This article brings you relevant knowledge about Java, which mainly introduces related issues about the detailed usage of Stream. The new Stream in the version, together with the Lambda that appears in the same version, provide us with the operation collection (Collection) It is a great convenience. Let’s take a look at it below. I hope it will be helpful to everyone.
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Java 8 is a very successful The new version of Stream, combined with the Lambda that appears in the same version, provides us with great convenience in operating collections. Stream is a new member of JDK8, which allows processing of data collections in a declarative manner. The Stream stream can be regarded as an advanced iterator for traversing the data collection. Stream is the key abstract concept for processing collections in Java 8. It can specify the operations you want to perform on the collection, and can perform very complex operations such as finding/filtering/filtering, sorting, aggregating and mapping data. Using the Stream API to operate on collection data is similar to using SQL to perform database queries. You can also use the Stream API to perform operations in parallel. In short, the Stream API provides an efficient and easy-to-use way to process data.
Code is written in a declarative manner, describing what it wants to accomplish, rather than how to complete an operation.
Several basic operations can be connected to express complex data processing pipelines while keeping the code clear and readable.
Generate a sequence of elements from a source that supports data processing operations. The data source can be a collection, array or IO resource.
From an operational perspective, streams are different from collections. Streams do not store data values; the purpose of streams is to process data, it is about algorithms and calculations.
If the collection is used as the data source of the stream, creating the stream will not cause data to flow; if the termination operation of the stream requires a value, the stream will get the value from the collection; the stream is only used once.
The central idea of the stream is to delay calculation, and the stream does not calculate the value until it is needed. 
Stream can be created from an array or collection. Operations on streams are divided into two types:
Intermediate operations return a new stream each time, and there can be multiple.
Terminal operation, each stream can only perform one terminal operation, and the stream cannot be used again after the terminal operation is completed. Terminal operations produce a new collection or value.
Features:
is not a data structure and will not save data.
The original data source will not be modified, it will save the operated data to another object. (Reservation: After all, the peek method can modify the elements in the stream)
Lazy evaluation, during the intermediate processing of the stream, the operation is only recorded and will not be executed immediately. You need to wait until the termination operation is performed. Only then will the actual calculations be carried out.
Stateless: refers to the processing of elements that are not affected by previous elements;
Stateful: refers to the operation You can only continue after getting all the elements.
Non-short-circuit operation: means that all elements must be processed to get the final result;
Short-circuit operation: means that the final result can be obtained when certain elements that meet the conditions are encountered, such as A || B, As long as A is true, there is no need to judge the result of B.
Stream can be created through a collection array.
1. Use the java.util.Collection.stream() method to create a stream with a collection
List<string> list = Arrays.asList("a", "b", "c");// 创建一个顺序流
Stream<string> stream = list.stream();// 创建一个并行流
Stream<string> parallelStream = list.parallelStream();</string></string></string>
2. Use the java.util.Arrays.stream(T[]array) method to create a stream with an array Stream
int[] array={1,3,5,6,8};IntStream stream = Arrays.stream(array);
3. Use the static methods of Stream: of(), iterate(), generate()
Stream<integer> stream = Stream.of(1, 2, 3, 4, 5, 6); Stream<integer> stream2 = Stream.iterate(0, (x) -> x + 3).limit(4);stream2.forEach(System.out::println); Stream<double> stream3 = Stream.generate(Math::random).limit(3);stream3.forEach(System.out::println);</double></integer></integer>
Output results:
0 3 6 90.67961569092719940.19143142088542830.8116932592396652
Simple distinction between stream and parallelStream : Stream is a sequential stream, and the main thread performs operations on the stream in sequence, while parallelStream is a parallel stream, and the stream is operated internally in a multi-threaded parallel execution manner, but the premise is that there is no order requirement for data processing in the stream. For example, filtering odd numbers in a collection, the processing difference between the two is: 
If the amount of data in the stream is large enough, parallel streams can speed up processing.
