阅读完需：约 19 分钟
Stream 是 Java8 中处理集合的关键抽象概念，它可以指定你希望对集合进行的操作，可以执行非常复杂的查找、过滤和映射数据等操作。使用Stream API 对集合数据进行操作，就类似于使用 SQL 执行的数据库查询。也可以使用 Stream API 来并行执行操作。简而言之，Stream API 提供了一种高效且易于使用的处理数据的方式。
特点：
1 . 不是数据结构，不会保存数据。
2. 不会修改原来的数据源，它会将操作后的数据保存到另外一个对象中。（保留意见：毕竟peek方法可以修改流中元素）
3. 惰性求值，流在中间处理过程中，只是对操作进行了记录，并不会立即执行，需要等到执行终止操作的时候才会进行实际的计算。
- 无状态：指元素的处理不受之前元素的影响；
- 有状态：指该操作只有拿到所有元素之后才能继续下去。
- 非短路操作：指必须处理所有元素才能得到最终结果；
- 短路操作：指遇到某些符合条件的元素就可以得到最终结果，如 A || B，只要A为true，则无需判断B的结果。
具体用法（创建流）
1. 使用Collection下的 stream() 和 parallelStream() 方法
List<String> list = new ArrayList<>();
Stream<String> stream = list.stream(); //获取一个顺序流
Stream<String> parallelStream = list.parallelStream(); //获取一个并行流2. 使用Arrays 中的 stream() 方法，将数组转成流
Integer[] nums = new Integer[10];
Stream<Integer> stream = Arrays.stream(nums);3. 使用Stream中的静态方法：of()、iterate()、generate()
Stream<Integer> stream = Stream.of(1,2,3,4,5,6);
Stream<Integer> stream2 = Stream.iterate(0, (x) -> x + 2).limit(6);
stream2.forEach(System.out::println); // 0 2 4 6 8 10
Stream<Double> stream3 = Stream.generate(Math::random).limit(2);
stream3.forEach(System.out::println);4. 使用 BufferedReader.lines() 方法，将每行内容转成流
BufferedReader reader = new BufferedReader(new FileReader("F:\\test_stream.txt"));
Stream<String> lineStream = reader.lines();
lineStream.forEach(System.out::println);5. 使用 Pattern.splitAsStream() 方法，将字符串分隔成流
Pattern pattern = Pattern.compile(",");
Stream<String> stringStream = pattern.splitAsStream("a,b,c,d");
stringStream.forEach(System.out::println);流的中间操作
1.筛选与切片
filter：过滤流中的某些元素
limit(n)：获取n个元素
skip(n)：跳过n元素，配合limit(n)可实现分页
distinct：通过流中元素的 hashCode() 和 equals() 去除重复元素
Stream<Integer> stream = Stream.of(6, 4, 6, 7, 3, 9, 8, 10, 12, 14, 14);
Stream<Integer> newStream = stream.filter(s -> s > 5) //6 6 7 9 8 10 12 14 14
.distinct() //6 7 9 8 10 12 14
.skip(2) //9 8 10 12 14
.limit(2); //9 8
newStream.forEach(System.out::println);2. 映射
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。
List<String> list = Arrays.asList("a,b,c", "1,2,3");
//将每个元素转成一个新的且不带逗号的元素
Stream<String> s1 = list.stream().map(s -> s.replaceAll(",", ""));
s1.forEach(System.out::println); // abc 123
Stream<String> s3 = 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;
});
s3.forEach(System.out::println); // a b c 1 2 33. 排序
sorted()：自然排序，流中元素需实现Comparable接口
sorted(Comparator com)：定制排序，自定义Comparator排序器
List<String> list = Arrays.asList("aa", "ff", "dd");
//String 类自身已实现Compareable接口
list.stream().sorted().forEach(System.out::println);// aa dd ff
Student s1 = new Student("aa", 10);
Student s2 = new Student("bb", 20);
Student s3 = new Student("aa", 30);
Student s4 = new Student("dd", 40);
List<Student> studentList = Arrays.asList(s1, s2, s3, s4);
//自定义排序：先按姓名升序，姓名相同则按年龄升序
studentList.stream().sorted(
(o1, o2) -> {
if (o1.getName().equals(o2.getName())) {
return o1.getAge() - o2.getAge();
} else {
return o1.getName().compareTo(o2.getName());
}
}
).forEach(System.out::println);sort 与 sorted 区别
sort()与sorted()的不同在于，sort排序不产生新的列表，而sorted()是产生一个新的列表。
sort 是应用在 list 上的方法，sorted 可以对所有可迭代的对象进行排序操作。
stream 排序
// 实体类
@Data
@Builder
class Person {
private String name;
private Integer age;
private BigDecimal salary;
@JSONField(format = "yyyy-MM-dd HH:mm:ss")
private Date birthday;
}
// 测试类
public class Java8StreamTest {
/**
* 常用排序手段： 根据date排序， 根据number排序， 根据money排序
*/
public static void testStream() throws ParseException {
