2023-05-26    2024-05-09    5859 字  12 分钟

Stream概述

Stream将要处理的元素集合看作一种流,在流的过程中,借助Stream API对流中的元素进行操作,可以由数组或者集合创建,比如:筛选、排序、聚合等

Stream 可以由数组或集合创建,对流的操作访问两种:

  1. 中间操作,每次返回一个新的流,可以有多个。

  2. 终端操作,每个流只能进行一次终端操作,终端操作结束流无法再次使用。终端操作会产生一个新的集合或值。

    Stream特性:

    1. stream不存储数据,而是按照特定的规则对数据进行计算,一般会输出结果。
    2. stream不会改变数据源,通常情况下会产生一个新的集合或值。
    3. stream具有延迟执行特性,只有调用终端操作时,中间操作才会执行。

Stream创建

Stream可以通过数组或集合创建。

  1. 通过 java.util.Collection.stream() 方法用集合创建流
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List<String> list = Arrays.asList("a", "b", "c");  
// 创建一个顺序流  
Stream<String> stream = list.stream();  
// 创建一个并行流  
Stream<String> parallelStream = list.parallelStream();  
  1. 使用java.util.Arrays.stream(T[] array)方法用数组创建流

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    int[] array={1,3,5,6,8};  
    IntStream stream = Arrays.stream(array);  
    
  2. 使用Stream的静态方法:of()、iterate()、generate()

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    Stream<Integer> stream = Stream.of(1, 2, 3, 4, 5, 6);  
    
    Stream<Integer> stream2 = Stream.iterate(0, (x) -> x + 3).limit(4);  
    stream2.forEach(System.out::println); // 0 2 4 6 8 10  
    
    Stream<Double> stream3 = Stream.generate(Math::random).limit(3);  
    stream3.forEach(System.out::println);  
    

streamparallelStream的简单区分: 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

遍历/匹配foreach/find/match

Stream也是支持类似集合的遍历和匹配元素的,只是Stream中的元素是以Optional类型存在的。Stream的遍历、匹配非常简单。

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public class StreamTest {  
 public static void main(String[] args) {  
        List<Integer> list = Arrays.asList(7, 6, 9, 3, 8, 2, 1);  
  
        // 遍历输出符合条件的元素  
        list.stream().filter(x -> x > 6).forEach(System.out::println);  
        // 匹配第一个  
        Optional<Integer> findFirst = list.stream().filter(x -> x > 6).findFirst();  
        // 匹配任意(适用于并行流)  
        Optional<Integer> findAny = list.parallelStream().filter(x -> x > 6).findAny();  
        // 是否包含符合特定条件的元素  
        boolean anyMatch = list.stream().anyMatch(x -> x < 6);  
        System.out.println("匹配第一个值:" + findFirst.get());  
        System.out.println("匹配任意一个值:" + findAny.get());  
        System.out.println("是否存在大于6的值:" + anyMatch);  
    }  
}  

筛选fillter

筛选,是按照一定的规则校验流中的元素,将符合条件的元素提取到新的流中的操作。

案例一:筛选出Integer集合中大于7的元素,并打印出来

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public class StreamTest {  
 public static void main(String[] args) {  
  List<Integer> list = Arrays.asList(6, 7, 3, 8, 1, 2, 9);  
  Stream<Integer> stream = list.stream();  
  stream.filter(x -> x > 7).forEach(System.out::println);  
 }  
}  

案例二:筛选员工中工资高于8000的人,并形成新的集合。 形成新集合依赖collect(收集)

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public class StreamTest {  
 public static void main(String[] args) {  
  List<Person> personList = new ArrayList<Person>();  
  personList.add(new Person("Tom", 8900, 23, "male", "New York"));  
  personList.add(new Person("Jack", 7000, 25, "male", "Washington"));  
  personList.add(new Person("Lily", 7800, 21, "female", "Washington"));  
  personList.add(new Person("Anni", 8200, 24, "female", "New York"));  
  personList.add(new Person("Owen", 9500, 25, "male", "New York"));  
  personList.add(new Person("Alisa", 7900, 26, "female", "New York"));  
  
  List<String> fiterList = personList.stream().filter(x -> x.getSalary() > 8000).map(Person::getName)  
    .collect(Collectors.toList());  
  System.out.print("高于8000的员工姓名:" + fiterList);  
 }  
}  

聚合max/min/count

案例一:获取String集合中最长的元素。

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public class StreamTest {  
 public static void main(String[] args) {  
  List<String> list = Arrays.asList("adnm", "admmt", "pot", "xbangd", "weoujgsd");  
  