In addition to directly creating parallel streams, you can also convert sequential streams into parallel streams through parallel():
Optional<integer> findFirst = list.stream().parallel().filter(x->x>6).findFirst();</integer>
先贴上几个案例，水平高超的同学可以挑战一下：从员工集合中筛选出salary大于8000的员工，并放置到新的集合里。统计员工的最高薪资、平均薪资、薪资之和。将员工按薪资从高到低排序，同样薪资者年龄小者在前。将员工按性别分类，将员工按性别和地区分类，将员工按薪资是否高于8000分为两部分。用传统的迭代处理也不是很难，但代码就显得冗余了，跟Stream相比高下立判。
Premise: Employee class
static List<person> personList = new ArrayList<person>();private static void initPerson() {
personList.add(new Person("张三", 8, 3000));
personList.add(new Person("李四", 18, 5000));
personList.add(new Person("王五", 28, 7000));
personList.add(new Person("孙六", 38, 9000));}</person></person>
Stream also supports similar collections It traverses and matches elements, but the elements in the Stream exist as Optional types. Stream traversal and matching are very simple.
// import已省略，请自行添加，后面代码亦是
public class StreamTest {
public static void main(String[] args) {
List<integer> list = Arrays.asList(7, 6, 9, 3, 8, 2, 1);
// 遍历输出符合条件的元素
list.stream().filter(x -> x > 6).forEach(System.out::println);
// 匹配第一个
Optional<integer> findFirst = list.stream().filter(x -> x > 6).findFirst();
// 匹配任意（适用于并行流）
Optional<integer> findAny = list.parallelStream().filter(x -> x > 6).findAny();
// 是否包含符合特定条件的元素
boolean anyMatch = list.stream().anyMatch(x -> x <h3>2、按条件匹配filter</h3>
<p><img src="https://img.php.cn/upload/article/000/000/067/c32799257ddfee7ae65dfaf7bce4b27c-5.png" alt="在这里插入Summary of detailed usage of Stream in Java8描述"></p>
<p><strong>（1）筛选员工中已满18周岁的人，并形成新的集合</strong></p>
<pre class="brush:php;toolbar:false">/**
* 筛选员工中已满18周岁的人，并形成新的集合
* @思路
* List<person> list = new ArrayList<person>();
* for(Person person : personList) {
* if(person.getAge() >= 18) {
* list.add(person);
* }
* }
*/
private static void filter01() {
initPerson();
List<person> collect = personList.stream().filter(x -> x.getAge()>=18).collect(Collectors.toList());
System.out.println(collect);}</person></person></person>
（2）自定义条件匹配
（1）获取String集合中最长的元素
/**
* 获取String集合中最长的元素
* @思路
* List<string> list = Arrays.asList("zhangsan", "lisi", "wangwu", "sunliu");
* String max = "";
* int length = 0;
* int tempLength = 0;
* for(String str : list) {
* tempLength = str.length();
* if(tempLength > length) {
* length = str.length();
* max = str;
* }
* }
* @return zhangsan
*/
private static void test02() {
List<string> list = Arrays.asList("zhangsan", "lisi", "wangwu", "sunliu");
Comparator super String> comparator = Comparator.comparing(String::length);
Optional<string> max = list.stream().max(comparator);
System.out.println(max);}</string></string></string>
（2）获取Integer集合中的最大值
//获取Integer集合中的最大值
private static void test05() {
List<integer> list = Arrays.asList(1, 17, 27, 7);
Optional<integer> max = list.stream().max(Integer::compareTo);
// 自定义排序
Optional<integer> max2 = list.stream().max(new Comparator<integer>() {
@Override
public int compare(Integer o1, Integer o2) {
return o1.compareTo(o2);
}
});
System.out.println(max2);}</integer></integer></integer></integer>
//获取员工中年龄最大的人
private static void test06() {
initPerson();
Comparator super Person> comparator = Comparator.comparingInt(Person::getAge);
Optional<person> max = personList.stream().max(comparator);
System.out.println(max);}</person>
（3）获取员工中年龄最大的人
（4）计算integer集合中大于10的元素的个数
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。
（1）字符串大写
（2）整数数组每个元素+3
