关于Stream和Collectors的用法，这应该是总结的最全的文章了，大家可以收藏一下。

一：简介

java.util.Stream 表示能应用在一组元素上一次执行的操作序列。Stream 操作分为中间操作或者最终操作两种，最终操作返回一特定类型的计算结果，而中间操作返回Stream本身，这样就可以将多个操作依次串起来。Stream 的创建需要指定一个数据源，比如 java.util.Collection的子类，List或者Set， Map不支持。Stream的操作可以串行stream()执行或者并行parallelStream()执行。

二：示例

1. forEach 循环

@Test
public void forEach(){
 // 你不鸟我,我也不鸟你
 List<String> list = Arrays.asList("you", "don't", "bird", "me", ",", 
									 "I", "don't", "bird", "you");
 // 方式一：JDK1.8之前的循环方式
 for (String item: list) {
 System.out.println(item);
 }
 // 方式二：使用Stream的forEach方法
 // void forEach(Consumer<? super T> action)
 list.stream().forEach(item -> System.out.println(item));
 // 方式三：方式二的简化方式
 // 由于方法引用也属于函数式接口，所以方式二Lambda表达式也可以使用方法引用来代替
 // 此种方式就是方式一、方式二的简写形式
 list.stream().forEach(System.out::println);
}

2. filter 过滤

public class User {
 private Long id;
 private String phone;
 private Integer age;
	public User(){}
 public User(Long id, String username, Integer age) {
 this.id = id;
 this.username = username;
 this.age = age;
 }
	// Getter & Setter & toString
}
@Test
public void filter(){
	List<User> users = Arrays.asList(
 new User(1L, "mengday", 28),
 new User(2L, "guoguo", 18),
 new User(3L, "liangliang", 17)
 );
 // Stream<T> filter(Predicate<? super T> predicate);
 users.stream().filter(user -> user.getAge() > 18).forEach(System.out::println);
}

3. map 映射

@Test
public void map(){
 List<String> list = Arrays.asList("how", "are", "you", "how", "old", "are", "you", "?");
 // <R> Stream<R> map(Function<? super T, ? extends R> mapper);
 list.stream().map(item -> item.toUpperCase()).forEach(System.out::println);
}

4. flatMap

@Test
public void flatMap(){
 List<Integer> a = Arrays.asList(1, 2, 3);
 List<Integer> b = Arrays.asList(4, 5, 6);
 // <R> Stream<R> flatMap(Function<? super T, ? extends Stream<? extends R>> mapper)
 List<List<Integer>> collect = Stream.of(a, b).collect(Collectors.toList());
 // [[1, 2, 3], [4, 5, 6]] 
 System.out.println(collect);
 // 将多个集合中的元素合并成一个集合
 List<Integer> mergeList = Stream.of(a, b).flatMap(list -> list.stream()).collect(Collectors.toList());
 // [1, 2, 3, 4, 5, 6]
 System.out.println(mergeList);
 // 通过Builder模式来构建
 Stream<Object> stream = Stream.builder().add("hello").add("hi").add("byebye").build();
}

5. sorted 排序

@Test
public void sort(){
 List<String> list = Arrays.asList("c", "e", "a", "d", "b");
 // Stream<T> sorted(Comparator<? super T> comparator);
 // int compare(T o1, T o2);
 list.stream().sorted((s1, s2) -> s1.compareTo(s2)).forEach(System.out::println);
}

6. distinct 去重复

@Test
public void distinct(){
 // 知之为知之,不知为不知
 Stream<String> stream = Stream.of("know", "is", "know", "noknow", "is", "noknow");
 stream.distinct().forEach(System.out::println); // know is noknow
}

7. count 总数量

@Test
public void count(){
 Stream<String> stream = Stream.of("know", "is", "know", "noknow", "is", "noknow");
 long count = stream.count();
 System.out.println(count);
}

8. min、max

@Test
public void min(){
 List<String> list = Arrays.asList("1", "2", "3", "4", "5");
 // Optional<T> min(Comparator<? super T> comparator);
 Optional<String> optional = list.stream().min((a, b) -> a.compareTo(b));
 String value = optional.get();
 System.out.println(value);
}

9. skip、limit

@Test
public void skip(){
 List<String> list = Arrays.asList("a", "b", "c", "d", "e");
 // Stream<T> skip(long n)
 list.stream().skip(2).forEach(System.out::println); // c、d、e
}
@Test
public void limit(){
 List<String> list = Arrays.asList("a", "b", "c", "d", "e");
 list.stream().skip(2).limit(2).forEach(System.out::println); // c、d
}