  Optional<String> max = list.stream().max(Comparator.comparing(String::length));  
  System.out.println("最长的字符串:" + max.get());  
 }  
} 

案例二:获取Integer集合中的最大值。

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 public class StreamTest {  
 public static void main(String[] args) {  
  List<Integer> list = Arrays.asList(7, 6, 9, 4, 11, 6);  
  
  // 自然排序  
  Optional<Integer> max = list.stream().max(Integer::compareTo);  
  // 自定义排序  
  Optional<Integer> max2 = list.stream().max(new Comparator<Integer>() {  
   @Override  
   public int compare(Integer o1, Integer o2) {  
    return o1.compareTo(o2);  
   }  
  });  
  System.out.println("自然排序的最大值:" + max.get());  
  System.out.println("自定义排序的最大值:" + max2.get());  
 }  
}  

案例三:获取员工工资最高的人。

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 public class StreamTest {  
 public static void main(String[] args) {  
  List<Person> personList = new ArrayList<Person>();  
  personList.add(new Person("Tom", 8900, 23, "male", "New York"));  
  personList.add(new Person("Jack", 7000, 25, "male", "Washington"));  
  personList.add(new Person("Lily", 7800, 21, "female", "Washington"));  
  personList.add(new Person("Anni", 8200, 24, "female", "New York"));  
  personList.add(new Person("Owen", 9500, 25, "male", "New York"));  
  personList.add(new Person("Alisa", 7900, 26, "female", "New York"));  
  
  Optional<Person> max = personList.stream().max(Comparator.comparingInt(Person::getSalary));  
  System.out.println("员工工资最大值:" + max.get().getSalary());  
 }  
}  

案例四:计算Integer集合中大于6的元素的个数

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public class StreamTest {  
 public static void main(String[] args) {  
  List<Integer> list = Arrays.asList(7, 6, 4, 8, 2, 11, 9);  
  
  long count = list.stream().filter(x -> x > 6).count();  
  System.out.println("list中大于6的元素个数:" + count);  
 }  
}  

映射map/flatMap

映射可以将一个流的元素按照一定的规则映射到另一个流中。分为map和flatMap:

  • 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,然后把所有的流连接成一个流。

案例一:英文字符串数组的元素全部改为大写。整数数组每个元素+3。

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public class StreamTest {  
 public static void main(String[] args) {  
  String[] strArr = { "abcd", "bcdd", "defde", "fTr" };  
  List<String> strList = Arrays.stream(strArr).map(String::toUpperCase).collect(Collectors.toList());  
  
  List<Integer> intList = Arrays.asList(1, 3, 5, 7, 9, 11);  
  List<Integer> intListNew = intList.stream().map(x -> x + 3).collect(Collectors.toList());  
  
  System.out.println("每个元素大写:" + strList);  
  System.out.println("每个元素+3:" + intListNew);  
 }  
}  

案例二:将员工的薪资全部增加1000。

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public class StreamTest {  
 public static void main(String[] args) {  
  List<Person> personList = new ArrayList<Person>();  
  personList.add(new Person("Tom", 8900, 23, "male", "New York"));  
  personList.add(new Person("Jack", 7000, 25, "male", "Washington"));  
  personList.add(new Person("Lily", 7800, 21, "female", "Washington"));  
  personList.add(new Person("Anni", 8200, 24, "female", "New York"));  
  personList.add(new Person("Owen", 9500, 25, "male", "New York"));  
  personList.add(new Person("Alisa", 7900, 26, "female", "New York"));  
  
  // 不改变原来员工集合的方式  
  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());  
  
  // 改变原来员工集合的方式  
  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() + "-->" + personListNew.get(0).getSalary());  
  System.out.println("二次改动后:" + personListNew2.get(0).getName() + "-->" + personListNew.get(0).getSalary());  
 }  
}  

案例三:将两个字符数组合并成一个新的字符数组。

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public class StreamTest {  
 public static void main(String[] args) {  
  List<String> list = Arrays.asList("m,k,l,a", "1,3,5,7");  
  List<String> listNew = list.stream().flatMap(s -> {  
   // 将每个元素转换成一个stream  
   String[] split = s.split(",");  
   Stream<String> s2 = Arrays.stream(split);  
   return s2;  
  }).collect(Collectors.toList());  
  
  System.out.println("处理前的集合:" + list);  
  System.out.println("处理后的集合:" + listNew);  
 }  
}  