/**
* 整数数组每个元素+3
* @思路
* List<integer> list = Arrays.asList(1, 17, 27, 7);
List<integer> list2 = new ArrayList<integer>();
for(Integer num : list) {
list2.add(num + 3);
}
@return [4, 20, 30, 10]
*/
private static void test09() {
List<integer> list = Arrays.asList(1, 17, 27, 7);
List<integer> collect = list.stream().map(x -> x + 3).collect(Collectors.toList());
System.out.println(collect);}</integer></integer></integer></integer></integer>
（3）公司效益好，每人涨2000
/**
* 公司效益好，每人涨2000
*
*/
private static void test10() {
initPerson();
List<person> collect = personList.stream().map(x -> {
x.setAge(x.getSalary()+2000);
return x;
}).collect(Collectors.toList());
System.out.println(collect);}</person>
（4）将两个字符数组合并成一个新的字符数组
/**
* 将两个字符数组合并成一个新的字符数组
*
*/
private static void test11() {
String[] arr = {"z, h, a, n, g", "s, a, n"};
List<string> list = Arrays.asList(arr);
System.out.println(list);
List<string> collect = list.stream().flatMap(x -> {
String[] array = x.split(",");
Stream<string> stream = Arrays.stream(array);
return stream;
}).collect(Collectors.toList());
System.out.println(collect);}</string></string></string>
（5）将两个字符数组合并成一个新的字符数组
/**
* 将两个字符数组合并成一个新的字符数组
* @return [z, h, a, n, g, s, a, n]
*/
private static void test11() {
String[] arr = {"z, h, a, n, g", "s, a, n"};
List<string> list = Arrays.asList(arr);
List<string> collect = list.stream().flatMap(x -> {
String[] array = x.split(",");
Stream<string> stream = Arrays.stream(array);
return stream;
}).collect(Collectors.toList());
System.out.println(collect);}</string></string></string>
归约，也称缩减，顾名思义，是把一个流缩减成一个值，能实现对集合求和、求乘积和求最值操作。
（1）求Integer集合的元素之和、乘积和最大值
/**
* 求Integer集合的元素之和、乘积和最大值
*
*/
private static void test13() {
List<integer> list = Arrays.asList(1, 2, 3, 4);
//求和
Optional<integer> reduce = list.stream().reduce((x,y) -> x+ y);
System.out.println("求和:"+reduce);
//求积
Optional<integer> reduce2 = list.stream().reduce((x,y) -> x * y);
System.out.println("求积:"+reduce2);
//求最大值
Optional<integer> reduce3 = list.stream().reduce((x,y) -> x>y?x:y);
System.out.println("求最大值:"+reduce3);}</integer></integer></integer></integer>
（2）求所有员工的工资之和和最高工资
/*
* 求所有员工的工资之和和最高工资
*/
private static void test14() {
initPerson();
Optional<integer> reduce = personList.stream().map(Person :: getSalary).reduce(Integer::sum);
Optional<integer> reduce2 = personList.stream().map(Person :: getSalary).reduce(Integer::max);
System.out.println("工资之和:"+reduce);
System.out.println("最高工资:"+reduce2);}</integer></integer>
取出大于18岁的员工转为map
/**
* 取出大于18岁的员工转为map
*
*/
private static void test15() {
initPerson();
Map<string> collect = personList.stream().filter(x -> x.getAge() > 18).collect(Collectors.toMap(Person::getName, y -> y));
System.out.println(collect);}</string>
Collectors提供了一系列用于数据统计的静态方法：
计数： count
平均值： averagingInt、 averagingLong、 averagingDouble
最值： maxBy、 minBy
求和： summingInt、 summingLong、 summingDouble
统计以上所有： summarizingInt、 summarizingLong、 summarizingDouble
/**
* 统计员工人数、平均工资、工资总额、最高工资
*/
private static void test01(){
//统计员工人数
Long count = personList.stream().collect(Collectors.counting());
//求平均工资
Double average = personList.stream().collect(Collectors.averagingDouble(Person::getSalary));
//求最高工资
Optional<integer> max = personList.stream().map(Person::getSalary).collect(Collectors.maxBy(Integer::compare));
//求工资之和
Integer sum = personList.stream().collect(Collectors.summingInt(Person::getSalary));
//一次性统计所有信息
