java peek函数_基础篇：JAVA.Stream函数，优雅的数据流操作

写在开头：本文是转载于掘金上的一篇文章，已获得原作者授权，我会在文章最后放上原作者和原文链接。

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

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-------
{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<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<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]

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1Stopwatch 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３stopwatch.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