规约reduce

归约,也称缩减,顾名思义,是把一个流缩减成一个值,能实现对集合求和、求乘积和求最值操作。

案例一:求Integer集合的元素之和、乘积和最大值。

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 public class StreamTest {  
 public static void main(String[] args) {  
  List<Integer> list = Arrays.asList(1, 3, 2, 8, 11, 4);  
  // 求和方式1  
  Optional<Integer> sum = list.stream().reduce((x, y) -> x + y);  
  // 求和方式2  
  Optional<Integer> sum2 = list.stream().reduce(Integer::sum);  
  // 求和方式3  
  Integer sum3 = list.stream().reduce(0, Integer::sum);  
    
  // 求乘积  
  Optional<Integer> product = list.stream().reduce((x, y) -> x * y);  
  
  // 求最大值方式1  
  Optional<Integer> max = list.stream().reduce((x, y) -> x > y ? x : y);  
  // 求最大值写法2  
  Integer max2 = list.stream().reduce(1, Integer::max);  
  
  System.out.println("list求和:" + sum.get() + "," + sum2.get() + "," + sum3);  
  System.out.println("list求积:" + product.get());  
  System.out.println("list求和:" + max.get() + "," + max2);  
 }  
}  

案例二:求所有员工的工资之和和最高工资。

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public class StreamTest {  
 public static void main(String[] args) {  
  List<Person> personList = new ArrayList<Person>();  
  personList.add(new Person("Tom", 8900, 23, "male", "New York"));  
  personList.add(new Person("Jack", 7000, 25, "male", "Washington"));  
  personList.add(new Person("Lily", 7800, 21, "female", "Washington"));  
  personList.add(new Person("Anni", 8200, 24, "female", "New York"));  
  personList.add(new Person("Owen", 9500, 25, "male", "New York"));  
  personList.add(new Person("Alisa", 7900, 26, "female", "New York"));  
  
  // 求工资之和方式1:  
  Optional<Integer> sumSalary = personList.stream().map(Person::getSalary).reduce(Integer::sum);  
  // 求工资之和方式2:  
  Integer sumSalary2 = personList.stream().reduce(0, (sum, p) -> sum += p.getSalary(),  
    (sum1, sum2) -> sum1 + sum2);  
  // 求工资之和方式3:  
  Integer sumSalary3 = personList.stream().reduce(0, (sum, p) -> sum += p.getSalary(), Integer::sum);  
  
  // 求最高工资方式1:  
  Integer maxSalary = personList.stream().reduce(0, (max, p) -> max > p.getSalary() ? max : p.getSalary(),  
    Integer::max);  
  // 求最高工资方式2:  
  Integer maxSalary2 = personList.stream().reduce(0, (max, p) -> max > p.getSalary() ? max : p.getSalary(),  
    (max1, max2) -> max1 > max2 ? max1 : max2);  
  
  System.out.println("工资之和:" + sumSalary.get() + "," + sumSalary2 + "," + sumSalary3);  
  System.out.println("最高工资:" + maxSalary + "," + maxSalary2);  
 }  
}  

收集

因为流不存储数据,那么在流中的数据完成处理之后,需要将流中的数据重新归集到新的集合里。toList,toSet,和toMap是比较常用的,另外还有toCollection,toConcurrentMap等一些复杂的用法。

  1. 归集(toList, toSet, toMap)

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     public class StreamTest {  
     public static void main(String[] args) {  
      List<Integer> list = Arrays.asList(1, 6, 3, 4, 6, 7, 9, 6, 20);  
      List<Integer> listNew = list.stream().filter(x -> x % 2 == 0).collect(Collectors.toList());  
      Set<Integer> set = list.stream().filter(x -> x % 2 == 0).collect(Collectors.toSet());  
    
      List<Person> personList = new ArrayList<Person>();  
      personList.add(new Person("Tom", 8900, 23, "male", "New York"));  
      personList.add(new Person("Jack", 7000, 25, "male", "Washington"));  
      personList.add(new Person("Lily", 7800, 21, "female", "Washington"));  
      personList.add(new Person("Anni", 8200, 24, "female", "New York"));  
    
      Map<?, Person> map = personList.stream().filter(p -> p.getSalary() > 8000)  
        .collect(Collectors.toMap(Person::getName, p -> p));  
      System.out.println("toList:" + listNew);  
      System.out.println("toSet:" + set);  
      System.out.println("toMap:" + map);  
     }  
    }  
    
  2. 统计(count/averaging)

