java stream 流操作 一些示例2

 

 

那么什么是Stream

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,比如:筛选、排序、聚合等。

Stream可以由数组或集合创建,对流的操作分为两种:

  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,终端操作结束后流无法再次使用。终端操作会产生一个新的集合或值。

另外,Stream有几个特性:

  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一个值。

  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可以通过集合数组创建。

1、通过 java.util.Collection.stream() 方法用集合创建流

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();

 

2、使用java.util.Arrays.stream(T[] array)方法用数组创建流

int[] array={1,3,5,6,8};
IntStream stream = Arrays.stream(array);

  

3、使用Stream的静态方法:of()、iterate()、generate()

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);

Stream<Double> stream3 = Stream.generate(Math::random).limit(3);
stream3.forEach(System.out::println);

 

遍历/匹配(foreach/find/match)

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());//匹配第一个值:7
System.out.println("匹配任意一个值:" + findAny.get());//匹配任意一个值:8
System.out.println("是否存在大于6的值:" + anyMatch);//是否存在大于6的值:true

 

筛选(filter)

案例一:筛选出Integer集合中大于7的元素,并打印出来

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),后文有详细介绍。

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);//高于8000的员工姓名:[Tom, Anni, Owen]

 

聚合(max/min/count)

案例一:获取String集合中最长的元素。

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());//最长的字符串:weoujgsd

案例二:获取Integer集合中的最大值。

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());//自然排序的最大值:11
System.out.println("自定义排序的最大值:" + max2.get());//自定义排序的最大值:11

案例三:获取员工工资最高的人。

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());//员工工资最大值:9500

案例四:计算Integer集合中大于6的元素的个数。

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);//4

 

映射(map/flatMap)

映射,可以将一个流的元素按照一定的映射规则映射到另一个流中。分为mapflatMap

  • 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。

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。

        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);//[ABCD, BCDD, DEFDE, FTR]
        System.out.println("每个元素+3:" + intListNew);//[4, 6, 8, 10, 12, 14]

案例二:将员工的薪资全部增加1000。

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personList:" + personList.get(0).getName() + "-->" + personList.get(0).getSalary());//一次改动personList:Tom-->8900
System.out.println("一次改动personListNew:" + personListNew.get(0).getName() + "-->" + personListNew.get(0).getSalary());//一次改动personListNew:Tom-->18900

// 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personList:" + personList.get(0).getName() + "-->" + personList.get(0).getSalary());//二次改动personList:Tom-->18900
System.out.println("二次改动后personListNew2:" + personListNew2.get(0).getName() + "-->" + personListNew2.get(0).getSalary());//二次改动后personListNew2:Tom-->18900

案例三:将两个字符数组合并成一个新的字符数组。

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(",");
    return Arrays.stream(split);
}).collect(Collectors.toList());

System.out.println("处理前的集合:" + list);//处理前的集合:[m,k,l,a, 1,3,5,7]
System.out.println("处理后的集合:" + listNew);//处理后的集合:[m, k, l, a, 1, 3, 5, 7]

 

归约(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。

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);//29,29,29
System.out.println("list求积:" + product.get());//2112
System.out.println("list求和:" + max.get() + "," + max2);//11,11

案例二:求所有员工的工资之和和最高工资。

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);//49300,49300,49300
System.out.println("最高工资:" + maxSalary + "," + maxSalary2);//9500,9500

 

收集(collect)

collect,收集,可以说是内容最繁多、功能最丰富的部分了。从字面上去理解,就是把一个流收集起来,最终可以是收集成一个值也可以收集成一个新的集合。

collect主要依赖java.util.stream.Collectors类内置的静态方法。

 

归集(toList/toSet/toMap)

因为流不存储数据,那么在流中的数据完成处理后,需要将流中的数据重新归集到新的集合里。toListtoSettoMap比较常用,另外还有toCollectiontoConcurrentMap等复杂一些的用法。

 下面用一个案例演示toListtoSettoMap

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);//[6, 4, 6, 6, 20]
System.out.println("toSet:" + set);//[4, 20, 6]
System.out.println("toMap:" + map);//{Tom=Person(name=Tom, salary=8900, age=23, sex=male, area=New York), Anni=Person(name=Anni, salary=8200, age=24, sex=female, area=New York)}

 

 

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: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。

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);//3
System.out.println("员工平均工资:" + average);//7900.0
System.out.println("员工工资总和:" + sum);//23700
System.out.println("员工工资所有统计:" + collect);//DoubleSummaryStatistics{count=3, sum=23700.000000, min=7000.000000, average=7900.000000, max=8900.000000}

 

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。

java stream 流操作 一些示例2

 

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

List<Person> personList = new ArrayList<Person>();
personList.add(new Person("Tom", 8900, 1, "male", "New York"));
personList.add(new Person("Jack", 7000, 1, "male", "Washington"));
personList.add(new Person("Lily", 7800, 1, "female", "Washington"));
personList.add(new Person("Anni", 8200, 1, "female", "New York"));
personList.add(new Person("Owen", 9500, 1, "male", "New York"));
personList.add(new Person("Alisa", 7900, 1, "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);

 

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。

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);//Tom,Jack,Lily
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);//A-B-C

归约(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。

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);//8700

// 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());//23700

排序(sorted)

sorted,中间操作。有两种排序:

  • 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

 

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"));

// 按工资升序排序(自然排序)
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());
// 先按工资再按年龄升序排序
List<String> newList3 = personList.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 = 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("按工资升序排序:" + newList);//[Lily, Tom, Sherry, Jack, Alisa]
System.out.println("按工资降序排序:" + newList2);//[Sherry, Jack, Alisa, Tom, Lily]
System.out.println("先按工资再按年龄升序排序:" + newList3);//[Lily, Tom, Sherry, Jack, Alisa]
System.out.println("先按工资再按年龄自定义降序排序:" + newList4);//[Alisa, Jack, Sherry, Tom, Lily]

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> 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);//[a, b, c, d, e, f, g]
System.out.println("limit:" + collect);//[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
System.out.println("skip:" + collect2);//[3, 5, 7, 9, 11]

 

 

 

 

 

 

 

附:

@Data
public class Person {

    private String name; // 姓名
    private int salary; // 薪资
    private int age; // 年龄
    private String sex; //性别
    private String area; // 地区

    // 构造方法
    public Person(String name, int salary, int age, String sex, String area) {
        this.name = name;
        this.salary = salary;
        this.age = age;
        this.sex = sex;
        this.area = area;
    }
}

 参考来源:blog.csdn.net/mu_wind/article/details/109516995

https://mp.weixin.qq.com/s/kYJjSIFlq8x4dVBWJ1O_CQ

3.6.1 归集(toList/toSet/toMap)

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