Java集合高效处理——Stream流操作

最新推荐文章于 2025-03-09 16:51:52 发布

study@lin017

最新推荐文章于 2025-03-09 16:51:52 发布

阅读量1.3k

点赞数 2

分类专栏： java学习文章标签： java

原文链接：https://www.kancloud.cn/hanxt/javacrazy/1568811

版权

java学习专栏收录该内容

8 篇文章

订阅专栏

一，什么是Java Stream API？

Java Stream函数式编程接口最初是在Java 8中引入的，并且与lambda一起成为Java开发的里程碑式的功能特性，它极大的方便了开放人员处理集合类数据的效率。从笔者之前看过的调查文章显示，绝大部分的开发者使用的JDK版本是java 8，其中Java Stream和lambda功不可没。

Java Stream就是一个数据流经的管道，并且在管道中对数据进行操作，然后流入下一个管道。有学过linux 管道的同学应该会很容易就理解。在没有Java Stram之前，对于集合类的操作，更多的是通过for循环。大家从后文中就能看出Java Stream相对于for 循环更加简洁、易用、快捷。

管道的功能包括：Filter（过滤）、Map(映射)、sort(排序）等，集合数据通过Java Stream管道处理之后，转化为另一组集合或数据输出。

Java Stream函数式编程？用过都说好，案例图文详解送给你

二、Stream API代替for循环

List<String> nameStrs = Arrays.asList("Monkey", "Lion", "Giraffe","Lemur");

List<String> list = nameStrs.stream()
        .filter(s -> s.startsWith("L"))
        .map(String::toUpperCase)
        .sorted()
        .collect(toList());
System.out.println(list);

三、将数组转换为管道流

String[] array = {"Monkey", "Lion", "Giraffe", "Lemur"};
Stream<String> nameStrs2 = Stream.of(array);

Stream<String> nameStrs3 = Stream.of("Monkey", "Lion", "Giraffe", "Lemur");

五、将文本文件转换为管道流

List<String> list = Arrays.asList("Monkey", "Lion", "Giraffe", "Lemur");
Stream<String> streamFromList = list.stream();

Set<String> set = new HashSet<>(list);
Stream<String> streamFromSet = set.stream();

五、将文本文件转换为管道流

Stream<String> lines = Files.lines(Paths.get("file.txt"));

六、filter与谓语逻辑

public class StreamFilterPredicate {
    
    public static void main(String[] args){
        Employee e1 = new Employee(1,23,"M","Rick","Beethovan");
        Employee e2 = new Employee(2,13,"F","Martina","Hengis");
        Employee e3 = new Employee(3,43,"M","Ricky","Martin");
        Employee e4 = new Employee(4,26,"M","Jon","Lowman");
        Employee e5 = new Employee(5,19,"F","Cristine","Maria");
        Employee e6 = new Employee(6,15,"M","David","Feezor");
        Employee e7 = new Employee(7,68,"F","Melissa","Roy");
        Employee e8 = new Employee(8,79,"M","Alex","Gussin");
        Employee e9 = new Employee(9,15,"F","Neetu","Singh");
        Employee e10 = new Employee(10,45,"M","Naveen","Jain");


        List<Employee> employees = Arrays.asList(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10);

        List<Employee> filtered = employees.stream()
                .filter(e -> e.getAge() > 70 && e.getGender().equals("M"))
                .collect(Collectors.toList());

        System.out.println(filtered);

    }

}

谓词逻辑的复用

   public static Predicate<Employee> ageGreaterThan70 = x -> x.getAge() >70;
   public static Predicate<Employee> genderM = x -> x.getGender().equals("M");

1.and语法（交集）

List<Employee> filtered = employees.stream()
        .filter(Employee.ageGreaterThan70.and(Employee.genderM))
        .collect(Collectors.toList());

2.or语法（并集）

List<Employee> filtered = employees.stream()
        .filter(Employee.ageGreaterThan70.or(Employee.genderM))
        .collect(Collectors.toList());

3.negate语法（取反）

List<Employee> filtered = employees.stream()
        .filter(Employee.ageGreaterThan70.or(Employee.genderM).negate())
        .collect(Collectors.toList());

