Map<String, WorkstationCenter> centerMap = workstationCenterList.stream().collect(Collectors.toMap(WorkstationCenter::getCenterId, WorkstationCenter ->WorkstationCenter));//Map<String, List<WorkstationCenter>> listMap =workstationCenters.stream().collect(Collectors.groupingBy(WorkstationCenter::getGrou...
1️⃣collect是Stream流的一个终止方法,会使用传入的收集器(入参)对结果执行相关的操作,这个收集器必须是Collector接口的某个具体实现类 2️⃣Collector是一个接口,collect方法的收集器是Collector接口的具体实现类3️⃣Collectors是一个工具类,提供了很多的静态工厂方法,提供了很多Collector接口的具体实现类,是...
所谓恒等处理,指的就是Stream的元素在经过Collector函数处理前后完全不变,例如toList()操作,只是最终将结果从Stream中取出放入到List对象中,并没有对元素本身做任何的更改处理: 恒等处理类型的Collector是实际编码中最常被使用的一种,比如: list.stream().collect(Collectors.toList()); list.stream().collect(Collec...
stream().collect(Collectors.maxBy(Comparator.comparing(Person::getAge))); // Optional[Person(id=1001, name=张三, birthday=1998-01-01, age=25, weight=70.24)], 注意返回类型是Optional 5. 统计结果:summarizingDouble、summarizingInt、summarizingLong 统计操作一般包含了计数、求平局、求和、最大、最小...
Map<Integer, List<Employee>> employeesByAge = employees.stream() .collect(Collectors.groupingBy(Employee::getAge)); Employee::getAge— 员工年龄getter作为方法参数[Function] 我们用简单的一行代码做到了! 按Function 和 Collector分组 我们将使用第二种方法,它接受Function和Collector作为方法参数。
Map<Integer, TreeSet<String>> result = strings.stream() .collect(groupingBy(String::length, toCollection(TreeSet::new))); System.out.println(result); // {1=[a], 2=[bb, cc], 3=[ddd]} 1. 2. 3. 4. 分组计数 如果你只是想知道分组元素的数量,提供一个自定义的 count() : ...
Map<String, Map<String, List<Product>>> prodMap= prodList.stream().collect(Collectors.groupingBy(Product::getCategory, Collectors.groupingBy(item -> { if(item.getNum() < 3) { return "3"; }else { return "other"; } }))); //{"啤酒":{"other":[{"category":"啤酒","id":4,"name...
//group by SalaryMap<BigDecimal,List<User>>groupByPriceMap=items.stream().collect(Collectors.groupingBy(User::getSalary));System.out.println(groupByPriceMap);// group by Salary, uses 'mapping' to convert List<Item> to Set<String>Map<BigDecimal,Set<String>>result=items.stream().collect(...
=menu.stream().collect(groupingBy(Dish::getType, mapping(dish -> { if (dish.getCalories() <= 400) return CaloricLevel.DIET;else if (dish.getCalories() <= 700) return CaloricLevel.NORMAL;else return CaloricLevel.FAT; },toCollection(HashSet::new)));System.out.println(caloricLevelsByType)...
Group by distinct department and salary pairs Map<Object,List<Integer>>map=persons.stream().collect(groupingBy(person->newPair<>(person.salary(),person.department()),mapping(Person::id,toList()));System.out.println(map); The program output clearly tells that persons 4 and 5 are in the sam...