DoubleSummaryStatistics collect = personList.stream().collect(Collectors.summarizingDouble(Person::getSalary));
System.out.println("统计员工人数:"+count);
System.out.println("求平均工资:"+average);
System.out.println("求最高工资:"+max);
System.out.println("求工资之和:"+sum);
System.out.println("一次性统计所有信息:"+collect);}</integer>
分区：将stream按条件分为两个 Map，比如员工按薪资是否高于8000分为两部分。
分组：将集合分为多个Map，比如员工按性别分组。有单级分组和多级分组。
将员工按薪资是否高于8000分为两部分；将员工按性别和地区分组
public class StreamTest {
public static void main(String[] args) {
personList.add(new Person("zhangsan",25, 3000, "male", "tieling"));
personList.add(new Person("lisi",27, 5000, "male", "tieling"));
personList.add(new Person("wangwu",29, 7000, "female", "tieling"));
personList.add(new Person("sunliu",26, 3000, "female", "dalian"));
personList.add(new Person("yinqi",27, 5000, "male", "dalian"));
personList.add(new Person("guba",21, 7000, "female", "dalian"));
// 将员工按薪资是否高于8000分组
Map<boolean>> part = personList.stream().collect(Collectors.partitioningBy(x -> x.getSalary() > 8000));
// 将员工按性别分组
Map<string>> group = personList.stream().collect(Collectors.groupingBy(Person::getSex));
// 将员工先按性别分组，再按地区分组
Map<string>>> group2 = personList.stream().collect(Collectors.groupingBy(Person::getSex, Collectors.groupingBy(Person::getArea)));
System.out.println("员工按薪资是否大于8000分组情况：" + part);
System.out.println("员工按性别分组情况：" + group);
System.out.println("员工按性别、地区：" + group2);
}}</string></string></boolean>
joining可以将stream中的元素用特定的连接符（没有的话，则直接连接）连接成一个字符串。
将员工按工资由高到低（工资一样则按年龄由大到小）排序
private static void test04(){
// 按工资升序排序（自然排序）
List<string> newList = personList.stream().sorted(Comparator.comparing(Person::getSalary)).map(Person::getName)
.collect(Collectors.toList());
// 按工资倒序排序
List<string> newList2 = personList.stream().sorted(Comparator.comparing(Person::getSalary).reversed())
.map(Person::getName).collect(Collectors.toList());
// 先按工资再按年龄升序排序
List<string> newList3 = personList.stream()
.sorted(Comparator.comparing(Person::getSalary).thenComparing(Person::getAge)).map(Person::getName)
.collect(Collectors.toList());
// 先按工资再按年龄自定义排序（降序）
List<string> newList4 = personList.stream().sorted((p1, p2) -> {
if (p1.getSalary() == p2.getSalary()) {
return p2.getAge() - p1.getAge();
} else {
return p2.getSalary() - p1.getSalary();
}
}).map(Person::getName).collect(Collectors.toList());
System.out.println("按工资升序排序：" + newList);
System.out.println("按工资降序排序：" + newList2);
System.out.println("先按工资再按年龄升序排序：" + newList3);
System.out.println("先按工资再按年龄自定义降序排序：" + newList4);}</string></string></string></string>
流也可以进行合并、去重、限制、跳过等操作。
private static void test05(){
String[] arr1 = { "a", "b", "c", "d" };
String[] arr2 = { "d", "e", "f", "g" };
Stream<string> stream1 = Stream.of(arr1);
Stream<string> stream2 = Stream.of(arr2);
// concat:合并两个流 distinct：去重
List<string> newList = Stream.concat(stream1, stream2).distinct().collect(Collectors.toList());
// limit：限制从流中获得前n个数据
List<integer> collect = Stream.iterate(1, x -> x + 2).limit(10).collect(Collectors.toList());
// skip：跳过前n个数据
List<integer> collect2 = Stream.iterate(1, x -> x + 2).skip(1).limit(5).collect(Collectors.toList());
System.out.println("流合并：" + newList);
System.out.println("limit：" + collect);
System.out.println("skip：" + collect2);}</integer></integer></string></string></string>
//计算两个list中的差集 List<string> reduce1 = allList.stream().filter(item -> !wList.contains(item)).collect(Collectors.toList());</string>
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