    • 计数:count
    • 平均值:averagingIntaveragingLongaveragingDouble
    • 最值:maxByminBy
    • 求和:summingIntsummingLongsummingDouble
    • 统计以上所有:summarizingIntsummarizingLongsummarizingDouble

    案例:统计员工人数、平均工资、工资总额、最高工资。

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     public class StreamTest {  
     public static void main(String[] args) {  
      List<Person> personList = new ArrayList<Person>();  
      personList.add(new Person("Tom", 8900, 23, "male", "New York"));  
      personList.add(new Person("Jack", 7000, 25, "male", "Washington"));  
      personList.add(new Person("Lily", 7800, 21, "female", "Washington"));  
    
      // 求总数  
      Long count = personList.stream().collect(Collectors.counting());  
      // 求平均工资  
      Double average = personList.stream().collect(Collectors.averagingDouble(Person::getSalary));  
      // 求最高工资  
      Optional<Integer> max = personList.stream().map(Person::getSalary).collect(Collectors.maxBy(Integer::compare));  
      // 求工资之和  
      Integer sum = personList.stream().collect(Collectors.summingInt(Person::getSalary));  
      // 一次性统计所有信息  
      DoubleSummaryStatistics collect = personList.stream().collect(Collectors.summarizingDouble(Person::getSalary));  
    
      System.out.println("员工总数:" + count);  
      System.out.println("员工平均工资:" + average);  
      System.out.println("员工工资总和:" + sum);  
      System.out.println("员工工资所有统计:" + collect);  
     }  
    } 
    
  3. 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。

    案例:将员工按薪资是否高于8000分为两部分;将员工按性别和地区分组

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     public class StreamTest {  
     public static void main(String[] args) {  
      List<Person> personList = new ArrayList<Person>();  
      personList.add(new Person("Tom", 8900, "male", "New York"));  
      personList.add(new Person("Jack", 7000, "male", "Washington"));  
      personList.add(new Person("Lily", 7800, "female", "Washington"));  
      personList.add(new Person("Anni", 8200, "female", "New York"));  
      personList.add(new Person("Owen", 9500, "male", "New York"));  
      personList.add(new Person("Alisa", 7900, "female", "New York"));  
    
      // 将员工按薪资是否高于8000分组  
            Map<Boolean, List<Person>> part = personList.stream().collect(Collectors.partitioningBy(x -> x.getSalary() > 8000));  
            // 将员工按性别分组  
            Map<String, List<Person>> group = personList.stream().collect(Collectors.groupingBy(Person::getSex));  
            // 将员工先按性别分组,再按地区分组  
            Map<String, Map<String, List<Person>>> group2 = personList.stream().collect(Collectors.groupingBy(Person::getSex, Collectors.groupingBy(Person::getArea)));  
            System.out.println("员工按薪资是否大于8000分组情况:" + part);  
            System.out.println("员工按性别分组情况:" + group);  
            System.out.println("员工按性别、地区:" + group2);  
     }  
    } 
    
  4. 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。

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     public class StreamTest {  
     public static void main(String[] args) {  
      List<Person> personList = new ArrayList<Person>();  
      personList.add(new Person("Tom", 8900, 23, "male", "New York"));  
      personList.add(new Person("Jack", 7000, 25, "male", "Washington"));  
      personList.add(new Person("Lily", 7800, 21, "female", "Washington"));  
    
      String names = personList.stream().map(p -> p.getName()).collect(Collectors.joining(","));  
      System.out.println("所有员工的姓名:" + names);  
      List<String> list = Arrays.asList("A", "B", "C");  
      String string = list.stream().collect(Collectors.joining("-"));  
      System.out.println("拼接后的字符串:" + string);  
     }  
    }  
    
  5. 规约reducing

    Collectors类提供的reducing方法,相比于stream本身的reduce方法,增加了对自定义归约的支持

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     public class StreamTest {  
     public static void main(String[] args) {  
      List<Person> personList = new ArrayList<Person>();  
      personList.add(new Person("Tom", 8900, 23, "male", "New York"));  
      personList.add(new Person("Jack", 7000, 25, "male", "Washington"));  
      personList.add(new Person("Lily", 7800, 21, "female", "Washington"));  
    
      // 每个员工减去起征点后的薪资之和(这个例子并不严谨,但一时没想到好的例子)  
      Integer sum = personList.stream().collect(Collectors.reducing(0, Person::getSalary, (i, j) -> (i + j - 5000)));  
      System.out.println("员工扣税薪资总和:" + sum);  
    
      // stream的reduce  
      Optional<Integer> sum2 = personList.stream().map(Person::getSalary).reduce(Integer::sum);  
      System.out.println("员工薪资总和:" + sum2.get());  
     }  
    }  
    
