Java 8 Stream API学习总结

2022-04-22 00:07:41

Java 8 API添加了一个新的抽象称为流Stream，可以让你以一种声明的方式处理数据。Stream API可以极大提高Java程序员的生产力，让程序员写出高效率、干净、简洁的代码。这种风格将要处理的元素集合看作一种流，流在管道中传输，并且可以在管道的节点上进行处理，比如筛选，排序，聚合等。元素流在管道中经过中间操作（intermediate operation）的处理，最后由最终操作(terminal operation)得到前面处理的结果。

这一次为什么要系统性的总结一下 Java 8 Stream API 呢？说得简单点，我们先不论性能，我们就是为了 装x ，而且要让这个 x 装得再优秀一些，仅此而已！

Stream基础知识

流程

创建流 → 流的中间操作 → 流的最终操作

创建流

我们需要把哪些元素放入流中，常见的api有：

// 使用List创建流
list.stream()

// 使用一个或多个元素创建流
Stream.of(T value)
Stream.of(T... values)

// 使用数组创建流
Arrays.stream(T[] array)

// 创建一个空流
Stream.empty()

// 两个流合并
Stream.concat(Stream<? extends T> a, Stream<? extends T> b)

// 无序无限流
Stream.generate(Supplier<T> s)

// 通过迭代产生无限流
Stream.iterate(final T seed, final UnaryOperator<T> f)

流的中间操作

// 元素过滤
filter
limit
skip
distinct

// 映射
map
flatmap

// 排序

流的最终操作

通过流对元素的最终操作，我们想得到一个什么样的结果

构造测试数据

员工实体类

/**
 * 员工实体类
 * @author Erwin Feng
 * @since 2020/4/27 2:10
 */
public class Employee {

    /** 员工ID */
    private Integer id;

    /** 员工姓名 */
    private String name;

    /** 员工薪资 */
    private Double salary;
    
    /** 构造方法、getter and setter、toString */
}

测试数据列表

[
    {
        "id":1,
        "name":"Jacob",
        "salary":1000
    },
    {
        "id":2,
        "name":"Sophia",
        "salary":2000
    },
    {
        "id":3,
        "name":"Rose",
        "salary":3000
    },
    {
        "id":4,
        "name":"Lily",
        "salary":4000
    },
    {
        "id":5,
        "name":"Daisy",
        "salary":5000
    },
    {
        "id":6,
        "name":"Jane",
        "salary":5000
    },
    {
        "id":7,
        "name":"Jasmine",
        "salary":6000
    },
    {
        "id":8,
        "name":"Jack",
        "salary":6000
    },
    {
        "id":9,
        "name":"Poppy",
        "salary":7000
    }
]

Stream API Test

filter 过滤

需求：查找薪酬为5000的员工列表

List<Employee> employees = list.stream().filter(employee -> employee.getSalary() == 5000)
        .peek(System.out::println)
        .collect(Collectors.toList());
Assert.assertEquals(2, employees.size());

map 映射

需求：将薪酬大于5000的员工放到Leader对象中

List<Leader> leaders = list.stream().filter(employee -> employee.getSalary() > 5000).map(employee -> {
    Leader leader = new Leader();
    leader.setName(employee.getName());
    leader.setSalary(employee.getSalary());
    return leader;
}).peek(System.out::println).collect(Collectors.toList());
Assert.assertEquals(3, leaders.size());

flatMap 水平映射

需求：将多维的列表转化为单维的列表

说明：

我们将薪酬在1000-3000的分为一个列表，4000-5000分为一个列表，6000-7000分为一个列表。

将这三个列表组合在一起形成一个多维列表。

List<Employee> employees = multidimensionalList.stream().flatMap(Collection::stream).collect(Collectors.toList());
Assert.assertEquals(9, employees.size());

sorted 排序

需求：根据薪酬排序

// 薪酬从小到大排序
List<Employee> employees = list.stream().sorted(Comparator.comparing(Employee::getSalary)).peek(System.out::println).collect(Collectors.toList());

// 薪酬从大到小排序
List<Employee> employees2 = list.stream().sorted(Comparator.comparing(Employee::getSalary).reversed()).peek(System.out::println).collect(Collectors.toList());

min 最小值

double minValue = list.stream().mapToDouble(Employee::getSalary).min().orElse(0);
Assert.assertEquals(1000, minValue, 0.0);