七.map操作

1.基础用法

将集合中的每一个字符串，全部转换成大写！

List<String> alpha = Arrays.asList("Monkey", "Lion", "Giraffe", "Lemur");

//不使用Stream管道流
List<String> alphaUpper = new ArrayList<>();
for (String s : alpha) {
    alphaUpper.add(s.toUpperCase());
}
System.out.println(alphaUpper); //[MONKEY, LION, GIRAFFE, LEMUR]

// 使用Stream管道流
List<String> collect = alpha.stream().map(String::toUpperCase).collect(Collectors.toList());
//上面使用了方法引用，和下面的lambda表达式语法效果是一样的
//List<String> collect = alpha.stream().map(s -> s.toUpperCase()).collect(Collectors.toList());

System.out.println(collect); //[MONKEY, LION, GIRAFFE, LEMUR]

2、处理非字符串类型集合元素

map()函数不仅可以处理数据，还可以转换数据的类型

List<Integer> lengths = alpha.stream()
        .map(String::length)
        .collect(Collectors.toList());

System.out.println(lengths); //[6, 4, 7, 5]

Stream.of("Monkey", "Lion", "Giraffe", "Lemur")
        .mapToInt(String::length)
        .forEach(System.out::println);

3、处理对象数据格式转换

将每一个Employee的年龄增加一岁
将性别中的“M”换成“male”，F换成Female。

public static void main(String[] args){
    Employee e1 = new Employee(1,23,"M","Rick","Beethovan");
    Employee e2 = new Employee(2,13,"F","Martina","Hengis");
    Employee e3 = new Employee(3,43,"M","Ricky","Martin");
    Employee e4 = new Employee(4,26,"M","Jon","Lowman");
    Employee e5 = new Employee(5,19,"F","Cristine","Maria");
    Employee e6 = new Employee(6,15,"M","David","Feezor");
    Employee e7 = new Employee(7,68,"F","Melissa","Roy");
    Employee e8 = new Employee(8,79,"M","Alex","Gussin");
    Employee e9 = new Employee(9,15,"F","Neetu","Singh");
    Employee e10 = new Employee(10,45,"M","Naveen","Jain");


    List<Employee> employees = Arrays.asList(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10);

    /*List<Employee> maped = employees.stream()
            .map(e -> {
                e.setAge(e.getAge() + 1);
                e.setGender(e.getGender().equals("M")?"male":"female");
                return e;
            }).collect(Collectors.toList());*/

    List<Employee> maped = employees.stream()
            .peek(e -> {
                e.setAge(e.getAge() + 1);
                e.setGender(e.getGender().equals("M")?"male":"female");
            }).collect(Collectors.toList());

    System.out.println(maped);

}

由于map的参数e就是返回值，所以可以用peek函数。peek函数是一种特殊的map函数，当函数没有返回值或者参数就是返回值的时候可以使用peek函数。

4、flatMap

map可以对管道流中的数据进行转换操作，但是如果管道中还有管道该如何处理？即：如何处理二维数组及二维集合类。实现一个简单的需求：将“hello”，“world”两个字符串组成的集合，元素的每一个字母打印出来。如果不用Stream我们怎么写？写2层for循环,第一层遍历字符串，并且将字符串拆分成char数组，第二层for循环遍历char数组。

List<String> words = Arrays.asList("hello", "word");
words.stream()
        .map(w -> Arrays.stream(w.split("")))    //[[h,e,l,l,o],[w,o,r,l,d]]
        .forEach(System.out::println);

输出打印结果：

java.util.stream.ReferencePipeline$Head@3551a94
java.util.stream.ReferencePipeline$Head@531be3c5

flatMap可以理解为将若干个子管道中的数据全都，平面展开到父管道中进行处理。

words.stream()
        .flatMap(w -> Arrays.stream(w.split(""))) // [h,e,l,l,o,w,o,r,l,d]
        .forEach(System.out::println);

输出打印结果：

h
e
l
l
o
w
o
r
d

5.对象的操作

  class  Test{
   public static void tredst1(Trader trader){
       System.out.println("************************");
      System.out.println(trader);
   }

   public static void main(String[] args) {
      Trader raoul = new Trader("Raoul", "Cambridge");
      Trader mario = new Trader("Mario","Milan");
      Trader alan = new Trader("Alan","Cambridge");
      Trader brian = new Trader("Brian","Cambridge");


