1. Stream和parallelStream
stream是顺序流,由主线程按顺序对流执行操作,而parallelStream是并行流,内部以多线程并行执行的方式对流进行操作,但前提是流中的数据处理没有顺序要求。如果流中的数据量足够大,并行流可以加快处速度。除了直接创建并行流,还可以通过parallel()把顺序流转换成并行流:
Optional<Integer> findFirst = list.stream().parallel().filter(x->x>6).findFirst();
使用Stream的静态方法生成流:of()、iterate()、generate()
Stream<Integer> stream = Stream.of(1, 2, 3, 4, 5, 6); Stream<Integer> stream3 = Stream.iterate(0, (x) -> x + 3).limit(4); stream3.forEach(System.out::println); Stream<Double> stream4 = Stream.generate(Math::random).limit(3); stream4.forEach(System.out::println);
2. Stream使用
map: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。
public static void main(String[] args) { List<Person> personList = new ArrayList<>(); personList.add(new Person("张三", 1000, 20, "男", "北京")); personList.add(new Person("李四", 2000, 21, "男", "南京")); personList.add(new Person("王五", 3000, 20, "女", "合肥")); personList.add(new Person("赵六", 4000, 22, "男", "四川")); personList.add(new Person("孙七", 5000, 25, "女", "上海"));
List<Person> personListNew = personList.stream().map(person -> { Person personNew = new Person(person.getName(), 0, 0, null, null); personNew.setSalary(person.getSalary() + 10000); return personNew; }).collect(Collectors.toList()); System.out.println("一次改动前:" + personList.get(0).getName() + ">>>" + personList.get(0).getSalary()); System.out.println("一次改动后:" + personListNew.get(0).getName() + ">>>" + personListNew.get(0).getSalary()); List<Person> personListNew2 = personList.stream().map(person -> { person.setSalary(person.getSalary() + 10000); return person; }).collect(Collectors.toList()); System.out.println("二次改动前:" + personList.get(0).getName() + ">>>" + personList.get(0).getSalary()); System.out.println("二次改动后:" + personListNew2.get(0).getName() + ">>>" + personListNew2.get(0).getSalary()); }
结果:方式一:不改变原来员工集合;方式二:改变原来员工集合的方式
// 将两个字符数组合并成一个新的字符数组 List<String> list = Arrays.asList("Hello", "World"); Stream<String> stringStream = list.stream().map(s -> s.split("")).flatMap(Arrays::stream); stringStream.forEach(System.out::print); // 给定两个数字列表 获取所有的数对 List<Integer> numbers1 = Arrays.asList(1, 2, 3); List<Integer> numbers2 = Arrays.asList(3, 4); List<int[]> collect = numbers1.stream().flatMap(x -> numbers2.stream().map(y -> new int[]{x, y})).collect(Collectors.toList()); collect.forEach(c-> System.out.println(Arrays.toString(c)));
归集(toMap)
List<Person> personList3 = new ArrayList<>(); personList3.add(new Person("张三", 1000, 20, "男", "北京")); personList3.add(new Person("李四", 2000, 21, "男", "南京")); personList3.add(new Person("王五", 3000, 20, "女", "合肥")); personList3.add(new Person("赵六", 4000, 22, "男", "四川")); personList3.add(new Person("孙七", 5000, 25, "女", "上海")); Map<String, Integer> map = personList3.stream().filter(person -> person.getSalary() > 3000).collect(Collectors.toMap(Person::getName, Person::getSalary)); System.out.println("工资大于3000元的员工:" + map);
结果:工资大于3000元的员工:{孙七=5000, 赵六=4000}
统计(count/averaging)
Collectors提供了一系列用于数据统计的静态方法
计数:count
平均值:averagingInt、averagingLong、averagingDouble
最值:maxBy、minBy
求和:summingInt、summingLong、summingDouble
统计以上所有:summarizingInt、summarizingLong、summarizingDouble
List<Person> personList4 = new ArrayList<>(); personList4.add(new Person("张三", 1000, 20, "男", "北京")); personList4.add(new Person("李四", 2000, 21, "男", "南京")); personList4.add(new Person("王五", 3000, 20, "女", "合肥")); personList4.add(new Person("赵六", 4000, 22, "男", "四川")); personList4.add(new Person("孙七", 5000, 25, "女", "上海")); // 员工总人数 long count = personList4.stream().count(); // 平均工资 Double salary = personList4.stream().collect(Collectors.averagingDouble(Person::getSalary)); // 最高工资 Optional<Integer> max = personList4.stream().map(Person::getSalary).max(Integer::compare); // 工资之和 int sum = personList4.stream().mapToInt(Person::getSalary).sum(); //一次性统计所有信息 DoubleSummaryStatistics summaryStatistics = personList4.stream().collect(Collectors.summarizingDouble(Person::getSalary)); System.out.println("员工总人数:" + count); System.out.println("员工平均工资:" + salary); System.out.println("员工工资总和:" + max); System.out.println("员工工资所有统计:" + summaryStatistics);