Employee employee = list.stream().min(Comparator.comparing(Employee::getSalary)).orElse(null);
assert employee != null;
Assert.assertEquals(employee.getSalary(), minValue, 0.0);

max 最大值

double maxValue = list.stream().mapToDouble(Employee::getSalary).max().orElse(0);
Assert.assertEquals(7000, maxValue, 0.0);

average 平均值

double sum = list.stream().mapToDouble(Employee::getSalary).sum();
double averageValue = list.stream().mapToDouble(Employee::getSalary).average().orElse(0);
Assert.assertEquals(sum / list.size(), averageValue, 0.0);

match 匹配

// allMatch 集合中的元素都要满足条件才会返回true
// 薪酬都是大于等于1000的
boolean isAllMatch = list.stream().allMatch(employee -> employee.getSalary() >= 1000);
Assert.assertTrue(isAllMatch);

// anyMatch 集合中只要有一个元素满足条件就会返回true
// 有没有薪酬大于等于7000
boolean isAnyMatch = list.stream().anyMatch(employee -> employee.getSalary() >= 7000);
Assert.assertTrue(isAnyMatch);

// noneMatch 集合中没有元素满足条件才会返回true
// 没有薪酬小于1000的
boolean isNoneMatch = list.stream().noneMatch(employee -> employee.getSalary() < 1000);
Assert.assertTrue(isNoneMatch);

distinct 去重

默认的 distinct() 不接收参数，是根据 Object#equals(Object) 去重。根据API介绍，这是一个有中间状态的操作。

List<Employee> employees = list.stream().distinct().collect(Collectors.toList());
Assert.assertEquals(9, employees.size());

如果我们要根据对象中的某个属性去重的，可以使用 StreamEx

// 使用StreamEx去重
List<Employee> employees2 = StreamEx.of(list).distinct(Employee::getSalary).collect(Collectors.toList());
Assert.assertEquals(7, employees2.size());

当然也可以使用JDK Stream API

private static <T>Predicate<T> distinctByKey(Function<? super T, ?> keyExtractor) {
    Map<Object, Boolean> result = new ConcurrentHashMap<>();
    return t -> result.putIfAbsent(keyExtractor.apply(t), Boolean.TRUE) == null;
}

List<Employee> employees3 = list.stream().filter(distinctByKey(Employee::getSalary)).collect(Collectors.toList());
Assert.assertEquals(7, employees3.size());

reduce 裁减

需求：计算薪酬总和

// 先将员工列表转换为薪酬列表
// 再计算薪酬总和
double salarySum = list.stream().map(Employee::getSalary).reduce(Double::sum).orElse(0.0);
double sum = list.stream().mapToDouble(Employee::getSalary).sum();
Assert.assertEquals(salarySum, sum, 0.0);

另外，我们也可以设定一个累加函数的标识值

double salarySum5 = list.stream().map(Employee::getSalary).reduce(1.00, Double::sum);
Assert.assertEquals(salarySum5, sum + 1, 0.0);

collector 流的终止结果

// joining 拼接字符串
String employeeNames = list.stream().map(Employee::getName).collect(Collectors.joining(", "));
System.out.println(employeeNames); // Jacob, Sophia, Rose, Lily, Daisy, Jane, Jasmine, Jack, Poppy

// 返回一个List
List<String> employeeNameList = list.stream().map(Employee::getName).collect(Collectors.toList());
System.out.println(employeeNameList);

// 返回一个Set
Set<String> employeeNameSet = list.stream().map(Employee::getName).collect(Collectors.toSet());
System.out.println(employeeNameSet);

// 返回一个Vector
Vector<String> employeeNameVector = list.stream().map(Employee::getName).collect(Collectors.toCollection(Vector::new));
System.out.println(employeeNameVector);

// 返回一个Map
Map<Integer, String> employeesMap = list.stream().collect(Collectors.toMap(Employee::getId, Employee::getName));
System.out.println(employeesMap);

count 统计

需求：薪酬为5000的员工数

不使用流

int count2 = 0;
for (Employee employee : list) {
    if (employee.getSalary() == 5000) {
        count2++;
    }
}
System.out.println(count2);

使用流

long count3 = list.stream().filter(employee -> employee.getSalary() == 5000).count();
Assert.assertEquals(count3, count2);

summarizingDouble 统计分析

DoubleSummaryStatistics employeeSalaryStatistics = list.stream().collect(Collectors.summarizingDouble(Employee::getSalary));
System.out.println("employee salary statistics:" + employeeSalaryStatistics);

DoubleSummaryStatistics employeeSalaryStatistics2 = list.stream().mapToDouble(Employee::getSalary).summaryStatistics();
System.out.println("employee salary statistics2:" + employeeSalaryStatistics2);

