JDK1.8新特性(五): Collectors

二：示例

流转换成集合

@Test public void testToCollection(){ List 
   
     list = Arrays.asList(1, 2, 3); // [10, 20, 30] List 
    
      collect = list.stream().map(i -> i * 10).collect(Collectors.toList()); // [20, 10, 30] Set 
     
       collect1 = list.stream().map(i -> i * 10).collect(Collectors.toSet()); // {key1=value:10, key2=value:20, key3=value:30} Map 
      
        collect2 = list.stream().map(i -> i * 10).collect(Collectors.toMap(key -> "key" + key/10, value -> "value:" + value)); // [1, 3, 4] TreeSet 
       
         collect3= Stream.of(1, 3, 4).collect(Collectors.toCollection(TreeSet::new)); } 
        
       
      
     
   

@Data @ToString @AllArgsConstructor @RequiredArgsConstructor public class User { private Long id; private String username; } @Test public void testToMap() { List 
   
     userList = Arrays.asList( new User(1L, "mengday"), new User(2L, "mengdee"), new User(3L, "mengdy") ); // toMap 可用于将List转为Map，便于通过key快速查找到某个value Map 
    
      userIdAndModelMap = userList.stream().collect(Collectors.toMap(User::getId, Function.identity())); User user = userIdAndModelMap.get(1L); // User(id=1, username=mengday) System.out.println(user); Map 
     
       userIdAndUsernameMap = userList.stream().collect(Collectors.toMap(User::getId, User::getUsername)); String username = userIdAndUsernameMap.get(1L); // mengday System.out.println(username); } 
      
     
   

集合元素拼接

@Test public void testJoining(){ // a,b,c List 
   
     list2 = Arrays.asList("a", "b", "c"); String result = list2.stream().collect(Collectors.joining(",")); // Collectors.joining(",")的结果是：a,b,c 然后再将结果 x + "d"操作, 最终返回a,b,cd String str= Stream.of("a", "b", "c").collect(Collectors.collectingAndThen(Collectors.joining(","), x -> x + "d")); } 
   

元素聚合

 @Test public void test(){ // 求最值 3 List 
   
     list = Arrays.asList(1, 2, 3); Integer maxValue = list.stream().collect(Collectors.collectingAndThen(Collectors.maxBy((a, b) -> a - b), Optional::get)); // 最小值 1 Integer minValue = list.stream().collect(Collectors.collectingAndThen(Collectors.minBy((a, b) -> a - b), Optional::get)); // 求和 6 Integer sumValue = list.stream().collect(Collectors.summingInt(item -> item)); // 平均值 2.0 Double avg = list.stream().collect(Collectors.averagingDouble(x -> x)); } 
   

@Test public void test(){ // 映射：先对集合中的元素进行映射，然后再对映射的结果使用Collectors操作 // A,B,C Stream.of("a", "b", "c").collect(Collectors.mapping(x -> x.toUpperCase(), Collectors.joining(","))); } 

分组

 public class User { private Long id; private String username; private Integer type; // Getter & Setter & toString } @Test public void testGroupBy(){ List 
   
     list = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10); // 奇偶数分组：奇数分一组，偶数分一组 // groupingBy(Function 
     classifier) 参数是Function类型，Function返回值可以是要分组的条件，也可以是要分组的字段 // 返回的结果是Map，其中key的数据类型为Function体中计算类型，value是List 
    
      类型，为分组的结果 Map 
     
       > result = list.stream().collect(Collectors.groupingBy(item -> item % 2 == 0)); // {false=[1, 3, 5, 7, 9], true=[2, 4, 6, 8, 10]} System.out.println(result); // partitioningBy 用于分成两组的情况 Map 
      
        > twoPartiton = list.stream().collect(Collectors.partitioningBy(item -> item % 2 == 0)); System.out.println(twoPartiton); User user = new User(1L, "zhangsan", 1); User user2 = new User(2L, "lisi", 2); User user3 = new User(3L, "wangwu", 3); User user4 = new User(4L, "fengliu", 1); List 
       
         users = Arrays.asList(user, user2, user3, user4); // 根据某个字段进行分组 Map 
        
          > userGroup = users.stream().collect(Collectors.groupingBy(item -> item.type)); / * key 为要分组的字段 * value 分组的结果 * { * 1=[User{id=1, username='zhangsan', type=1}, User{id=4, username='fengliu', type=1}], * 2=[User{id=2, username='lisi', type=2}], * 3=[User{id=3, username='wangwu', type=3}] * } */ System.out.println(userGroup); } // 分组并对分组中的数据统计 @Test public void testGroupBy2() { Foo foo1 = new Foo(1, 2); Foo foo2 = new Foo(2, 23); Foo foo3 = new Foo(2, 6); List 
         
           list = new ArrayList<>(4); list.add(foo1); list.add(foo2); list.add(foo3); Map 
          
            collect = list.stream().collect(Collectors.groupingBy(Foo::getCode, Collectors.summarizingInt(Foo::getCount))); IntSummaryStatistics statistics1 = collect.get(1); IntSummaryStatistics statistics2 = collect.get(2); System.out.println(statistics1.getSum()); System.out.println(statistics1.getAverage()); System.out.println(statistics1.getMax()); System.out.println(statistics1.getMin()); System.out.println(statistics1.getCount()); System.out.println(statistics2.getSum()); System.out.println(statistics2.getAverage()); System.out.println(statistics2.getMax()); System.out.println(statistics2.getMin()); System.out.println(statistics2.getCount()); } 
           
          
         
        
       
      
     
   

累计操作

@Test public void testReducing(){ // sum: 是每次累计计算的结果，b是Function的结果 System.out.println(Stream.of(1, 3, 4).collect(Collectors.reducing(0, x -> x + 1, (sum, b) -> { System.out.println(sum + "-" + b); return sum + b; }))); // 下面代码是对reducing函数功能实现的描述，用于理解reducing的功能 int sum = 0; List 
   
     list3 = Arrays.asList(1, 3, 4); for (Integer item : list3) { int b = item + 1; System.out.println(sum + "-" + b); sum = sum + b; } System.out.println(sum); // 注意reducing可以用于更复杂的累计计算，加减乘除或者更复杂的操作 // result = 2 * 4 * 5 = 40 System.out.println(Stream.of(1, 3, 4).collect(Collectors.reducing(1, x -> x + 1, (result, b) -> { System.out.println(result + "-" + b); return result * b; }))); } 
   

对BigDecimal分组求和。

Map<String, BigDecimal> returnPaymentMap = retrnPayments.stream().collect(Collectors.groupingBy( Payment::getPayType, Collectors.mapping(Payment::getPayAmount, Collectors.reducing(BigDecimal.ZERO, BigDecimal::add))) ); 

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