     Arrays.asList(Arrays.asList(raoul,mario,alan,brian))
             .stream()
             .flatMap(e->e.stream())
             .forEach(item->{
                 tredst1(item);
             });



   }
}

八.Stream的状态与并行操作

通过前面章节的学习，我们应该明白了Stream管道流的基本操作。我们来回顾一下：

源操作：可以将数组、集合类、行文本文件转换成管道流Stream进行数据处理
中间操作：对Stream流中的数据进行处理，比如：过滤、数据转换等等
终端操作：作用就是将Stream管道流转换为其他的数据类型。这部分我们还没有讲，我们后面章节再介绍。

看下面的脑图，可以有更清晰的理解：

1.Limit与Skip管道数据截取

List<String> limitN = Stream.of("Monkey", "Lion", "Giraffe", "Lemur")
        .limit(2)
        .collect(Collectors.toList());
List<String> skipN = Stream.of("Monkey", "Lion", "Giraffe", "Lemur")
        .skip(2)
        .collect(Collectors.toList());

limt方法传入一个整数n，用于截取管道中的前n个元素。经过管道处理之后的数据是：[Monkey, Lion]。
skip方法与limit方法的使用相反，用于跳过前n个元素，截取从n到末尾的元素。经过管道处理之后的数据是： [Giraffe, Lemur]

2、Distinct元素去重

List<String> uniqueAnimals = Stream.of("Monkey", "Lion", "Giraffe", "Lemur", "Lion")
        .distinct()
        .collect(Collectors.toList());

3、Sorted排序

List<String> alphabeticOrder = Stream.of("Monkey", "Lion", "Giraffe", "Lemur")
        .sorted()
        .collect(Collectors.toList());

九.像使用SQL一样排序集合

1、字符串List排序

List<String> cities = Arrays.asList(
        "Milan",
        "london",
        "San Francisco",
        "Tokyo",
        "New Delhi"
);
System.out.println(cities);
//[Milan, london, San Francisco, Tokyo, New Delhi]

cities.sort(String.CASE_INSENSITIVE_ORDER);
System.out.println(cities);
//[london, Milan, New Delhi, San Francisco, Tokyo]

cities.sort(Comparator.naturalOrder());
System.out.println(cities);
//[Milan, New Delhi, San Francisco, Tokyo, london]

当使用sort方法，按照String.CASE_INSENSITIVE_ORDER（字母大小写不敏感）的规则排序，结果是：[london, Milan, New Delhi, San Francisco, Tokyo]
如果使用Comparator.naturalOrder()字母自然顺序排序，结果是：[Milan, New Delhi, San Francisco, Tokyo, london]

同样我们可以把排序器Comparator用在Stream管道流中。

cities.stream().sorted(Comparator.naturalOrder()).forEach(System.out::println);

//Milan
//New Delhi
//San Francisco
//Tokyo
//london

在java 7我们是使用Collections.sort()接受一个数组参数，对数组进行排序。在java 8之后可以直接调用集合类的sort()方法进行排序。sort()方法的参数是一个比较器Comparator接口的实现类，Comparator接口的我们下一节再给大家介绍一下。

2、整数类型List排序

List<Integer> numbers = Arrays.asList(6, 2, 1, 4, 9);
System.out.println(numbers); //[6, 2, 1, 4, 9]

numbers.sort(Comparator.naturalOrder());  //自然排序
System.out.println(numbers); //[1, 2, 4, 6, 9]

numbers.sort(Comparator.reverseOrder()); //倒序排序
System.out.println(numbers);  //[9, 6, 4, 2, 1]

3、按对象字段对`List<Object>`排序

Employee e1 = new Employee(1,23,"M","Rick","Beethovan");
Employee e2 = new Employee(2,13,"F","Martina","Hengis");
Employee e3 = new Employee(3,43,"M","Ricky","Martin");
Employee e4 = new Employee(4,26,"M","Jon","Lowman");
Employee e5 = new Employee(5,19,"F","Cristine","Maria");
Employee e6 = new Employee(6,15,"M","David","Feezor");
Employee e7 = new Employee(7,68,"F","Melissa","Roy");
Employee e8 = new Employee(8,79,"M","Alex","Gussin");
Employee e9 = new Employee(9,15,"F","Neetu","Singh");
Employee e10 = new Employee(10,45,"M","Naveen","Jain");