{count=9, sum=39000.000000, min=1000.000000, average=4333.333333, max=7000.000000}

partitioningBy 分区

分成满足条件（true）和不满足条件（false）两个区

需求：找出薪酬大于5000的员工

Map<Boolean, List<Employee>> map = list.stream().collect(Collectors.partitioningBy(employee -> employee.getSalary() > 5000));
System.out.println("true:" + map.get(Boolean.TRUE));
System.out.println("false:" + map.get(Boolean.FALSE));

true:[Employee{id=7, name='Jasmine', salary=6000.0}, Employee{id=8, name='Jack', salary=6000.0}, Employee{id=9, name='Poppy', salary=7000.0}]

false:[Employee{id=1, name='Jacob', salary=1000.0}, Employee{id=2, name='Sophia', salary=2000.0}, Employee{id=3, name='Rose', salary=3000.0}, Employee{id=4, name='Lily', salary=4000.0}, Employee{id=5, name='Daisy', salary=5000.0}, Employee{id=6, name='Jane', salary=5000.0}]

groupingBy 分组

需求：根据员工薪酬分组

Map<Double, List<Employee>> map = list.stream().collect(Collectors.groupingBy(Employee::getSalary));
System.out.println(map);

再举一个例子：薪酬一> 总和（薪酬*员工数）

Map<Double, Double> map3 = list.stream().collect(Collectors.groupingBy(Employee::getSalary, Collectors.summingDouble(Employee::getSalary)));
System.out.println(map3);

parallel 平行计算

简单的说，就是启动多个线程计算

private static void cal(Employee employee) {
    try {
        long sleepTime = employee.getSalary().longValue();
        TimeUnit.MILLISECONDS.sleep(sleepTime);
        logger.info("employee name: {}", employee.getName());
    } catch (InterruptedException e) {
        e.printStackTrace();
    }
}

list.stream().parallel().forEach(StreamTest::cal);

2020-05-15 01:47:14.231 [ForkJoinPool.commonPool-worker-4] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Jacob
2020-05-15 01:47:15.226 [ForkJoinPool.commonPool-worker-2] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Sophia
2020-05-15 01:47:16.226 [ForkJoinPool.commonPool-worker-1] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Rose
2020-05-15 01:47:17.226 [ForkJoinPool.commonPool-worker-3] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Lily
2020-05-15 01:47:18.225 [main] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Jane
2020-05-15 01:47:18.228 [ForkJoinPool.commonPool-worker-7] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Daisy
2020-05-15 01:47:19.226 [ForkJoinPool.commonPool-worker-5] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Jack
2020-05-15 01:47:19.228 [ForkJoinPool.commonPool-worker-6] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Jasmine
2020-05-15 01:47:21.234 [ForkJoinPool.commonPool-worker-4] INFO  com.fengwenyi.study_stream.StreamTest - employee name: Poppy

file 文件操作

try (PrintWriter printWriter = new PrintWriter(Files.newBufferedWriter(Paths.get(tempFilePath)))) { // 使用 try 自动关闭流
    list.forEach(printWriter::println);
    list.forEach(employee -> printWriter.println(employee.getName())); // 将员工的姓名写到文件中
}

// 从文件中读取员工的姓名
List<String> s = Files.lines(Paths.get(tempFilePath)).peek(System.out::println).collect(Collectors.toList());

测试代码

Study Java 8 Stream API

码农公寓

Java 8 Stream API学习总结

Stream基础知识

流程

创建流

流的中间操作

流的最终操作

构造测试数据

员工实体类

测试数据列表

Stream API Test

filter 过滤

map 映射

flatMap 水平映射

sorted 排序

min 最小值

max 最大值

average 平均值

match 匹配

distinct 去重

reduce 裁减

collector 流的终止结果

count 统计

summarizingDouble 统计分析

partitioningBy 分区

groupingBy 分组

parallel 平行计算

file 文件操作

测试代码

学习链接

码农公寓

Stream基础知识

流程

创建流

流的中间操作

流的最终操作

构造测试数据

员工实体类

测试数据列表

Stream API Test

filter 过滤

map 映射

flatMap 水平映射

sorted 排序

min 最小值

max 最大值

average 平均值

match 匹配

distinct 去重

reduce 裁减

collector 流的终止结果

count 统计

summarizingDouble 统计分析

partitioningBy 分区

groupingBy 分组

parallel 平行计算

file 文件操作

测试代码

学习链接

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