分组(partitioningBy/groupingBy)
分区:将stream按条件分为两个Map,比如员工按薪资是否高于8000分为两部分。
分组:将集合分为多个Map,比如员工按性别分组。有单级分组和多级分组。
List<Person> personList5 = new ArrayList<>(); personList5.add(new Person("张三", 1000, 20, "男", "北京")); personList5.add(new Person("李四", 2000, 21, "男", "南京")); personList5.add(new Person("王五", 3000, 20, "女", "合肥")); personList5.add(new Person("赵六", 4000, 22, "男", "合肥")); personList5.add(new Person("孙七", 5000, 25, "女", "上海")); // 按薪资高于3000分组 Map<Boolean, List<Person>> salaryGroup = personList5.stream().collect(Collectors.partitioningBy(p -> p.getSalary() > 3000)); List<Person> group1 = salaryGroup.get(true); List<Person> group2 = salaryGroup.get(false); for (Person person : group1) { System.out.println("薪资高于3000元组:" + person); } for (Person person : group2) { System.out.println("薪资低于3000元组:" + person); } // 按性别分组 Map<String, List<Person>> sexGroup = personList5.stream().collect(Collectors.groupingBy(Person::getSex)); List<Person> group3 = sexGroup.get("男"); List<Person> group4 = sexGroup.get("女"); for (Person person : group3) { System.out.println("男子组:" + person); } for (Person person : group4) { System.out.println("女子组:" + person); } // 将员工先按性别分组,再按地区分组 Map<String, Map<String, List<Person>>> group = personList5.stream().collect(Collectors.groupingBy(Person::getSex, Collectors.groupingBy(Person::getArea))); Map<String, List<Person>> manGroup = group.get("男"); Map<String, List<Person>> womenGroup = group.get("女"); List<Person> group5 = manGroup.get("合肥"); List<Person> group6 = womenGroup.get("上海"); System.out.println("地区在合肥的男子组:" + group5); System.out.println("地区在上海的女子组:" + group6);
接合(joining)
joining可以将stream中的元素用特定的连接符(没有的话,则直接连接)连接成一个字符串。
String persons = personList5.stream().map(p -> p.getName() + "-" + p.getSex() + "-" + p.getSalary()).collect(Collectors.joining(",")); System.out.println("所有员工信息:" + persons);
结果:所有员工信息:张三-男-1000,李四-男-2000,王五-女-3000,赵六-男-4000,孙七-女-5000
排序(sorted)
sorted():自然排序,流中元素需实现Comparable接口
sorted(Comparator com):Comparator排序器自定义排序
List<Person> personList6 = new ArrayList<>(); personList6.add(new Person("张三", 16000, 20, "男", "北京")); personList6.add(new Person("李四", 8500, 21, "男", "南京")); personList6.add(new Person("王五", 7300, 20, "女", "合肥")); personList6.add(new Person("赵六", 8000, 22, "男", "合肥")); personList6.add(new Person("孙七", 15860, 25, "女", "上海")); // 按工资升序排序(自然排序) List<String> newList = personList6.stream().sorted(Comparator.comparing(Person::getSalary)).map(Person::getName).collect(Collectors.toList()); // 按工资倒序排序 List<String> newList2 = personList6.stream().sorted(Comparator.comparing(Person::getSalary).reversed()).map(Person::getName).collect(Collectors.toList()); // 先按工资再按年龄升序排序 List<String> newList3 = personList6.stream().sorted(Comparator.comparing(Person::getSalary).thenComparing(Person::getAge)).map(Person::getName).collect(Collectors.toList()); // 先按工资再按年龄自定义排序(降序) List<String> newList4 = personList6.stream().sorted((p1, p2) -> { if (p1.getSalary().equals(p2.getSalary())) { return p2.getAge() - p1.getAge(); } else { return p2.getSalary() - p1.getSalary(); } }).map(Person::getName).collect(Collectors.toList()); System.out.println("按工资升序排序:" + newList); System.out.println("按工资降序排序:" + newList2); System.out.println("先按工资再按年龄升序排序:" + newList3); System.out.println("先按工资再按年龄自定义降序排序:" + newList4);
提取/组合
流也可以进行合并、去重、限制、跳过等操作。
String[] arr1 = {"a", "b", "c", "d"}; String[] arr2 = {"d", "e", "f", "g"}; Stream<String> stream1 = Stream.of(arr1); Stream<String> stream2 = Stream.of(arr2); // concat:合并两个流 distinct:去重 List<String> collect1 = Stream.concat(stream1, stream2).distinct().collect(Collectors.toList()); List<Integer> collect2 = Stream.iterate(1, x -> x + 2).limit(10).collect(Collectors.toList()); List<Integer> collect3 = Stream.iterate(1, x -> x + 2).skip(1).limit(5).collect(Collectors.toList()); System.out.println("流合并:" + collect1); System.out.println("limit:" + collect2); System.out.println("skip:" + collect3);
标签:Java,stream,编程,System,Person,println,new,out From: https://www.cnblogs.com/homle/p/16884482.html