基础篇(JAVA.Stream函数,优雅的数据流操作)

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

Collection声明的stream函数
default Stream 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 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 filter(Predicate 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 demo = Arrays.asList("a", "b", "c"); Predicate f1 = item -> item.equals("a"); Predicate f2 = item -> item.equals("b"); demo.stream().filter(f1.or(f2)).forEach(System.out::println); -------result-------- a b

映射转化 map
Stream map(Function mapper) IntStream mapToInt(ToIntFunction mapper); LongStream mapToLong(ToLongFunction mapper); DoubleStream mapToDouble(ToDoubleFunction 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 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 peek(Consumer 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 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
Stream flatMap(Function> mapper); IntStream flatMapToInt(Function mapper); LongStream flatMapToLong(Function mapper); DoubleStream flatMapToDouble(Function mapper);

  • flatMap:将元素为Stream\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> 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 distinct();

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

排序 sorted
Stream sorted(); Stream sorted(Comparator comparator);

  • 示例
    List demo = Arrays.asList(5, 1, 2); //默认升序 demo.stream().sorted().forEach(System.out::println); //降序 Comparator comparator = Comparator.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 limit(long maxSize); //跳过前n个流 Stream skip(long n);

  • 示例
    List 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 takeWhile(Predicate predicate); default Stream dropWhile(Predicate predicate);

3 stream的终止操作action 遍历消费
//遍历消费 void forEach(Consumer 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 action);

  • 示例
    List 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[] toArray(IntFunction generator)

  • 示例
    List demo = Arrays.asList("1", "2", "3"); // A[] toArray(IntFunction generator) String[] data = https://www.it610.com/article/demo.stream().toArray(String[]::new);

最大最小值
//获取最小值 Optional min(Comparator comparator) //获取最大值 Optional max(Comparator comparator)

  • 示例
    List demo = Arrays.asList(1, 2, 3); Optional min = demo.stream().min(Comparator.comparing(item->item)); Optional 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 predicate) //全部匹配 boolean allMatch(Predicate predicate) //不匹配 boolean noneMatch(Predicate predicate) //查找第一个 Optional findFirst(); //任意一个 Optional findAny();

归约合并
//两两合并 Optional reduce(BinaryOperator accumulator) //两两合并,带初始值的 T reduce(T identity, BinaryOperator accumulator) //先转化元素类型再两两合并,带初始值的 U reduce(U identity, BiFunction accumulator, BinaryOperator combiner)

  • 示例
    List 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 = https://www.it610.com/article/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 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 collect(Supplier supplier, BiConsumer accumulator, BiConsumer 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 collect(Collector 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 supplier(); //流元素消费者 BiConsumer 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 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 finisher(); //流的特性 Set characteristics(); public static Collector of(Supplier supplier, BiConsumer accumulator, BinaryOperator combiner, Function finisher, Characteristics... characteristics)

流聚合转换成List, Set
//流转化成List public static Collector> toList() //流转化成Set public static Collector> toSet()

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

流聚合转化成Map
//流转化成Map public static Collector> toMap( Function keyMapper, Function valueMapper) /** * mergeFunction:相同的key,值怎么合并 */ public static Collector> toMap( Function keyMapper, Function valueMapper, BinaryOperator mergeFunction) /** * mergeFunction:相同的key,值怎么合并 * mapSupplier:返回值Map的生产者 */ public static > Collector toMap( Function keyMapper, Function valueMapper, BinaryOperator mergeFunction, Supplier mapSupplier)

  • 如果存在相同key的元素,会报错; 或者使用groupBy
  • 示例
    List demo = Arrays.asList(new User(1), new User(2), new User(3)); Map map = demo.stream().collect(Collectors.toMap(User::getId,item->item)); System.out.println(map); -------result------- {1=TestS$User@7b23ec81, 2=TestS$User@6acbcfc0, 3=TestS$User@5f184fc6}

字符串流聚合拼接
//多个字符串拼接成一个字符串 public static Collector joining(); //多个字符串拼接成一个字符串(指定分隔符) public static Collector joining(CharSequence delimiter)

  • 示例
    List 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 Collector mapping(Function mapper, Collector downstream);

  • 示例
    List demo = Arrays.asList("1", "2", "3"); List data = https://www.it610.com/article/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 Collector collectingAndThen(Collector downstream, Function finisher);

  • 示例
    List 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 Collector>> groupingBy( Function classifier); /** * classifier: 流分组器 * downstream: 每组流的聚合处理器 */ public static Collector> groupingBy( Function classifier, Collector 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 > Collector groupingBy( Function classifier, Supplier mapFactory, Collector downstream)

  • 示例
    public static void main(String[] args) throws Exception { List 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 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 Collector>> groupingByConcurrent( Function classifier); /** * classifier: 分组器 * downstream: 流的聚合处理器 */ public static Collector> groupingByConcurrent( Function classifier, Collector 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 > Collector groupingByConcurrent( Function classifier, Supplier mapFactory, Collector downstream);

  • 用法和groupingBy一样
拆分流,一变二(相当于特殊的groupingBy)
public static Collector>> partitioningBy( Predicate predicate) /** * predicate: 二分器 * downstream: 流的聚合处理器 */ public static Collector> partitioningBy( Predicate predicate, Collector downstream)

  • 示例
    List demo = Arrays.asList(1, 2,3,4, 5,6); // 奇数偶数分组 Map> 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 Collector averagingDouble(ToDoubleFunction mapper) // 返回Long 类型 public static Collector averagingLong(ToLongFunction mapper) //返回Int 类型 public static Collector averagingInt(ToIntFunction mapper)

  • 示例
    List demo = Arrays.asList(1, 2, 5); Double data = https://www.it610.com/article/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 Collector> minBy(Comparator comparator) //最大值 public static Collector> maxBy(Comparator comparator)

  • 【基础篇(JAVA.Stream函数,优雅的数据流操作)】示例
    List demo = Arrays.asList(1, 2, 5); Optional min = demo.stream().collect(Collectors.minBy(Comparator.comparing(item -> item))); Optional 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 Collector summarizingInt( ToIntFunction mapper) //返回Double 类型 public static Collector summarizingDouble( ToDoubleFunction mapper) //返回Long 类型 public static Collector summarizingLong( ToLongFunction mapper)

  • 示例
    List demo = Arrays.asList(1, 2, 5); IntSummaryStatistics data = https://www.it610.com/article/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 Collector teeing( Collector downstream1, Collector downstream2, BiFunction 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 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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