10. collect

@Test
public void collect(){
 List<String> list = Arrays.asList("a", "b", "c", "d", "e");
 // Stream -> Collection
 List<String> collect = list.stream().collect(Collectors.toList());
 // Stream -> Object[]
 Object[] objects = list.stream().toArray();
}

11. concat

@Test
public void concat(){
 List<String> list = Arrays.asList("a", "b");
 List<String> list2 = Arrays.asList("c", "d");
 Stream<String> concatStream = Stream.concat(list.stream(), list2.stream());
 concatStream.forEach(System.out::println);
}

12. anyMatch、allMatch

@Test
public void match(){
 // 你给我站住
 List<String> list = Arrays.asList("you", "give", "me", "stop");
 // boolean anyMatch(Predicate<? super T> predicate);
 // parallelStream可以并行计算，速度比stream更快
 boolean result = list.parallelStream().anyMatch(item -> item.equals("me"));
 System.out.println(result);
}
/**
* anyMatch伪代码
 * 如果集合中有一个元素满足条件就返回true
 * @return
 */
public boolean anyMatch() {
 List<String> list = Arrays.asList("you", "give", "me", "stop");
 for (String item : list) {
 if (item.equals("me")) {
 return true;
 }
 }
 return false;
}

13. reduce 归纳

@Test
public void reduce(){
 Stream<String> stream = Stream.of("you", "give", "me", "stop");
 // Optional<T> reduce(BinaryOperator<T> accumulator);
 Optional<String> optional = stream.reduce((before, after) -> before + "," + after);
 optional.ifPresent(System.out::println); // you,give,me,stop
}

14. findFirst、findAny

@Test
public void findFirst(){
 Stream<String> stream = Stream.of("you", "give", "me", "stop");
 String value = stream.findFirst().get();
 System.out.println(value);
}
@Test
public void findAny(){
 Stream<String> stream = Stream.of("you", "give", "me", "stop");
 String value2 = stream.findAny().get();
 System.out.println(value2);
}

15. 流转换成集合

@Test
public void testToCollection(){
 List<Integer> list = Arrays.asList(1, 2, 3);
 // [10, 20, 30]
 List<Integer> collect = list.stream().map(i -> i * 10).collect(Collectors.toList());
 
 // [20, 10, 30]
 Set<Integer> collect1 = list.stream().map(i -> i * 10).collect(Collectors.toSet());
 
 // {key1=value:10, key2=value:20, key3=value:30}
 Map<String, String> collect2 = list.stream().map(i -> i * 10).collect(Collectors.toMap(key -> "key" + key/10, value -> "value:" + value));
 
 // [1, 3, 4]
 TreeSet<Integer> collect3= Stream.of(1, 3, 4).collect(Collectors.toCollection(TreeSet::new));
}
@Data
@ToString
@AllArgsConstructor
@RequiredArgsConstructor
public class User {
 private Long id;
 private String username;
}
@Test
public void testToMap() {
	List<User> userList = Arrays.asList(
	 new User(1L, "mengday"),
	 new User(2L, "mengdee"),
	 new User(3L, "mengdy")
	);
	
	// toMap 可用于将List转为Map，便于通过key快速查找到某个value
	Map<Long, User> userIdAndModelMap = userList.stream().collect(Collectors.toMap(User::getId, Function.identity()));
	User user = userIdAndModelMap.get(1L);
	// User(id=1, username=mengday)
	System.out.println(user);
	
	Map<Long, String> userIdAndUsernameMap = userList.stream().collect(Collectors.toMap(User::getId, User::getUsername));
	String username = userIdAndUsernameMap.get(1L);
	// mengday
	System.out.println(username);
}

16. 集合元素拼接

@Test
public void testJoining(){
 // a,b,c
 List<String> list2 = Arrays.asList("a", "b", "c");
 String result = list2.stream().collect(Collectors.joining(","));
 // Collectors.joining(",")的结果是：a,b,c 然后再将结果 x + "d"操作, 最终返回a,b,cd
 String str= Stream.of("a", "b", "c").collect(Collectors.collectingAndThen(Collectors.joining(","), x -> x + "d"));
}