List<Employee> employees = Arrays.asList(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10);

employees.sort(Comparator.comparing(Employee::getAge));
employees.forEach(System.out::println);

首先，我们创建了10个Employee对象，然后将它们转换为List
然后重点的的代码:使用了函数应用Employee::getAge作为对象的排序字段，即使用员工的年龄作为排序字段
然后调用List的forEach方法将List排序结果打印出来，如下(当然我们重写了Employee的toString方法，不然打印结果没有意义)：

Employee(id=2, age=13, gender=F, firstName=Martina, lastName=Hengis)
Employee(id=6, age=15, gender=M, firstName=David, lastName=Feezor)
Employee(id=9, age=15, gender=F, firstName=Neetu, lastName=Singh)
Employee(id=5, age=19, gender=F, firstName=Cristine, lastName=Maria)
Employee(id=1, age=23, gender=M, firstName=Rick, lastName=Beethovan)
Employee(id=4, age=26, gender=M, firstName=Jon, lastName=Lowman)
Employee(id=3, age=43, gender=M, firstName=Ricky, lastName=Martin)
Employee(id=10, age=45, gender=M, firstName=Naveen, lastName=Jain)
Employee(id=7, age=68, gender=F, firstName=Melissa, lastName=Roy)
Employee(id=8, age=79, gender=M, firstName=Alex, lastName=Gussin)

如果我们希望List按照年龄age的倒序排序，就使用reversed()方法。如：

employees.sort(Comparator.comparing(Employee::getAge).reversed());

4、Comparator链对`List<Object>`排序

下面这段代码先是按性别的倒序排序，再按照年龄的倒序排序

employees.sort(
        Comparator.comparing(Employee::getGender)
        .thenComparing(Employee::getAge)
        .reversed()
);
employees.forEach(System.out::println);

//都是正序 ，不加reversed
//都是倒序，最后面加一个reserved
//先是倒序（加reserved），然后正序
//先是正序（加reserved），然后倒序（加reserved）

5、自定义Comparator排序

我们自定义一个排序器，实现compare函数（函数式接口Comparator唯一的抽象方法）。返回0表示元素相等，-1表示前一个元素小于后一个元素，1表示前一个元素大于后一个元素。这个规则和java 8之前没什么区别。

下面代码用自定义接口实现类的的方式实现：按照年龄的倒序排序！

employees.sort(new Comparator<Employee>() {
    @Override
    public int compare(Employee em1, Employee em2) {
        if(em1.getAge() == em2.getAge()){
            return 0;
        }
        return em1.getAge() - em2.getAge() > 0 ? -1:1;
    }
});
employees.forEach(System.out::println);

最终的打印结果如下，按照年龄的自定义规则进行排序。

Employee(id=8, age=79, gender=M, firstName=Alex, lastName=Gussin)
Employee(id=7, age=68, gender=F, firstName=Melissa, lastName=Roy)
Employee(id=10, age=45, gender=M, firstName=Naveen, lastName=Jain)
Employee(id=3, age=43, gender=M, firstName=Ricky, lastName=Martin)
Employee(id=4, age=26, gender=M, firstName=Jon, lastName=Lowman)
Employee(id=1, age=23, gender=M, firstName=Rick, lastName=Beethovan)
Employee(id=5, age=19, gender=F, firstName=Cristine, lastName=Maria)
Employee(id=9, age=15, gender=F, firstName=Neetu, lastName=Singh)
Employee(id=6, age=15, gender=M, firstName=David, lastName=Feezor)
Employee(id=2, age=13, gender=F, firstName=Martina, lastName=Hengis)

这段代码如果以lambda表达式简写。箭头左侧是参数，右侧是函数体，参数类型和返回值根据上下文自动判断。如下：

employees.sort((em1,em2) -> {
    if(em1.getAge() == em2.getAge()){
        return 0;
    }
    return em1.getAge() - em2.getAge() > 0 ? -1:1;
});
employees.forEach(System.out::println);

