaryOperator<U> mergeFunction)
/**
* mergeFunction:相同的key,值怎么合并
* mapSupplier:返回值Map的生产者
*/
public static <T, K, U, M extends Map<K, U>> Collector<T, ?, M> toMap(
Function<? super T, ? extends K> keyMapper,
Function<? super T, ? extends U> valueMapper,
BinaryOperator<U> mergeFunction,
Supplier<M> mapSupplier)
  • 如果存在相同key的元素,会报错;或者使用groupBy
  • 示例

    List<User> demo = Arrays.asList(new User(1), new User(2), new User(3));
    Map<Integer,User> map = demo.stream().collect(Collectors.toMap(User::getId,item->item));
    System.out.println(map);
    -------result-------
    {[email protected], [email protected], [email protected]}

字符串流聚合拼接

//多个字符串拼接成一个字符串
public static Collector<CharSequence, ?, String> joining();
//多个字符串拼接成一个字符串(指定分隔符)
public static Collector<CharSequence, ?, String> joining(CharSequence delimiter)
  • 示例

    List<String> demo = Arrays.asList("c", "s", "c","w","潜行前行");
    String name = demo.stream().collect(Collectors.joining("-"));
    System.out.println(name);
    -------result-------
    c-s-c-w-潜行前行

映射处理再聚合流

  • 相当于先map再collect

    /**
    * mapper:映射处理器
    * downstream:映射处理后需要再次聚合处理
    */
    public static <T, U, A, R> Collector<T, ?, R> mapping(Function<? super T, ? extends U> mapper,
    Collector<? super U, A, R> downstream);
  • 示例

    List<String> demo = Arrays.asList("1", "2", "3");
    List<Integer> data = demo.stream().collect(Collectors.mapping(Integer::valueOf, Collectors.toList()));
    System.out.println(data);
    -------result-------
    [1, 2, 3]

聚合后再转换结果

/**
* downstream:聚合处理
* finisher:结果转换处理
*/
public static<T,A,R,RR> Collector<T,A,RR> collectingAndThen(Collector<T,A,R> downstream,
Function<R, RR> finisher); 
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 3, 4, 5, 6);
    //聚合成List,最后提取数组的size作为返回值
    Integer size = demo.stream().collect(Collectors.collectingAndThen(Collectors.toList(), List::size));
    System.out.println(size);
    ---------result----------
    6

流分组(Map是HashMap)

/**
* classifier 指定T类型某一属性作为Key值分组
* 分组后,使用List作为每个流的容器
*/
public static <T, K> Collector<T, ?, Map<K, List<T>>> groupingBy(
Function<? super T, ? extends K> classifier);
/**
* classifier: 流分组器
* downstream: 每组流的聚合处理器
*/
public static <T, K, A, D> Collector<T, ?, Map<K, D>> groupingBy(
Function<? super T, ? extends K> classifier,
Collector<? super T, A, D> downstream)
/**
* classifier: 流分组器
* mapFactory: 返回值map的工厂(Map的子类)
* downstream: 每组流的聚合处理器
*/
public static <T, K, D, A, M extends Map<K, D>> Collector<T, ?, M> groupingBy(
Function<? super T, ? extends K> classifier,
Supplier<M> mapFactory,
Collector<? super T, A, D> downstream)
  • 示例

    public static void main(String[] args) throws Exception {
    List<Integer> demo = Stream.iterate(0, item -> item + 1)
    .limit(15)
    .collect(Collectors.toList());
    // 分成三组,并且每组元素转化为String类型
    Map<Integer, List<String>> map = demo.stream()
    .collect(Collectors.groupingBy(item -> item % 3,
    HashMap::new,
    Collectors.mapping(String::valueOf, Collectors.toList())));
    System.out.println(map);
    }
    ---------result----------
    {0=[0, 3, 6, 9, 12], 1=[1, 4, 7, 10, 13], 2=[2, 5, 8, 11, 14]} 

流分组(分组使用的Map是ConcurrentHashMap)

/**
* classifier: 分组器 ; 分组后,使用List作为每个流的容器
*/
public static <T, K> Collector<T, ?, ConcurrentMap<K, List<T>>> groupingByConcurrent(
Function<? super T, ? extends K> classifier);
/**
* classifier: 分组器
* downstream: 流的聚合处理器
*/
public static <T, K, A, D> Collector<T, ?, ConcurrentMap<K, D>> groupingByConcurrent(
Function<? super T, ? extends K> classifier, Collector<? super T, A, D> downstream)
/**
* classifier: 分组器
* mapFactory: 返回值类型map的生产工厂(ConcurrentMap的子类)
* downstream: 流的聚合处理器
*/
public static <T, K, A, D, M extends ConcurrentMap<K, D>> Collector<T, ?, M> groupingByConcurrent(
Function<? super T, ? extends K> classifier,
Supplier<M> mapFactory,
Collector<? super T, A, D> downstream);
  • 用法和groupingBy一样

拆分流,一变二(相当于特殊的groupingBy)

public static <T> Collector<T, ?, Map<Boolean, List<T>>> partitioningBy(
Predicate<? super T> predicate)
/**
* predicate: 二分器
* downstream: 流的聚合处理器
*/
public static <T, D, A> Collector<T, ?, Map<Boolean, D>> partitioningBy(
Predicate<? super T> predicate, Collector<? super T, A, D> downstream)
  • 示例