17. 元素聚合

@Test
public void test(){
 // 求最值 3
 List<Integer> list = Arrays.asList(1, 2, 3);
 Integer maxValue = list.stream().collect(Collectors.collectingAndThen(Collectors.maxBy((a, b) -> a - b), Optional::get));
 // 最小值 1
 Integer minValue = list.stream().collect(Collectors.collectingAndThen(Collectors.minBy((a, b) -> a - b), Optional::get));
 // 求和 6
 Integer sumValue = list.stream().collect(Collectors.summingInt(item -> item));
 // 平均值 2.0
 Double avg = list.stream().collect(Collectors.averagingDouble(x -> x));
}
@Test
public void test(){
	// 映射：先对集合中的元素进行映射，然后再对映射的结果使用Collectors操作
 // A,B,C
 Stream.of("a", "b", "c").collect(Collectors.mapping(x -> x.toUpperCase(), Collectors.joining(",")));
}

18. 分组

public class User {
 private Long id;
 private String username;
 private Integer type;
 // Getter & Setter & toString
}
@Test
public void testGroupBy(){
 List<Integer> list = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
 // 奇偶数分组：奇数分一组，偶数分一组
 // groupingBy(Function<? super T, ? extends K> classifier) 参数是Function类型，Function返回值可以是要分组的条件，也可以是要分组的字段
 // 返回的结果是Map，其中key的数据类型为Function体中计算类型，value是List<T>类型，为分组的结果
 Map<Boolean, List<Integer>> result = list.stream().collect(Collectors.groupingBy(item -> item % 2 == 0));
 // {false=[1, 3, 5, 7, 9], true=[2, 4, 6, 8, 10]}
 System.out.println(result);
	 // partitioningBy 用于分成两组的情况
 Map<Boolean, List<Integer>> twoPartiton = list.stream().collect(Collectors.partitioningBy(item -> item % 2 == 0));
 System.out.println(twoPartiton);
 
 
 User user = new User(1L, "zhangsan", 1);
 User user2 = new User(2L, "lisi", 2);
 User user3 = new User(3L, "wangwu", 3);
 User user4 = new User(4L, "fengliu", 1);
 List<User> users = Arrays.asList(user, user2, user3, user4);
 // 根据某个字段进行分组
 Map<Integer, List<User>> userGroup = users.stream().collect(Collectors.groupingBy(item -> item.type));
 /**
 * key 为要分组的字段
 * value 分组的结果
 * {
 * 1=[User{id=1, username='zhangsan', type=1}, User{id=4, username='fengliu', type=1}],
 * 2=[User{id=2, username='lisi', type=2}],
 * 3=[User{id=3, username='wangwu', type=3}]
 * }
 */
 System.out.println(userGroup);
} 
// 分组并对分组中的数据统计
@Test
public void testGroupBy2() {
	 Foo foo1 = new Foo(1, 2);
 Foo foo2 = new Foo(2, 23);
 Foo foo3 = new Foo(2, 6);
 List<Foo> list = new ArrayList<>(4);
 list.add(foo1);
 list.add(foo2);
 list.add(foo3);
 Map<Integer, IntSummaryStatistics> collect = list.stream().collect(Collectors.groupingBy(Foo::getCode, Collectors.summarizingInt(Foo::getCount)));
 IntSummaryStatistics statistics1 = collect.get(1);
 IntSummaryStatistics statistics2 = collect.get(2);
 System.out.println(statistics1.getSum());
 System.out.println(statistics1.getAverage());
 System.out.println(statistics1.getMax());
 System.out.println(statistics1.getMin());
 System.out.println(statistics1.getCount());
 System.out.println(statistics2.getSum());
 System.out.println(statistics2.getAverage());
 System.out.println(statistics2.getMax());
 System.out.println(statistics2.getMin());
 System.out.println(statistics2.getCount());
}

19.累计操作

@Test
public void testReducing(){
 // sum: 是每次累计计算的结果，b是Function的结果
 System.out.println(Stream.of(1, 3, 4).collect(Collectors.reducing(0, x -> x + 1, (sum, b) -> {
 System.out.println(sum + "-" + b);
 return sum + b;
 })));
	
	 // 下面代码是对reducing函数功能实现的描述，用于理解reducing的功能
 int sum = 0;
 List<Integer> list3 = Arrays.asList(1, 3, 4);
 for (Integer item : list3) {
 int b = item + 1;
 System.out.println(sum + "-" + b);
 sum = sum + b;
 }
 System.out.println(sum);
		
 // 注意reducing可以用于更复杂的累计计算，加减乘除或者更复杂的操作
 // result = 2 * 4 * 5 = 40
 System.out.println(Stream.of(1, 3, 4).collect(Collectors.reducing(1, x -> x + 1, (result, b) -> {
 System.out.println(result + "-" + b);
 return result * b;
 })));
}