LocalDateTime ldt = LocalDateTime.now();
LocalDateTime with = ldt.with(TemporalAdjusters.lastDayOfMonth());
Date date = Date.from(with.atZone(ZoneId.systemDefault()).toInstant());
LocalDateTime parse = LocalDateTime.parse("2021-01-02 21:21:21", DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss"));
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date1 = sdf.parse("2021-01-02 21:32:32");
Date date2 = sdf.parse("2021-07-05 12:32:45");
Date date3 = sdf.parse("2020-09-18 23:21:21");
Date date4 = sdf.parse("2018-05-02 19:23:01");
Date date5 = sdf.parse("2019-11-25 03:54:12");
List<Person> personList = new ArrayList<>();
Person tom = Person.builder()
.name("tom")
.age(38)
.salary(new BigDecimal("11123.00"))
.birthday(date1).build();
Person jerry = Person.builder()
.name("jerry")
.age(25)
.salary(new BigDecimal("6464.00"))
.birthday(date2).build();
Person jack = Person.builder()
.name("jack")
.age(49)
.salary(new BigDecimal("9797.00"))
.birthday(date3).build();
Person rose = Person.builder()
.name("rose")
.age(67)
.salary(new BigDecimal("8098.00"))
.birthday(date4).build();
Person shel = Person.builder()
.name("shel")
.age(52)
.salary(new BigDecimal("94465165.00"))
.birthday(date5).build();
personList.add(tom);
personList.add(tom);
personList.add(jerry);
personList.add(jack);
personList.add(rose);
personList.add(shel);
personList.sort((o1, o2) -> o1.getSalary().compareTo(o2.getSalary()));
System.out.println("根据金钱升序：" + personList);
personList.sort((o1, o2) -> -1 * o1.getSalary().compareTo(o2.getSalary()));
System.out.println("根据金钱降序：" + personList);
personList.sort(new Comparator<Person>() {
@Override
public int compare(Person o1, Person o2) {
return o1.getAge().compareTo(o2.getAge());
}
});
System.out.println("根据年龄升序：" + personList);
personList.sort(new Comparator<Person>() {
@Override
public int compare(Person o1, Person o2) {
return o1.getBirthday().compareTo(o2.getBirthday());
}
});
System.out.println("根据日期升序：" + personList);
List<Person> collect = personList.stream().distinct().collect(Collectors.toList());
System.out.println("java8根据对象去重：" + collect);
List<Person> collect1 = personList.stream().sorted(Comparator.comparing(Person::getAge)).collect(Collectors.toList());
System.out.println("java8根据年龄升序：" + collect1);
List<Person> collect2 = personList.stream().sorted(Comparator.comparing(Person::getSalary).reversed()).collect(Collectors.toList());
System.out.println("java8根据金钱降序：" + collect2);
List<Person> collect3 = personList.stream().distinct().sorted(Comparator.comparing(Person::getBirthday).thenComparing(Person::getSalary)).collect(Collectors.toList());
System.out.println("java8先去重再根据日期排序，再根据年龄排序：" + collect3);
List<Person> collect4 = personList.stream().filter(person -> person.getName() == "tom" || person.getName() == "jerry").collect(Collectors.toList());
System.out.println("java8获取符合条件的变量的对象：" + collect4);
// 相当于获取数据库表的列
List<String> collect5 = personList.stream().map(person -> person.getName() + " - " + person.getAge()).collect(Collectors.toList());
System.out.println("java8获取所有的对象的属性组合：" + collect5);
List<BigDecimal> collect6 = personList.stream().map(person -> person.getSalary()).collect(Collectors.toList());
System.out.println("java8获取所有对象的某个属性（salary）：" + collect6);
List<BigDecimal> collect7 = collect6.stream().skip(1).collect(Collectors.toList());
