流中間操作

終端操作

Action Start

初始化

@Data
@AllArgsConstructor
public class Employee implements Serializable {

   private static final long serialVersionUID = -8078550089934349371L;

   private Long id;
   /**
    * 姓名
    */
   private String name;
   /**
    * 年齡
    */
   private Integer age;

   /**
    * 部門
    */
   private String department;

   /**
    * 專業
    */
   private String profession;

   /**
    * 地址
    */
   private String Address;
}
 

public static List<Employee> employees = Collections.unmodifiableList(Arrays.asList(
   new Employee(1L, "張三", 18, "技術開發中心", "計算機科學", "江西南昌"),
   new Employee(2L, "李四", 18, "技術開發中心", "計算機科學", "江西撫州"),
   new Employee(3L, "王五", 22, "人力行政中心", "土木工程", "北京"),
   new Employee(4L, "趙六", 23, "財務管理中心", "經濟管理", "廣東廣州"),
   new Employee(5L, "田七", 28, "財務管理中心", "機械與自動化", "上海"),
   new Employee(5L, "田七", 28, "財務管理中心", "機械與自動化", "上海"),
   new Employee(6L, "孔明", 26, "戰略合作事業部", "生化工程", "廣東深圳"),
   new Employee(7L, "玄德", 40, "戰略合作事業部", "生命科學", "江蘇南京"),
   new Employee(8L, "雲長", 33, "客戶服務部", "社會學", "廣東廣州"),
   new Employee(9L, "翼德", 28, "智能金融事業部", "經濟管理", "江蘇南京"),
   new Employee(10L, "魯肅", 36, "智能金融事業部", "經濟管理", "上海")
));

1.0 過濾

• filter 篩選年齡小於25的員工

  List<Employee> list = employees.stream()
            .filter(employee -> employee.getAge() > 25)
            .collect(Collectors.toList());

• distinct 篩選重復元素

  List<Employee> list = employees.stream()
              .filter(employee -> employee.getAge() > 25)
              .distinct()
              .collect(Collectors.toList());

• limit 截斷流

List<Employee> list = employees.stream()
           .filter(employee -> employee.getAge() > 25)
           .limit(3)
           .collect(Collectors.toList());

• ? skip 跳過元素

List<Employee> list = employees.stream()
               .filter(employee -> employee.getAge() > 25)
               .skip(2)
               .collect(Collectors.toList());

2.0 映射

• map 獲取所有員工的姓名

  List<String> list = employees.stream()
            .map(Employee::getName)
            .collect(Collectors.toList());

• flatMap 扁平流

  String[] arrays = {"Hello", "World"};
  List<String> list = Arrays.stream(arrays)
              .map(str -> str.split("")) // 映射成為Stream<String[]>
              .flatMap(Arrays::stream) // 扁平化為Stream<String>
              .distinct()
              .collect(Collectors.toList());

3.0 查找和匹配

• anyMatch 流中是否有一個元素能匹配給定的謂詞 返回 boolean

  boolean result = employees.stream()
           .anyMatch(employee -> employee.getAge() == 28);

• allMatch 流中的所有元素能否符合條件

   boolean result = employees.stream()
           .allMatch(employee -> employee.getAge() < 35);

• noneMatch 可以確保流中沒有任何元素與給定的謂詞匹配

  boolean result = employees.stream()
         .noneMatch(employee -> employee.getAge() > 40);

• findAny 返回當前流中的任意元素

  Optional<Employee> result = employees.stream()
             .filter(employee -> Objects.equals(employee.getProfession(), "計算機科學"))
             .findAny();

• findFirst 返回當前流中的第一個元素

  Optional<Employee> result = employees.stream()
             .filter(employee -> Objects.equals(employee.getProfession(), "計算機科學"))
             .findFirst();

  你可能會想，為什麽會同時有 findFirst 和 findAny 呢？答案是並行。找到第一個元素在並行上限制更多。如果你不關心返回的元素是哪個，請使用 findAny ，因為它在使用並行流時限制較少.

4.0 歸約

List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5);
// 計算數值總合
// 方式一
Integer result1 = numbers.stream().reduce(0, (a, b) -> a + b);
// 方式二
Integer result2 = numbers.stream().reduce(0, Integer::sum);
// 計算最大值
Optional<Integer> min = numbers.stream().reduce(Integer::min);
// 計算最小值
Optional<Integer> min = numbers.stream().reduce(Integer::max);

5.0 收集器

• eg : 計算 員工總數

long count = employees.stream().count();

Long count = employees.stream().collect(Collectors.counting());

• eg : 查找 員工中年齡最大的

  
  Optional<Employee> employee = employees.stream()
            .collect(Collectors.maxBy(Comparator.comparing(Employee::getAge)));
  
  Optional<Employee> employee = employees.stream()
               .max(Comparator.comparing(Employee::getAge));

• eg: 員工年齡匯總

  Integer sum = employees.stream()
            .mapToInt(Employee::getAge).sum();
  
  Integer sum = employees.stream()
            .collect(Collectors.summingInt(Employee::getAge));

• eg : 員工平均值

  Double averaging = employees.stream().collect(Collectors.averagingInt(Employee::getAge))

6.0 分組

• eg : 員工 按專業進行分組

  Map<String, List<Employee>> map = employees.stream()
            .collect(Collectors.groupingBy(Employee::getProfession));

• eg : 員工 按專業進行分組 並統計專業人數

  Map<String, Long> map = employees.stream()
            .collect(Collectors.groupingBy(Employee::getProfession, Collectors.counting()));

• eg : 按專業進行分組 並查找專業中年齡最大的那人

 Map<String, Optional<Employee>> map = employees.stream().collect(
           Collectors.groupingBy(Employee::getProfession,                                                          Collectors.maxBy(Comparator.comparing(Employee::getAge))));

• eg : 將收集器結果轉換成 另一種類型 Collectors.collectingAndThen

  Map<String, Employee> map = employees.stream().collect(
   Collectors.groupingBy(Employee::getProfession,Collectors.collectingAndThen(Collectors.maxBy(Comparator.comparing(Employee::getAge)),Optional::get)));

• eg : 將收集器映射成具體的對象

  Map<String, Set<Integer>> map = employees.stream().collect(
            Collectors.groupingBy(Employee::getProfession, Collectors.mapping(Employee::getAge, Collectors.toSet())));

  ?

Java8 新特性之流式數據處理