5、求最大值和最小值

//         找出最大值
        int[] array=new int[]{123,43,2453,3,532,5,324,123,21,412,3,123,2154,35,123241,125443};

        int max = Arrays.stream(array).max().getAsInt();
        System.out.println(max);

        //最小值
        int min = Arrays.stream(array).min().getAsInt();
        System.out.println(min);

5、List集合中最大值和最小值

List<Integer> integers = Arrays.asList(1, 1, 23, 43, 4, 4, 21, 3, 21, 321, 3, 214, 12, 34, 13, 21, 4, 214);
        List<Integer> collect = integers.stream().distinct().collect(Collectors.toList());
        System.out.println(collect);
        //方式1
        System.out.println(collect.stream().mapToInt(e->e).max().getAsInt());
        //方式2
        System.out.println(collect.stream().max(Integer::compare).get());

十.Stream查找与匹配元素

1、对比一下有多简单

employees是10个员工对象组成的List，在前面的章节中我们已经用过多次，这里不再列出代码。

如果我们不用Stream API实现，查找员工列表中是否包含年龄大于70的员工？代码如下：

boolean isExistAgeThan70 = false;
for(Employee employee:employees){
  if(employee.getAge() > 70){
    isExistAgeThan70 = true;
    break;
  }
}

如果我们使用Stream API就是下面的一行代码，其中使用到了我们之前学过的"谓词逻辑"。

boolean isExistAgeThan70 = employees.stream().anyMatch(Employee.ageGreaterThan70);

将谓词逻辑换成lambda表达式也可以，代码如下：

boolean isExistAgeThan72 = employees.stream().anyMatch(e -> e.getAge() > 72);

所以，我们介绍了第一个匹配规则函数：anyMatch，判断Stream流中是否包含某一个“匹配规则”的元素。这个匹配规则可以是lambda表达式或者谓词。

2、其他匹配规则函数介绍

是否所有员工的年龄都大于10岁？allMatch匹配规则函数：判断是够Stream流中的所有元素都符合某一个"匹配规则"。

boolean isExistAgeThan10 = employees.stream().allMatch(e -> e.getAge() > 10);

是否不存在小于18岁的员工？noneMatch匹配规则函数：判断是否Stream流中的所有元素都不符合某一个"匹配规则"。

boolean isExistAgeLess18 = employees.stream().noneMatch(e -> e.getAge() < 18);

3、元素查找与Optional

从列表中按照顺序查找第一个年龄大于40的员工。

Optional<Employee> employeeOptional
        =  employees.stream().filter(e -> e.getAge() > 40).findFirst();
System.out.println(employeeOptional.get());

打印结果

Employee(id=3, age=43, gender=M, firstName=Ricky, lastName=Martin)

Optional类代表一个值存在或者不存在。在java8中引入，这样就不用返回null了。

isPresent() 将在 Optional 包含值的时候返回 true , 否则返回 false 。
ifPresent(Consumer block) 会在值存在的时候执行给定的代码块。我们在第3章
介绍了 Consumer 函数式接口；它让你传递一个接收 T 类型参数，并返回 void 的Lambda
表达式。
T get() 会在值存在时返回值，否则?出一个 NoSuchElement 异常。
T orElse(T other) 会在值存在时返回值，否则返回一个默认值。
findFirst用于查找第一个符合“匹配规则”的元素，返回值为Optional
findAny用于查找任意一个符合“匹配规则”的元素，返回值为Optional

十一.Stream集合元素归约

1.Integer类型归约

reduce初始值为0，累加器可以是lambda表达式，也可以是方法引用。

List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 6);
int result = numbers
        .stream()
        .reduce(0, (subtotal, element) -> subtotal + element);
System.out.println(result);  //21

int result = numbers
        .stream()
        .reduce(0, Integer::sum);
System.out.println(result); //21

2.String类型归约

不仅可以归约Integer类型，只要累加器参数类型能够匹配，可以对任何类型的集合进行归约计算。

List<String> letters = Arrays.asList("a", "b", "c", "d", "e");
String result = letters
        .stream()
        .reduce("", (partialString, element) -> partialString + element);
System.out.println(result);  //abcde


String result = letters
        .stream()
        .reduce("", String::concat);
System.out.println(result);  //ancde