System.out.println("将第一个元素跳过： " + collect7);
BigDecimal max = collect6.stream().max(BigDecimal::compareTo).get();
System.out.println("获取最大的值：" + max);
BigDecimal min = collect6.stream().min(BigDecimal::compareTo).get();
System.out.println("获取最小的值：" + min);
BigDecimal sum = collect6.stream().reduce(BigDecimal::add).get();
System.out.println("获取总和：" + sum);
BigDecimal min2 = collect6.stream().reduce(BigDecimal.valueOf(0), BigDecimal::min);
System.out.println("最小值：" + min2);
BigDecimal max2 = collect6.stream().reduce(BigDecimal.valueOf(0), BigDecimal::max);
System.out.println("最大值：" + max2);
}
public static void main(String[] args) throws ParseException {
testStream();
}
}
// 结果
根据金钱升序：[Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021), Person(name=rose, age=67, salary=8098.00, birthday=Wed May 02 19:23:01 CST 2018), Person(name=jack, age=49, salary=9797.00, birthday=Fri Sep 18 23:21:21 CST 2020), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=shel, age=52, salary=94465165.00, birthday=Mon Nov 25 03:54:12 CST 2019)]
根据金钱降序：[Person(name=shel, age=52, salary=94465165.00, birthday=Mon Nov 25 03:54:12 CST 2019), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=jack, age=49, salary=9797.00, birthday=Fri Sep 18 23:21:21 CST 2020), Person(name=rose, age=67, salary=8098.00, birthday=Wed May 02 19:23:01 CST 2018), Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021)]
根据年龄升序：[Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=jack, age=49, salary=9797.00, birthday=Fri Sep 18 23:21:21 CST 2020), Person(name=shel, age=52, salary=94465165.00, birthday=Mon Nov 25 03:54:12 CST 2019), Person(name=rose, age=67, salary=8098.00, birthday=Wed May 02 19:23:01 CST 2018)]
根据日期升序：[Person(name=rose, age=67, salary=8098.00, birthday=Wed May 02 19:23:01 CST 2018), Person(name=shel, age=52, salary=94465165.00, birthday=Mon Nov 25 03:54:12 CST 2019), Person(name=jack, age=49, salary=9797.00, birthday=Fri Sep 18 23:21:21 CST 2020), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021)]
java8根据对象去重：[Person(name=rose, age=67, salary=8098.00, birthday=Wed May 02 19:23:01 CST 2018), Person(name=shel, age=52, salary=94465165.00, birthday=Mon Nov 25 03:54:12 CST 2019), Person(name=jack, age=49, salary=9797.00, birthday=Fri Sep 18 23:21:21 CST 2020), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021)]
java8根据年龄升序：[Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=jack, age=49, salary=9797.00, birthday=Fri Sep 18 23:21:21 CST 2020), Person(name=shel, age=52, salary=94465165.00, birthday=Mon Nov 25 03:54:12 CST 2019), Person(name=rose, age=67, salary=8098.00, birthday=Wed May 02 19:23:01 CST 2018)]
java8根据金钱降序：[Person(name=shel, age=52, salary=94465165.00, birthday=Mon Nov 25 03:54:12 CST 2019), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=jack, age=49, salary=9797.00, birthday=Fri Sep 18 23:21:21 CST 2020), Person(name=rose, age=67, salary=8098.00, birthday=Wed May 02 19:23:01 CST 2018), Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021)]
java8先去重再根据日期排序，再根据年龄排序：[Person(name=rose, age=67, salary=8098.00, birthday=Wed May 02 19:23:01 CST 2018), Person(name=shel, age=52, salary=94465165.00, birthday=Mon Nov 25 03:54:12 CST 2019), Person(name=jack, age=49, salary=9797.00, birthday=Fri Sep 18 23:21:21 CST 2020), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021)]
