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
文章图片
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 extends T> a, Stream extends T> 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
Stream 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 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
Stream 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
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 peekStream
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
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
Stream 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\
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
Stream distinct();
- 示例
List
demo = Arrays.asList(1, 1, 2); demo.stream().distinct().forEach(System.out::println); -------result-------- 1 2
Stream sorted();
Stream sorted(Comparator super T> 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
//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
- 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 super T> predicate); default Stream dropWhile(Predicate super T> predicate);
//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
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
//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);
//获取最小值
Optional min(Comparator super T> comparator)
//获取最大值
Optional max(Comparator super T> 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
//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 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
long count()
- 示例
List
demo = Arrays.asList(1, 2, 3, 4, 5, 6); System.out.println(demo.stream().count()); -------result-------- 6
/**
* 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 super T, A, R> collector);
- 示例,看下面
//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());
//流转化成Map
public static Collector> 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 Collector> toMap(
Function super T, ? extends K> keyMapper,
Function super T, ? extends U> 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 super T, ? extends K> keyMapper,
Function super T, ? extends U> 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}
//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
- 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 super T, ? extends U> mapper, Collector super U, A, R> 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]
/**
* 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
/**
* 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 super T, ? extends K> classifier);
/**
* classifier: 流分组器
* downstream: 每组流的聚合处理器
*/
public static Collector> 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 > Collector groupingBy(
Function super T, ? extends K> classifier,
Supplier mapFactory,
Collector super T, A, D> 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]}
/**
* classifier: 分组器 ; 分组后,使用List作为每个流的容器
*/
public static Collector>> groupingByConcurrent(
Function super T, ? extends K> classifier);
/**
* classifier: 分组器
* downstream: 流的聚合处理器
*/
public static Collector> 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 > Collector groupingByConcurrent(
Function super T, ? extends K> classifier,
Supplier mapFactory,
Collector super T, A, D> downstream);
- 用法和groupingBy一样
public static Collector>> partitioningBy(
Predicate super T> predicate)
/**
* predicate: 二分器
* downstream: 流的聚合处理器
*/
public static Collector> partitioningBy(
Predicate super T> predicate, Collector super T, A, D> 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]}
// 返回Double类型
public static Collector averagingDouble(ToDoubleFunction super T> mapper)
// 返回Long 类型
public static Collector averagingLong(ToLongFunction super T> mapper)
//返回Int 类型
public static Collector averagingInt(ToIntFunction super T> 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
//最小值
public static Collector> minBy(Comparator super T> comparator)
//最大值
public static Collector> maxBy(Comparator super T> 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
- 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 super T> mapper) //返回Double 类型 public static Collector summarizingDouble( ToDoubleFunction super T> mapper) //返回Long 类型 public static Collector summarizingLong( ToLongFunction super T> 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}
//流分别经过downstream1、downstream2聚合处理,再合并两聚合结果
public static Collector 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
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
推荐阅读
- 2018年11月19日|2018年11月19日 星期一 亲子日记第144篇
- 《魔法科高中的劣等生》第26卷(Invasion篇)发售
- 拍照一年啦,如果你想了解我,那就请先看看这篇文章
- 亲子日记第186篇,2018、7、26、星期四、晴
- 漫画初学者如何学习漫画背景的透视画法(这篇教程请收藏好了!)
- 两短篇
- Python基础|Python基础 - 练习1
- 第四十三篇接纳孩子的感受
- 感恩日记第111篇2020.02.06
- 2018年8月25日|2018年8月25日 星期六 晴 亲子日记第259篇