什么是 Stream？
Stream（流）是一个来自数据源的元素队列并支持聚合操作
- 元素是特定类型的对象，形成一个队列。 Java中的Stream并不会存储元素，而是按需计算。
- 数据源 流的来源。 可以是集合，数组，I/O channel， 产生器generator 等。
- 聚合操作 类似SQL语句一样的操作， 比如filter, map, reduce, find, match, sorted等。
和以前的Collection操作不同， Stream操作还有两个基础的特征：
- Pipelining: 中间操作都会返回流对象本身。 这样多个操作可以串联成一个管道， 如同流式风格（fluent style）。 这样做可以对操作进行优化， 比如延迟执行(laziness)和短路( short-circuiting)。
- 内部迭代： 以前对集合遍历都是通过Iterator或者For-Each的方式, 显式的在集合外部进行迭代， 这叫做外部迭代。 Stream提供了内部迭代的方式， 通过访问者模式(Visitor)实现。
生成流
在 Java 8 中, 集合接口有两个方法来生成流：
- stream() − 为集合创建串行流。
- parallelStream() − 为集合创建并行流。
List<String> strings = Arrays.asList("abc", "", "bc", "efg", "abcd","", "jkl");
List<String> filtered = strings.stream().filter(string -> !string.isEmpty()).collect(Collectors.toList());
forEach
Stream 提供了新的方法 'forEach' 来迭代流中的每个数据。以下代码片段使用 forEach 输出了10个随机数：
Random random = new Random();
random.ints().limit(10).forEach(System.out::println);
map
map 方法用于映射每个元素到对应的结果，以下代码片段使用 map 输出了元素对应的平方数：
List<Integer> numbers = Arrays.asList(3, 2, 2, 3, 7, 3, 5);
// 获取对应的平方数
List<Integer> squaresList = numbers.stream().map( i -> i*i).distinct().collect(Collectors.toList());
filter
filter 方法用于通过设置的条件过滤出元素。以下代码片段使用 filter 方法过滤出空字符串：
List<String>strings = Arrays.asList("abc", "", "bc", "efg", "abcd","", "jkl");
// 获取空字符串的数量
long count = strings.stream().filter(string -> string.isEmpty()).count();
limit
limit 方法用于获取指定数量的流。 以下代码片段使用 limit 方法打印出 10 条数据：
Random random = new Random();
random.ints().limit(10).forEach(System.out::println);
sorted
sorted 方法用于对流进行排序。以下代码片段使用 sorted 方法对输出的 10 个随机数进行排序：
Random random = new Random();
random.ints().limit(10).sorted().forEach(System.out::println);
并行（parallel）程序
parallelStream 是流并行处理程序的代替方法。以下实例我们使用 parallelStream 来输出空字符串的数量：
List<String> strings = Arrays.asList("abc", "", "bc", "efg", "abcd","", "jkl");
// 获取空字符串的数量
long count = strings.parallelStream().filter(string -> string.isEmpty()).count();
我们可以很容易的在顺序运行和并行直接切换。
Collectors
Collectors 类实现了很多归约操作，例如将流转换成集合和聚合元素。Collectors 可用于返回列表或字符串：
List<String>strings = Arrays.asList("abc", "", "bc", "efg", "abcd","", "jkl");
List<String> filtered = strings.stream().filter(string -> !string.isEmpty()).collect(Collectors.toList()); System.out.println("筛选列表: " + filtered);
//合并字符串，并以逗号分隔
String mergedString = strings.stream().filter(string -> !string.isEmpty()).collect(Collectors.joining(", ")); System.out.println("合并字符串: " + mergedString);
统计
另外，一些产生统计结果的收集器也非常有用。它们主要用于int、double、long等基本类型上，它们可以用来产生类似如下的统计结果。
List<Integer> numbers = Arrays.asList(3, 2, 2, 3, 7, 3, 5);
IntSummaryStatistics stats = numbers.stream().mapToInt((x) -> x).summaryStatistics(); System.out.println("列表中最大的数 : " + stats.getMax()); System.out.println("列表中最小的数 : " + stats.getMin());
System.out.println("所有数之和 : " + stats.getSum()); System.out.println("平均数 : " + stats.getAverage`());`
实例演示
API功能举例 首先创建一个用户的实体类，包括姓名、年龄、性别、地址、赏金 几个属性
@Data
public class User {
//姓名
private String name;
//年龄
private Integer age;
//性别
private Integer sex;
//地址
private String address;
//赏金
private BigDecimal money;