java8获取符合条件的变量的对象：[Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=tom, age=38, salary=11123.00, birthday=Sat Jan 02 21:32:32 CST 2021), Person(name=jerry, age=25, salary=6464.00, birthday=Mon Jul 05 12:32:45 CST 2021)]
java8获取所有的对象的属性组合：[rose - 67, shel - 52, jack - 49, tom - 38, tom - 38, jerry - 25]
java8获取所有对象的某个属性（salary）：[8098.00, 94465165.00, 9797.00, 11123.00, 11123.00, 6464.00]
将第一个元素跳过： [94465165.00, 9797.00, 11123.00, 11123.00, 6464.00]
获取最大的值：94465165.00
获取最小的值：6464.00
获取总和：94511770.00
最小值：0
最大值：94465165.004. 消费
peek：如同于map，能得到流中的每一个元素。但map接收的是一个Function表达式，有返回值；而peek接收的是Consumer表达式，没有返回值。
Student s1 = new Student("aa", 10);
Student s2 = new Student("bb", 20);
List<Student> studentList = Arrays.asList(s1, s2);
studentList.stream()
.peek(o -> o.setAge(100))
.forEach(System.out::println);
//结果：
Student{name='aa', age=100}
Student{name='bb', age=100} 流的终止操作
1. 匹配、聚合操作
allMatch：接收一个 Predicate 函数，当流中每个元素都符合该断言时才返回true，否则返回false
noneMatch：接收一个 Predicate 函数，当流中每个元素都不符合该断言时才返回true，否则返回false
anyMatch：接收一个 Predicate 函数，只要流中有一个元素满足该断言则返回true，否则返回false
findFirst：返回流中第一个元素
findAny：返回流中的任意元素
count：返回流中元素的总个数
max：返回流中元素最大值
min：返回流中元素最小值
List<Integer> list = Arrays.asList(1, 2, 3, 4, 5);
boolean allMatch = list.stream().allMatch(e -> e > 10); //false
boolean noneMatch = list.stream().noneMatch(e -> e > 10); //true
boolean anyMatch = list.stream().anyMatch(e -> e > 4); //true
Integer findFirst = list.stream().findFirst().get(); //1
Integer findAny = list.stream().findAny().get(); //1
long count = list.stream().count(); //5
Integer max = list.stream().max(Integer::compareTo).get(); //5
Integer min = list.stream().min(Integer::compareTo).get(); //12. 递归操作
Optional reduce(BinaryOperator accumulator)：第一次执行时，accumulator函数的第一个参数为流中的第一个元素，第二个参数为流中元素的第二个元素；第二次执行时，第一个参数为第一次函数执行的结果，第二个参数为流中的第三个元素；依次类推。
T reduce(T identity, BinaryOperator accumulator)：流程跟上面一样，只是第一次执行时，accumulator函数的第一个参数为identity，而第二个参数为流中的第一个元素。
U reduce(U identity,BiFunction accumulator,BinaryOperator combiner)：在串行流(stream)中，该方法跟第二个方法一样，即第三个参数combiner不会起作用。在并行流(parallelStream)中,我们知道流被fork join出多个线程进行执行，此时每个线程的执行流程就跟第二个方法reduce(identity,accumulator)一样，而第三个参数combiner函数，则是将每个线程的执行结果当成一个新的流，然后使用第一个方法reduce(accumulator)流程进行递归。
//经过测试，当元素个数小于24时，并行时线程数等于元素个数，当大于等于24时，并行时线程数为16
List<Integer> list = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24);
Integer v = list.stream().reduce((x1, x2) -> x1 + x2).get();
System.out.println(v); // 300
Integer v1 = list.stream().reduce(10, (x1, x2) -> x1 + x2);
System.out.println(v1); //310
Integer v2 = list.stream().reduce(0,
(x1, x2) -> {
System.out.println("stream accumulator: x1:" + x1 + " x2:" + x2);
return x1 - x2;
},
(x1, x2) -> {
System.out.println("stream combiner: x1:" + x1 + " x2:" + x2);
return x1 * x2;
});
System.out.println(v2); // -300
Integer v3 = list.parallelStream().reduce(0,
(x1, x2) -> {
System.out.println("parallelStream accumulator: x1:" + x1 + " x2:" + x2);
return x1 - x2;
},
(x1, x2) -> {
System.out.println("parallelStream combiner: x1:" + x1 + " x2:" + x2);
return x1 * x2;
});
System.out.println(v3); //1974740483. 收集操作
collect：接收一个Collector实例，将流中元素收集成另外一个数据结构。
Collector 是一个接口，有以下5个抽象方法：
Supplier supplier()：创建一个结果容器A
BiConsumer accumulator()：消费型接口，第一个参数为容器A，第二个参数为流中元素T。
BinaryOperator combiner()：函数接口，该参数的作用跟上一个方法(reduce)中的combiner参数一样，将并行流中各 个子进程的运行结果(accumulator函数操作后的容器A)进行合并。