    List<Integer> demo = Arrays.asList(1, 2,3,4, 5,6);
    // 奇数偶数分组
    Map<Boolean, List<Integer>> map = demo.stream()
    .collect(Collectors.partitioningBy(item -> item % 2 == 0));
    System.out.println(map);
    ---------result----------
    {false=[1, 3, 5], true=[2, 4, 6]}

聚合求平均值

// 返回Double类型
public static <T> Collector<T, ?, Double> averagingDouble(ToDoubleFunction<? super T> mapper)
// 返回Long 类型
public static <T> Collector<T, ?, Double> averagingLong(ToLongFunction<? super T> mapper)
//返回Int 类型
public static <T> Collector<T, ?, Double> averagingInt(ToIntFunction<? super T> mapper)
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 5);
    Double data = demo.stream().collect(Collectors.averagingInt(Integer::intValue));
    System.out.println(data);
    ---------result----------
    2.6666666666666665

流聚合查找最大最小值

//最小值
public static <T> Collector<T, ?, Optional<T>> minBy(Comparator<? super T> comparator)
//最大值
public static <T> Collector<T, ?, Optional<T>> maxBy(Comparator<? super T> comparator) 
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 5);
    Optional<Integer> min = demo.stream().collect(Collectors.minBy(Comparator.comparing(item -> item)));
    Optional<Integer> max = demo.stream().collect(Collectors.maxBy(Comparator.comparing(item -> item)));
    System.out.println(min.get()+"-"+max.get());
    ---------result----------
    1-5

聚合计算统计结果

  • 可以获得元素总个数,元素累计总和,最小值,最大值,平均值

    //返回Int 类型
    public static <T> Collector<T, ?, IntSummaryStatistics> summarizingInt(
    ToIntFunction<? super T> mapper)
    //返回Double 类型
    public static <T> Collector<T, ?, DoubleSummaryStatistics> summarizingDouble(
    ToDoubleFunction<? super T> mapper)
    //返回Long 类型
    public static <T> Collector<T, ?, LongSummaryStatistics> summarizingLong(
    ToLongFunction<? super T> mapper) 
  • 示例

    List<Integer> demo = Arrays.asList(1, 2, 5);
    IntSummaryStatistics data = demo.stream().collect(Collectors.summarizingInt(Integer::intValue));
    System.out.println(data);
    ---------result----------
    IntSummaryStatistics{count=3, sum=8, min=1, average=2.666667, max=5}

JDK12提供的新聚合方法

//流分别经过downstream1、downstream2聚合处理,再合并两聚合结果
public static <T, R1, R2, R> Collector<T, ?, R> teeing(
Collector<? super T, ?, R1> downstream1,
Collector<? super T, ?, R2> downstream2,
BiFunction<? super R1, ? super R2, R> merger) 

5 并发paralleStream的使用

  • 配合CompletableFuture和线程池的使用
  • 示例

    public static void main(String[] args) throws Exception{
    List<Integer> demo = Stream.iterate(0, item -> item + 1)
    .limit(5)
    .collect(Collectors.toList());
    //示例1
    Stopwatch stopwatch = Stopwatch.createStarted(Ticker.systemTicker());
    demo.stream().forEach(item -> {
    try {
    Thread.sleep(500);
    System.out.println("示例1-"+Thread.currentThread().getName());
    } catch (Exception e) { }
    });
    System.out.println("示例1-"+stopwatch.stop().elapsed(TimeUnit.MILLISECONDS));
    //示例2, 注意需要ForkJoinPool,parallelStream才会使用executor指定的线程,否则还是用默认的 ForkJoinPool.commonPool()
    ExecutorService executor = new ForkJoinPool(10);
    stopwatch.reset(); stopwatch.start();
    CompletableFuture.runAsync(() -> demo.parallelStream().forEach(item -> {
    try {
    Thread.sleep(1000);
    System.out.println("示例2-" + Thread.currentThread().getName());
    } catch (Exception e) { }
    }), executor).join();
    System.out.println("示例2-"+stopwatch.stop().elapsed(TimeUnit.MILLISECONDS));
    //示例3
    stopwatch.reset(); stopwatch.start();
    demo.parallelStream().forEach(item -> {
    try {
    Thread.sleep(1000);
    System.out.println("示例3-"+Thread.currentThread().getName());
    } catch (Exception e) { }
    });
    System.out.println("示例3-"+stopwatch.stop().elapsed(TimeUnit.MILLISECONDS));
    executor.shutdown();
    }
  • ——————-result————————–

    示例1-main
    示例1-main
    示例1-main
    示例1-main
    示例1-main
    示例1-2501
    示例2-ForkJoinPool-1-worker-19
    示例2-ForkJoinPool-1-worker-9
    示例2-ForkJoinPool-1-worker-5
    示例2-ForkJoinPool-1-worker-27
    示例2-ForkJoinPool-1-worker-23
    示例2-1004
    示例3-main
    示例3-ForkJoinPool.commonPool-worker-5
    示例3-ForkJoinPool.commonPool-worker-7
    示例3-ForkJoinPool.commonPool-worker-9
    示例3-ForkJoinPool.commonPool-worker-3
    示例3-1001
  • parallelStream的方法确实会使用多线程去运行,并且可以指定线程池,不过自定义线程必须是ForkJoinPool类型,否则会默认使ForkJoinPool.commonPool()的线程

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