  6. 排序sourted

    • 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

    案例:将员工按工资由高到低(工资一样则按年龄由大到小)排序

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     public class StreamTest {  
     public static void main(String[] args) {  
      List<Person> personList = new ArrayList<Person>();  
    
      personList.add(new Person("Sherry", 9000, 24, "female", "New York"));  
      personList.add(new Person("Tom", 8900, 22, "male", "Washington"));  
      personList.add(new Person("Jack", 9000, 25, "male", "Washington"));  
      personList.add(new Person("Lily", 8800, 26, "male", "New York"));  
      personList.add(new Person("Alisa", 9000, 26, "female", "New York"));  
    
      // 按工资增序排序  
      List<String> newList = personList.stream().sorted(Comparator.comparing(Person::getSalary)).map(Person::getName)  
        .collect(Collectors.toList());  
      // 按工资倒序排序  
      List<String> newList2 = personList.stream().sorted(Comparator.comparing(Person::getSalary).reversed())  
        .map(Person::getName).collect(Collectors.toList());  
      // 先按工资再按年龄自然排序(从小到大)  
      List<String> newList3 = personList.stream().sorted(Comparator.comparing(Person::getSalary).reversed())  
        .map(Person::getName).collect(Collectors.toList());  
      // 先按工资再按年龄自定义排序(从大到小)  
      List<String> newList4 = personList.stream().sorted((p1, p2) -> {  
       if (p1.getSalary() == p2.getSalary()) {  
        return p2.getAge() - p1.getAge();  
       } else {  
        return p2.getSalary() - p1.getSalary();  
       }  
      }).map(Person::getName).collect(Collectors.toList());  
    
      System.out.println("按工资自然排序:" + newList);  
      System.out.println("按工资降序排序:" + newList2);  
      System.out.println("先按工资再按年龄自然排序:" + newList3);  
      System.out.println("先按工资再按年龄自定义降序排序:" + newList4);  
     }  
    } 
    
  7. 提取/组合

    流也可以进行合并、去重、限制、跳过等操作。

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    public class StreamTest {  
     public static void main(String[] args) {  
      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> newList = Stream.concat(stream1, stream2).distinct().collect(Collectors.toList());  
      // limit:限制从流中获得前n个数据  
      List<Integer> collect = Stream.iterate(1, x -> x + 2).limit(10).collect(Collectors.toList());  
      // skip:跳过前n个数据  
      List<Integer> collect2 = Stream.iterate(1, x -> x + 2).skip(1).limit(5).collect(Collectors.toList());  
    
      System.out.println("流合并:" + newList);  
      System.out.println("limit:" + collect);  
      System.out.println("skip:" + collect2);  
     }  
    }  
    

Collectors.toMap使用

  1. 使用规则

    toMap(Function, Function)返回一个Collector,它将元素累积到一个Map中,其键和值是将提供的映射函数应用于输入的元素的结果。

    当映射的键包含重复项,则在执行收集操作时会抛出IIIegalStateException。如果映射的键有重复项,可以用toMap(Function, Function, BinaryOperator)。

  2. 示例

    实体类:

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    public class Student {
        private String id;
        private String name;
        public String getId() {
            return id;
        }
        public void setId(String id) {
            this.id = id;
        }
        public String getName() {
            return name;
        }
        public void setName(String name) {
            this.name = name;
        }
    }
    

    使用示例:

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    public class test {
        public static void main(String[] args) throws IOException {
            List<Student> list = new ArrayList<>();
            Student student1 =  new Student();
            student1.setId("1");
            student1.setName("cong");
            Student student2 =  new Student();
            student2.setId("2");
            student2.setName("pei");
            Student student3 =  new Student();
            student3.setId("3");
            student3.setName("tong");
            list.add(student1);
            list.add(student2);
            list.add(student3);
            Map<String, Student> newList = list.stream().collect(Collectors.toMap(Student::getId, Function.identity()));
            System.out.println(newList);
        }
    }
    

    结果:

    key,Student实体的Map对应关系

  3. 当key存在重复的解决办法

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    Map<String, Student> newList = list.stream().collect(Collectors.toMap(Student::getId, Function.identity(), (oldValue, newValue) -> {newValue})); // 取最新的key
    
  4. 获得id和name对应关系用法

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    Map<String, Student> newList = list.stream().collect(Collectors.toMap(Student::getId, Student::getName, (oldValue, newValue) -> {newValue})); // 取最新的key