Function finisher()：函数式接口，参数为：容器A，返回类型为：collect方法最终想要的结果R。
Set characteristics()：返回一个不可变的Set集合，用来表明该Collector的特征。有以下三个特征：
CONCURRENT：表示此收集器支持并发。（官方文档还有其他描述，暂时没去探索，故不作过多翻译）
UNORDERED：表示该收集操作不会保留流中元素原有的顺序。
IDENTITY_FINISH：表示finisher参数只是标识而已，可忽略。
Collector 工具库：Collectors
Student s1 = new Student("aa", 10,1);
Student s2 = new Student("bb", 20,2);
Student s3 = new Student("cc", 10,3);
List<Student> list = Arrays.asList(s1, s2, s3);
//装成list
List<Integer> ageList = list.stream().map(Student::getAge).collect(Collectors.toList()); // [10, 20, 10]
//转成set
Set<Integer> ageSet = list.stream().map(Student::getAge).collect(Collectors.toSet()); // [20, 10]
//转成map,注:key不能相同，否则报错
Map<String, Integer> studentMap = list.stream().collect(Collectors.toMap(Student::getName, Student::getAge)); // {cc=10, bb=20, aa=10}
//字符串分隔符连接
String joinName = list.stream().map(Student::getName).collect(Collectors.joining(",", "(", ")")); // (aa,bb,cc)
//聚合操作
//1.学生总数
Long count = list.stream().collect(Collectors.counting()); // 3
//2.最大年龄 (最小的minBy同理)
Integer maxAge = list.stream().map(Student::getAge).collect(Collectors.maxBy(Integer::compare)).get(); // 20
//3.所有人的年龄
Integer sumAge = list.stream().collect(Collectors.summingInt(Student::getAge)); // 40
//4.平均年龄
Double averageAge = list.stream().collect(Collectors.averagingDouble(Student::getAge)); // 13.333333333333334
// 带上以上所有方法
DoubleSummaryStatistics statistics = list.stream().collect(Collectors.summarizingDouble(Student::getAge));
System.out.println("count:" + statistics.getCount() + ",max:" + statistics.getMax() + ",sum:" + statistics.getSum() + ",average:" + statistics.getAverage());
//分组
Map<Integer, List<Student>> ageMap = list.stream().collect(Collectors.groupingBy(Student::getAge));
//多重分组,先根据类型分再根据年龄分
Map<Integer, Map<Integer, List<Student>>> typeAgeMap = list.stream().collect(Collectors.groupingBy(Student::getType, Collectors.groupingBy(Student::getAge)));
//分区
//分成两部分，一部分大于10岁，一部分小于等于10岁
Map<Boolean, List<Student>> partMap = list.stream().collect(Collectors.partitioningBy(v -> v.getAge() > 10));
//递归
Integer allAge = list.stream().map(Student::getAge).collect(Collectors.reducing(Integer::sum)).get(); //40Collectors.groupingBy用法
public Product(Long id, Integer num, BigDecimal price, String name, String category) {
this.id = id;
this.num = num;
this.price = price;
this.name = name;
this.category = category;
}
Product prod1 = new Product(1L, 1, new BigDecimal("15.5"), "面包", "零食");
Product prod2 = new Product(2L, 2, new BigDecimal("20"), "饼干", "零食");
Product prod3 = new Product(3L, 3, new BigDecimal("30"), "月饼", "零食");
Product prod4 = new Product(4L, 3, new BigDecimal("10"), "青岛啤酒", "啤酒");
Product prod5 = new Product(5L, 10, new BigDecimal("15"), "百威啤酒", "啤酒");
List<Product> prodList = Lists.newArrayList(prod1, prod2, prod3, prod4, prod5);
//按照类目分组：
Map<String, List<Product>> prodMap= prodList.stream().collect(Collectors.groupingBy(Product::getCategory));
//{"啤酒":[{"category":"啤酒","id":4,"name":"青岛啤酒","num":3,"price":10},{"category":"啤酒","id":5,"name":"百威啤酒","num":10,"price":15}],"零食":[{"category":"零食","id":1,"name":"面包","num":1,"price":15.5},{"category":"零食","id":2,"name":"饼干","num":2,"price":20},{"category":"零食","id":3,"name":"月饼","num":3,"price":30}]}
//按照几个属性拼接分组：
Map<String, List<Product>> prodMap = prodList.stream().collect(Collectors.groupingBy(item -> item.getCategory() + "_" + item.getName()));