public User(String name, Integer age, Integer sex, String address,BigDecimal money) {
this.name = name;
this.age = age;
this.sex = sex;
this.address = address;
this.money = money;
}
@Override
public String toString() {
return "User{" +
"name='" + name + '\'' +
", age=" + age +
", sex=" + sex +
", money=" + money +
", address='" + address + '\'' +
'}';
}
}
我们在创建一个测试类，包含主方法，并创建一个数据源，作为我们测试的对象
public class Stream {
public static void main(String[] args) {
}
public static List<User> users(){
List<User> list = Arrays.asList(
new User("李星云", 18, 0, "渝州",new BigDecimal(1000)),
new User("陆林轩", 16, 1, "渝州",new BigDecimal(500)),
new User("姬如雪", 17, 1, "幻音坊",new BigDecimal(800)),
new User("袁天罡", 99, 0, "藏兵谷",new BigDecimal(100000)),
new User("张子凡", 19, 0, "天师府",new BigDecimal(900)),
new User("陆佑劫", 45, 0, "不良人",new BigDecimal(600)),
new User("张天师", 48, 0, "天师府",new BigDecimal(1100)),
new User("蚩梦", 18, 1, "万毒窟",new BigDecimal(800))
);
return list;
}
}
api实例
/*filter过滤(T-> boolean)*/
public static void filter(){
List<User> list = users();
List<User> newlist = list.stream().filter(user -> user.getAge() > 20)
.collect(Collectors.toList());
for (User user : newlist) {
System.out.println(user.getName()+" --> "+ user.getAge());
}
}
---结果---
袁天罡 --> 99
陆佑劫 --> 45
张天师 --> 48
/*distinct 去重*/
数据源中复制new User("李星云", 18, 0, "渝州",new BigDecimal(1000)) 并粘贴两个
public static void distinct(){
List<User> list = users();
List<User> newlist = list.stream().distinct().collect(Collectors.toList());
for (User user : newlist) {
System.out.println(user.getName()+" --> "+ user.getAge());
}
}
---结果---
李星云 --> 18
陆林轩 --> 16
姬如雪 --> 17
袁天罡 --> 99
张子凡 --> 19
陆佑劫 --> 45
张天师 --> 48
蚩梦 --> 18
/*sorted排序*/
public static void sorted(){
List<User> list = users();
List<User> newlist = list.stream()
.sorted(Comparator.comparingInt(User::getAge))
.collect(Collectors.toList());
for (User user : newlist) {
System.out.println(user.getName()+" --> "+ user.getAge());
}
}
---结果---
陆林轩 --> 16
姬如雪 --> 17
李星云 --> 18
蚩梦 --> 18
张子凡 --> 19
陆佑劫 --> 45
张天师 --> 48
袁天罡 --> 99
/*limit返回前n个元素*/
public static void limit(){
List<User> list = users();
List<User> newlist = list.stream()
.sorted(Comparator.comparingInt(User::getAge))
.limit(2)
.collect(Collectors.toList());
for (User user : newlist) {
System.out.println(user.getName()+" --> "+ user.getAge());
}
}
---结果---
陆林轩 --> 16
姬如雪 --> 17
/*skip去除前n个元素*/
public static void skip(){
List<User> list = users();
List<User> newlist = list.stream()
.sorted(Comparator.comparingInt(User::getAge))
.skip(2)
.collect(Collectors.toList());
for (User user : newlist) {
System.out.println(user.getName()+" --> "+ user.getAge());
}
}
---结果---
李星云 --> 18
蚩梦 --> 18
张子凡 --> 19
陆佑劫 --> 45
张天师 --> 48
袁天罡 --> 99
/*map(T->R)*/
public static void map(){
List<User> list = users();
List<String> newlist = list.stream()
.map(User::getName).distinct().collect(Collectors.toList());
for (String add : newlist) {
System.out.println(add);
}
}
---结果---
李星云
陆林轩
姬如雪
袁天罡
张子凡
陆佑劫
张天师
蚩梦
/*flatMap(T -> Stream<R>)*/