3.复杂对象归约

计算所有的员工的年龄总和。

Employee e1 = new Employee(1,23,"M","Rick","Beethovan");
Employee e2 = new Employee(2,13,"F","Martina","Hengis");
Employee e3 = new Employee(3,43,"M","Ricky","Martin");
Employee e4 = new Employee(4,26,"M","Jon","Lowman");
Employee e5 = new Employee(5,19,"F","Cristine","Maria");
Employee e6 = new Employee(6,15,"M","David","Feezor");
Employee e7 = new Employee(7,68,"F","Melissa","Roy");
Employee e8 = new Employee(8,79,"M","Alex","Gussin");
Employee e9 = new Employee(9,15,"F","Neetu","Singh");
Employee e10 = new Employee(10,45,"M","Naveen","Jain");


List<Employee> employees = Arrays.asList(e1, e2, e3, e4, e5, e6, e7, e8, e9, e10);


Integer total = employees.stream().map(Employee::getAge).reduce(0,Integer::sum);
System.out.println(total); //346

先用map将Stream流中的元素由Employee类型处理为Integer类型（age）。
然后对Stream流中的Integer类型进行归约

十二.练习题

 public static void main(String[] args) {
        Trader raoul = new Trader("Raoul", "Cambridge");
        Trader mario = new Trader("Mario","Milan");
        Trader alan = new Trader("Alan","Cambridge");
        Trader brian = new Trader("Brian","Cambridge");
        List<Transaction> transactions = Arrays.asList(
                new Transaction(brian, 2011, 300),
                new Transaction(raoul, 2012, 1000),
                new Transaction(raoul, 2011, 400),
                new Transaction(mario, 2012, 710),
                new Transaction(mario, 2012, 700),
                new Transaction(alan, 2012, 950)
        );
        Stream<Transaction> stream = transactions.stream();
        //(1) 找出2011年发生的所有交易，并按交易额排序（从低到高）。
        Predicate<Transaction> year=e->e.getYear()==2011;
        stream
                .filter(year)
                .sorted(Comparator.comparing(e->e.getValue()))
                .collect(Collectors.toList()).forEach(System.out::println);
   //(2) 交易员都在哪些不同的城市工作过？
        transactions.stream().map(e->e.getTrader().getCity()).distinct().collect(Collectors.toList()).forEach(System.out::println);
        System.out.println("---------------------------------2");
   //(3) 查找所有来自于剑桥的交易员，并按姓名排序。
    transactions.stream()
            .filter(e->e.getTrader().getCity().equals("Cambridge"))
            .sorted(Comparator.comparing(e->e.getTrader().getName()))
            .map(e->e.getTrader().getName())
            .distinct()
            .collect(Collectors.toList())
            .forEach(System.out::println);
        System.out.println("---------------------------------3");
   //(4) 返回所有交易员的姓名字符串，按字母顺序排序。
    transactions.stream()
            .map(e->e.getTrader().getName())
            .sorted(Comparator.naturalOrder())
            .distinct()
            .collect(Collectors.toList())
            .forEach(System.out::println);
        System.out.println("---------------------------------4");
        String reduce = transactions.stream()
                .map(e -> e.getTrader().getName())
                .distinct()
                .sorted()
                .reduce("", (n1, n2) -> n1 + n2);
        System.out.println(reduce);
        System.out.println("------------------------------------4");
//        (5) 有没有交易员是在米兰工作的？
        boolean milan = transactions.stream()
                .anyMatch(e -> e.getTrader().getCity().equals("Milan"));
        System.out.println(milan);
        System.out.println("_-----------------------------------5");
  //(6) 打印生活在剑桥的交易员的所有交易额。
        transactions.stream()
                .filter(e->e.getTrader().getCity().equals("Cambridge"))
                .map(Transaction::getValue)
        .collect(Collectors.toList())
        .forEach(System.out::println);
        System.out.println("_------------------------------------6");
        //(7) 所有交易中，最高的交易额是多少？
        Integer integer = transactions.stream()
                .map(Transaction::getValue)
                .max(Integer::compareTo).get();
        System.out.println(integer);
        System.out.println("_------------------------------------8");
    //(8) 找到交易额最小的交易。
        Integer integer1 = transactions.stream()
                .map(Transaction::getValue)
                .min(Integer::compareTo).get();
        System.out.println(integer1);


    }