//{"零食_月饼":[{"category":"零食","id":3,"name":"月饼","num":3,"price":30}],"零食_面包":[{"category":"零食","id":1,"name":"面包","num":1,"price":15.5}],"啤酒_百威啤酒":[{"category":"啤酒","id":5,"name":"百威啤酒","num":10,"price":15}],"啤酒_青岛啤酒":[{"category":"啤酒","id":4,"name":"青岛啤酒","num":3,"price":10}],"零食_饼干":[{"category":"零食","id":2,"name":"饼干","num":2,"price":20}]}
//根据不同条件分组
Map<String, List<Product>> prodMap= prodList.stream().collect(Collectors.groupingBy(item -> {
if(item.getNum() < 3) {
return "3";
}else {
return "other";
}
}));
//{"other":[{"category":"零食","id":3,"name":"月饼","num":3,"price":30},{"category":"啤酒","id":4,"name":"青岛啤酒","num":3,"price":10},{"category":"啤酒","id":5,"name":"百威啤酒","num":10,"price":15}],"3":[{"category":"零食","id":1,"name":"面包","num":1,"price":15.5},{"category":"零食","id":2,"name":"饼干","num":2,"price":20}]}
//多级分组
Map<String, Map<String, List<Product>>> prodMap= prodList.stream().collect(Collectors.groupingBy(Product::getCategory, Collectors.groupingBy(item -> {
if(item.getNum() < 3) {
return "3";
}else {
return "other";
}
})));
//{"啤酒":{"other":[{"category":"啤酒","id":4,"name":"青岛啤酒","num":3,"price":10},{"category":"啤酒","id":5,"name":"百威啤酒","num":10,"price":15}]},"零食":{"other":[{"category":"零食","id":3,"name":"月饼","num":3,"price":30}],"3":[{"category":"零食","id":1,"name":"面包","num":1,"price":15.5},{"category":"零食","id":2,"name":"饼干","num":2,"price":20}]}}
//按子组收集数据
//求总数
Map<String, Long> prodMap = prodList.stream().collect(Collectors.groupingBy(Product::getCategory, Collectors.counting()));
//{"啤酒":2,"零食":3}
//求和
Map<String, Integer> prodMap = prodList.stream().collect(Collectors.groupingBy(Product::getCategory, Collectors.summingInt(Product::getNum)));
//{"啤酒":13,"零食":6}
//把收集器的结果转换为另一种类型
Map<String, Product> prodMap = prodList.stream().collect(Collectors.groupingBy(Product::getCategory, Collectors.collectingAndThen(Collectors.maxBy(Comparator.comparingInt(Product::getNum)), Optional::get)));
//{"啤酒":{"category":"啤酒","id":5,"name":"百威啤酒","num":10,"price":15},"零食":{"category":"零食","id":3,"name":"月饼","num":3,"price":30}}
//联合其他收集器
Map<String, Set<String>> prodMap = prodList.stream().collect(Collectors.groupingBy(Product::getCategory, Collectors.mapping(Product::getName, Collectors.toSet())));
//{"啤酒":["青岛啤酒","百威啤酒"],"零食":["面包","饼干","月饼"]}Collectors.toList() 解析
//toList 源码
public static <T> Collector<T, ?, List<T>> toList() {
return new CollectorImpl<>((Supplier<List<T>>) ArrayList::new, List::add,
(left, right) -> {
left.addAll(right);
return left;
}, CH_ID);
}
//为了更好地理解，我们转化一下源码中的lambda表达式
public <T> Collector<T, ?, List<T>> toList() {
Supplier<List<T>> supplier = () -> new ArrayList();
BiConsumer<List<T>, T> accumulator = (list, t) -> list.add(t);
BinaryOperator<List<T>> combiner = (list1, list2) -> {
list1.addAll(list2);
return list1;
};
Function<List<T>, List<T>> finisher = (list) -> list;
Set<Collector.Characteristics> characteristics = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH));
return new Collector<T, List<T>, List<T>>() {
@Override
public Supplier supplier() {
return supplier;
}
@Override
public BiConsumer accumulator() {
return accumulator;
}
@Override
public BinaryOperator combiner() {
return combiner;
}
@Override
public Function finisher() {
return finisher;
}
@Override
public Set<Characteristics> characteristics() {
return characteristics;
}
};
}