public static void flatmap(){
List<String> flatmap = new ArrayList<>();
flatmap.add("常宣灵,常昊灵");
flatmap.add("孟婆,判官红,判官蓝");
/*
这里原集合中的数据由逗号分割，使用split进行拆分后，得到的是Stream<String[]>，
字符串数组组成的流，要使用flatMap的Arrays::stream
将Stream<String[]>转为Stream<String>,然后把流相连接
*/
flatmap = flatmap.stream()
.map(s -> s.split(","))
.flatMap(Arrays::stream)
.collect(Collectors.toList());
for (String name : flatmap) {
System.out.println(name);
}
}
---结果---
常宣灵
常昊灵
孟婆
判官红
判官蓝
/*allMatch（T->boolean）检测是否全部满足参数行为*/
public static void allMatch(){
List<User> list = users();
boolean flag = list.stream()
.allMatch(user -> user.getAge() >= 17);
System.out.println(flag);
}
---结果---
false
/*anyMatch（T->boolean）检测是否有任意元素满足给定的条件*/
public static void anyMatch(){
List<User> list = users();
boolean flag = list.stream()
.anyMatch(user -> user.getSex() == 1);
System.out.println(flag);
}
---结果---
true
/*noneMatchT->boolean）流中是否有元素匹配给定的 T -> boolean条件*/
public static void noneMatch(){
List<User> list = users();
boolean flag = list.stream()
.noneMatch(user -> user.getAddress().contains("郑州"));
System.out.println(flag);
}
---结果---
true
/*findFirst( ):找到第一个元素*/
public static void findfirst(){
List<User> list = users();
Optional<User> optionalUser = list.stream()
.sorted(Comparator.comparingInt(User::getAge))
.findFirst();
System.out.println(optionalUser.toString());
}
---结果---
Optional[User{name='陆林轩', age=16, sex=1, money=500, address='渝州'}]
/*findAny( ):找到任意一个元素*/
public static void findAny(){
List<User> list = users();
// Optional<User> optionalUser = list.stream()
.findAny();
Optional<User> optionalUser = list.stream()
.findAny();
System.out.println(optionalUser.toString());
}
---结果---
Optional[User{name='李星云', age=18, sex=0, money=1000, address='渝州'}]
/*计算总数*/
public static void count(){
List<User> list = users();
long count = list.stream().count();
System.out.println(count);
}
---结果---
8
/*最大值最小值*/
public static void max_min(){
List<User> list = users();
Optional<User> max = list.stream()
.collect(
Collectors.maxBy(
Comparator.comparing(User::getAge)
)
);
Optional<User> min = list.stream()
.collect(
Collectors.minBy(
Comparator.comparing(User::getAge)
)
);
System.out.println("max--> " + max+" min--> "+ min);
}
---结果---
max--> Optional[User{name='袁天罡', age=99, sex=0, money=100000, address='藏兵谷'}] min--> Optional[User{name='陆林轩', age=16, sex=1, money=500, address='渝州'}]
/*求和_平均值*/
public static void sum_avg(){
List<User>list = users();
int totalAge = list.stream()
.collect(Collectors.summingInt(User::getAge));
System.out.println("totalAge--> "+ totalAge);
/*获得列表对象金额， 使用reduce聚合函数,实现累加器*/
BigDecimal totalMpney = list.stream()
.map(User::getMoney)
.reduce(BigDecimal.ZERO, BigDecimal::add);
System.out.println("totalMpney--> " + totalMpney);
double avgAge = list.stream()
.collect(Collectors.averagingInt(User::getAge));
System.out.println("avgAge--> " + avgAge);
}
---结果---
totalAge--> 280
totalMpney--> 105700
avgAge--> 35.0
/*一次性得到元素的个数、总和、最大值、最小值*/
public static void allVlaue(){
List<User> list = users();
