内容简介:Java8里的stream使用总结
java8里新特性之一 stream,非常的好用,就是容易忘了怎么写了,下面来总结一下
声明:代码来自尚硅谷官网上下载的 java 8视频教程
创建Stream
//1. 创建 Stream @Test public void test1() { //1. Collection 提供了两个方法 stream() 与 parallelStream() List<String> list = new ArrayList<>(); Stream<String> stream = list.stream(); //获取一个顺序流 Stream<String> parallelStream = list.parallelStream(); //获取一个并行流 //2. 通过 Arrays 中的 stream() 获取一个数组流 Integer[] nums = new Integer[10]; Stream<Integer> stream1 = Arrays.stream(nums); //3. 通过 Stream 类中静态方法 of() Stream<Integer> stream2 = Stream.of(1, 2, 3, 4, 5, 6); //4. 创建无限流 //迭代 Stream<Integer> stream3 = Stream.iterate(0, (x) -> x + 2).limit(10); stream3.forEach(System.out::println); //生成 Stream<Double> stream4 = Stream.generate(Math::random).limit(2); stream4.forEach(System.out::println); }
对Stream进行中间操作
//2. 中间操作 List<Employee> emps = Arrays.asList( new Employee(102, "李四", 59, 6666.66), new Employee(101, "张三", 18, 9999.99), new Employee(103, "王五", 28, 3333.33), new Employee(104, "赵六", 8, 7777.77), new Employee(104, "赵六", 8, 7777.77), new Employee(104, "赵六", 8, 7777.77), new Employee(105, "田七", 38, 5555.55) ); /* 筛选与切片 filter——接收 Lambda , 从流中排除某些元素。 limit——截断流,使其元素不超过给定数量。 skip(n) —— 跳过元素,返回一个扔掉了前 n 个元素的流。若流中元素不足 n 个,则返回一个空流。与 limit(n) 互补 distinct——筛选,通过流所生成元素的 hashCode() 和 equals() 去除重复元素 */ //内部迭代:迭代操作 Stream API 内部完成 @Test public void test2() { //所有的中间操作不会做任何的处理 Stream<Employee> stream = emps.stream() .filter((e) -> { System.out.println("测试中间操作"); return e.getAge() <= 35; }); //只有当做终止操作时,所有的中间操作会一次性的全部执行,称为“惰性求值” stream.forEach(System.out::println); } //外部迭代 @Test public void test3() { Iterator<Employee> it = emps.iterator(); while (it.hasNext()) { System.out.println(it.next()); } } @Test public void test4() { emps.stream() .filter((e) -> { System.out.println("短路!"); // && || return e.getSalary() >= 5000; }).limit(3) .forEach(System.out::println); } @Test public void test5() { emps.parallelStream() .filter((e) -> e.getSalary() >= 5000) .skip(2) .forEach(System.out::println); } @Test public void test6() { emps.stream() .distinct() .forEach(System.out::println); }
映射, 排序
//2. 中间操作 /* 映射 map——接收 Lambda , 将元素转换成其他形式或提取信息。接收一个函数作为参数,该函数会被应用到每个元素上,并将其映射成一个新的元素。 flatMap——接收一个函数作为参数,将流中的每个值都换成另一个流,然后把所有流连接成一个流 */ @Test public void test1() { Stream<String> str = emps.stream() .map((e) -> e.getName()); System.out.println("-------------------------------------------"); List<String> strList = Arrays.asList("aaa", "bbb", "ccc", "ddd", "eee"); Stream<String> stream = strList.stream() .map(String::toUpperCase); stream.forEach(System.out::println); Stream<Stream<Character>> stream2 = strList.stream() .map(TestStreamAPI1::filterCharacter); stream2.forEach((sm) -> { sm.forEach(System.out::println); }); System.out.println("---------------------------------------------"); Stream<Character> stream3 = strList.stream() .flatMap(TestStreamAPI1::filterCharacter); stream3.forEach(System.out::println); } public static Stream<Character> filterCharacter(String str) { List<Character> list = new ArrayList<>(); for (Character ch : str.toCharArray()) { list.add(ch); } return list.stream(); } /* sorted()——自然排序 sorted(Comparator com)——定制排序 */ @Test public void test2() { emps.stream() .map(Employee::getName) .sorted() .forEach(System.out::println); System.out.println("------------------------------------"); emps.stream() .sorted((x, y) -> { if (x.getAge() == y.getAge()) { return x.getName().compareTo(y.getName()); } else { return Integer.compare(x.getAge(), y.getAge()); } }).forEach(System.out::println); }
查找与匹配
List<Employee> emps = Arrays.asList( new Employee(102, "李四", 59, 6666.66, Status.BUSY), new Employee(101, "张三", 18, 9999.99, Status.FREE), new Employee(103, "王五", 28, 3333.33, Status.VOCATION), new Employee(104, "赵六", 8, 7777.77, Status.BUSY), new Employee(104, "赵六", 8, 7777.77, Status.FREE), new Employee(104, "赵六", 8, 7777.77, Status.FREE), new Employee(105, "田七", 38, 5555.55, Status.BUSY) ); //3. 终止操作 /* allMatch——检查是否匹配所有元素 anyMatch——检查是否至少匹配一个元素 noneMatch——检查是否没有匹配的元素 findFirst——返回第一个元素 findAny——返回当前流中的任意元素 count——返回流中元素的总个数 max——返回流中最大值 min——返回流中最小值 */ @Test public void test1() { boolean bl = emps.stream() .allMatch((e) -> e.getStatus().equals(Status.BUSY)); System.out.println(bl); boolean bl1 = emps.stream() .anyMatch((e) -> e.getStatus().equals(Status.BUSY)); System.out.println(bl1); boolean bl2 = emps.stream() .noneMatch((e) -> e.getStatus().equals(Status.BUSY)); System.out.println(bl2); } @Test public void test2() { Optional<Employee> op = emps.stream() .sorted((e1, e2) -> Double.compare(e1.getSalary(), e2.getSalary())) .findFirst(); System.out.println(op.get()); System.out.println("--------------------------------"); Optional<Employee> op2 = emps.parallelStream() .filter((e) -> e.getStatus().equals(Status.FREE)) .findAny(); System.out.println(op2.get()); } @Test public void test3() { long count = emps.stream() .filter((e) -> e.getStatus().equals(Status.FREE)) .count(); System.out.println(count); Optional<Double> op = emps.stream() .map(Employee::getSalary) .max(Double::compare); System.out.println(op.get()); Optional<Employee> op2 = emps.stream() .min((e1, e2) -> Double.compare(e1.getSalary(), e2.getSalary())); System.out.println(op2.get()); } //注意:流进行了终止操作后,不能再次使用 @Test public void test4() { Stream<Employee> stream = emps.stream() .filter((e) -> e.getStatus().equals(Status.FREE)); long count = stream.count(); stream.map(Employee::getSalary) .max(Double::compare); }
归约和收集
List<Employee> emps = Arrays.asList( new Employee(102, "李四", 79, 6666.66, Status.BUSY), new Employee(101, "张三", 18, 9999.99, Status.FREE), new Employee(103, "王五", 28, 3333.33, Status.VOCATION), new Employee(104, "赵六", 8, 7777.77, Status.BUSY), new Employee(104, "赵六", 8, 7777.77, Status.FREE), new Employee(104, "赵六", 8, 7777.77, Status.FREE), new Employee(105, "田七", 38, 5555.55, Status.BUSY) ); //3. 终止操作 /* 归约 reduce(T identity, BinaryOperator) / reduce(BinaryOperator) ——可以将流中元素反复结合起来,得到一个值。 */ @Test public void test1() { List<Integer> list = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10); Integer sum = list.stream() .reduce(0, (x, y) -> x + y); System.out.println(sum); System.out.println("----------------------------------------"); Optional<Double> op = emps.stream() .map(Employee::getSalary) .reduce(Double::sum); System.out.println(op.get()); } //需求:搜索名字中 “六” 出现的次数 @Test public void test2() { Optional<Integer> sum = emps.stream() .map(Employee::getName) .flatMap(TestStreamAPI1::filterCharacter) .map((ch) -> { if (ch.equals('六')) return 1; else return 0; }).reduce(Integer::sum); System.out.println(sum.get()); } //collect——将流转换为其他形式。接收一个 Collector接口的实现,用于给Stream中元素做汇总的方法 @Test public void test3() { List<String> list = emps.stream() .map(Employee::getName) .collect(Collectors.toList()); list.forEach(System.out::println); System.out.println("----------------------------------"); Set<String> set = emps.stream() .map(Employee::getName) .collect(Collectors.toSet()); set.forEach(System.out::println); System.out.println("----------------------------------"); HashSet<String> hs = emps.stream() .map(Employee::getName) .collect(Collectors.toCollection(HashSet::new)); hs.forEach(System.out::println); } @Test public void test4() { Optional<Double> max = emps.stream() .map(Employee::getSalary) .collect(Collectors.maxBy(Double::compare)); System.out.println(max.get()); Optional<Employee> op = emps.stream() .collect(Collectors.minBy((e1, e2) -> Double.compare(e1.getSalary(), e2.getSalary()))); System.out.println(op.get()); Double sum = emps.stream() .collect(Collectors.summingDouble(Employee::getSalary)); System.out.println(sum); Double avg = emps.stream() .collect(Collectors.averagingDouble(Employee::getSalary)); System.out.println(avg); Long count = emps.stream() .collect(Collectors.counting()); System.out.println(count); System.out.println("--------------------------------------------"); DoubleSummaryStatistics dss = emps.stream() .collect(Collectors.summarizingDouble(Employee::getSalary)); System.out.println(dss.getMax()); } //分组 @Test public void test5() { Map<Status, List<Employee>> map = emps.stream() .collect(Collectors.groupingBy(Employee::getStatus)); System.out.println(map); } //多级分组 @Test public void test6() { Map<Status, Map<String, List<Employee>>> map = emps.stream() .collect(Collectors.groupingBy(Employee::getStatus, Collectors.groupingBy((e) -> { if (e.getAge() >= 60) return "老年"; else if (e.getAge() >= 35) return "中年"; else return "成年"; }))); System.out.println(map); } //分区 @Test public void test7() { Map<Boolean, List<Employee>> map = emps.stream() .collect(Collectors.partitioningBy((e) -> e.getSalary() >= 5000)); System.out.println(map); } // @Test public void test8() { String str = emps.stream() .map(Employee::getName) .collect(Collectors.joining(",", "----", "----")); System.out.println(str); } @Test public void test9() { Optional<Double> sum = emps.stream() .map(Employee::getSalary) .collect(Collectors.reducing(Double::sum)); System.out.println(sum.get()); }
以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网
猜你喜欢:本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们。
C++ Primer Plus
Stephen Prata / Addison Wesley / 2011-10-18 / GBP 39.99
C++ Primer Plus, Sixth Edition New C++11 Coverage C++ Primer Plus is a carefully crafted, complete tutorial on one of the most significant and widely used programming languages today. An accessible an......一起来看看 《C++ Primer Plus》 这本书的介绍吧!