IntSummaryStatistics statistics = list.stream()
.collect(Collectors.summarizingInt(User::getAge));
System.out.println(statistics);
}
---结果---
IntSummaryStatistics{count=8, sum=280, min=16, average=35.000000, max=99}
/*拼接*/
public static void join(){
List<User> list = users();
String names = list.stream()
.map(User::getName)
.collect(Collectors.joining(", "));
System.out.println(names);
}
---结果---
李星云, 陆林轩, 姬如雪, 袁天罡, 张子凡, 陆佑劫, 张天师, 蚩梦
/*分组*/
public static void group(){
Map<Integer, List<User>> map = users().stream()
.collect(Collectors.groupingBy(User::getSex));
System.out.println(new Gson().toJson(map));
System.out.println();
Map<Integer, Map<Integer,List<User>>> map2 = users().stream()
.collect(Collectors.groupingBy(User::getSex,
Collectors.groupingBy(User::getAge)));
System.out.println(new Gson().toJson(map2));
}
---结果---
{"0":[{"name":"李星云","age":18,"sex":0,"address":"渝州","money":1000},{"name":"袁天罡","age":99,"sex":0,"address":"藏兵谷","money":100000},{"name":"张子凡","age":19,"sex":0,"address":"天师府","money":900},{"name":"陆佑劫","age":45,"sex":0,"address":"不良人","money":600},{"name":"张天师","age":48,"sex":0,"address":"天师府","money":1100}],"1":[{"name":"陆林轩","age":16,"sex":1,"address":"渝州","money":500},{"name":"姬如雪","age":17,"sex":1,"address":"幻音坊","money":800},{"name":"蚩梦","age":18,"sex":1,"address":"万毒窟","money":800}]}
{"0":{"48":[{"name":"张天师","age":48,"sex":0,"address":"天师府","money":1100}],"18":[{"name":"李星云","age":18,"sex":0,"address":"渝州","money":1000}],"19":[{"name":"张子凡","age":19,"sex":0,"address":"天师府","money":900}],"99":[{"name":"袁天罡","age":99,"sex":0,"address":"藏兵谷","money":100000}],"45":[{"name":"陆佑劫","age":45,"sex":0,"address":"不良人","money":600}]},"1":{"16":[{"name":"陆林轩","age":16,"sex":1,"address":"渝州","money":500}],"17":[{"name":"姬如雪","age":17,"sex":1,"address":"幻音坊","money":800}],"18":[{"name":"蚩梦","age":18,"sex":1,"address":"万毒窟","money":800}]}}
/*分组合计*/
public static void groupCount(){
Map<Integer, Long> num = users().stream()
.collect(Collectors.groupingBy(User::getSex, Collectors.counting()));
System.out.println(num);
Map<Integer, Long> num2 = users().stream()
.filter(user -> user.getAge()>=18)
.collect(Collectors.groupingBy(User::getSex, Collectors.counting()));
System.out.println(num2);
}
---结果---
{0=5, 1=3}
{0=5, 1=1}
/*分区*/
public static void partitioningBy(){
List<User> list = users();
Map<Boolean, List<User>> part = list.stream()
.collect(Collectors.partitioningBy(user -> user.getAge() <= 30));
System.out.println(new Gson().toJson(part));
}
---结果---
{"false":[{"name":"袁天罡","age":99,"sex":0,"address":"藏兵谷","money":100000},{"name":"陆佑劫","age":45,"sex":0,"address":"不良人","money":600},{"name":"张天师","age":48,"sex":0,"address":"天师府","money":1100}],"true":[{"name":"李星云","age":18,"sex":0,"address":"渝州","money":1000},{"name":"陆林轩","age":16,"sex":1,"address":"渝州","money":500},{"name":"姬如雪","age":17,"sex":1,"address":"幻音坊","money":800},{"name":"张子凡","age":19,"sex":0,"address":"天师府","money":900},{"name":"蚩梦","age":18,"sex":1,"address":"万毒窟","money":800}]}
