Performance advantages, (large data volume) compared to iterators, it is faster
Supports serial and parallel processing, Parallel processing can make full use of CPU resources
Stream is a stream of calculation data. It does not store data itself
Supports functional programming
The code is elegant, making the code more efficient, clean and concise
Three steps:
CreateStream
Intermediate operation
Termination operation
# The creation of ##Stream will depend on the data source, usually a container or an array Stream The creation of a stream is roughly divided into 4 types, the most commonly used is to create it through a collection
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.IntStream;
import java.util.stream.Stream;
public class CreateStreamDemo {
public static void main(String[] args) {
// 1 通过集合创建Stream也是用的最多的一种形式
List<String> strList = new ArrayList<>();
strList.add("a");
strList.add("b");
strList.add("c");
// 创建串行操作流
Stream<String> stream = strList.stream();
// 创建并行操作流
Stream<String> parallelStream = strList.parallelStream();
// 2 通过数组创建Stream
int[] arr = new int[]{1,2,3};
IntStream intStream = Arrays.stream(arr);
// 3 通过Stream.of
Stream<Integer> integerStream = Stream.of(1,2,3);
Stream<String> stringStream = Stream.of("a","b","c");
// 4 无限流
// 每隔五个数取一个
Stream.iterate(0, t -> t + 5).forEach(System.out::println); // 迭代
Stream.generate(Math::random).forEach(System.out::println); // 生成
}
}Stream Intermediate operations, our most commonly used ones are filtering, deduplication, and sorting. This chapter includes the most commonly used deduplication of objects and updating of objects in our development. Sorting attribute combinations
import com.zhj.java8.bean.Student;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import java.util.TreeSet;
import java.util.stream.Stream;
import static java.util.stream.Collectors.collectingAndThen;
import static java.util.stream.Collectors.toCollection;
public class MiddleStreamDemo {
public static void main(String[] args) {
List<Student> students = new ArrayList<>();
students.add(new Student(1,"小华",23,1));
students.add(new Student(1,"小华",23,2));
students.add(new Student(2,"小米",20,2));
students.add(new Student(3,"小果",30,3));
students.add(new Student(4,"小维",18,2));
// 过滤
students.stream().filter(stu -> stu.getAge() > 20).forEach(System.out::println);
// 去重
// 对对象去重是根据引用去重，内容重复并不会去重，除非重写equals和hashCode方法
System.out.println("----------去重----------");
System.out.println("去重1----------");
students.stream().distinct().forEach(System.out::println);
// 对集合中对象某些属性去重，不重写equals和hashCode方法，只能借助其他数据结构来辅助去重
// 单个属性可以stu -> stu.getId()
// 多个属性可以stu -> stu.getId() + ";" + stu.getName()
System.out.println("去重2----------");
ArrayList<Student> distinctList = students.stream().collect(
collectingAndThen(toCollection(() -> new TreeSet<>(Comparator.comparing(stu -> stu.getId() + ";" + stu.getName()))), ArrayList::new)
);
distinctList.stream().forEach(System.out::println);
// 排序 支持定义排序方式
// sorted 默认使用 自然序排序， 其中的元素必须实现Comparable 接口
System.out.println("----------排序----------");
System.out.println("排序1----------");
students.stream().sorted().forEach(System.out::println);
// sorted(Comparator<? super T> comparator) ：我们可以使用lambada 来创建一个Comparator 实例。可以按照升序或着降序来排序元素。
System.out.println("排序2----------");
students.stream()
.sorted(Comparator.comparing(Student::getAge,Comparator.reverseOrder())) // ,Comparator.reverseOrder() 逆序
.forEach(System.out::println);
// 创建比较器，通过对比较器内容的定义实现对多个属性进行排序，类似sql中连续的orderBy
System.out.println("排序3----------");
students.stream().sorted(
(s1,s2) -> {
if (s1.getAge() == s2.getAge()) {
return s1.getSex().compareTo(s2.getSex());
} else {
return -s1.getAge().compareTo(s2.getAge());
}
}
).forEach(System.out::println);
System.out.println("排序4----------");
Comparator<Student> studentComparator = (s1,s2) -> {
Integer age1 = s1.getAge();
Integer age2 = s2.getAge();
if (age1 != age2) return age1 - age2;
Integer sex1 = s1.getSex();
Integer sex2 = s2.getSex();
if (sex1 != sex2) return sex2 - sex1;
return 0;
};
students.stream().sorted(studentComparator).forEach(System.out::println);
// 截取 截取前三个元素
System.out.println("----------截取----------");
students.stream().limit(3).forEach(System.out::println);
// 跳过 跳过前3个元素
System.out.println("----------跳过----------");
students.stream().skip(3).forEach(System.out::println);
// 映射
System.out.println("----------映射----------");
System.out.println("映射Map----------");
// map接收Lambda,将元素转换其他形式，或者是提取信息，并将其映射成一个新的元素
Stream<Stream<Student>> streamStream1 = students.stream().map(str -> filterStudent(str));
streamStream1.forEach(sm -> sm.forEach(System.out::println));
System.out.println("映射flatMap----------");
// map接收Lambda,将流中的每一个元素转换成另一个流，然后把所有流连成一个流 扁平化映射
Stream<Student> studentStream2 = students.stream().flatMap(str -> filterStudent(str));
studentStream2.forEach(System.out::println);
// 消费
System.out.println("----------消费----------");
students.stream().peek(stu -> stu.setAge(100)).forEach(System.out::println);
}
public static Stream<Student> filterStudent(Student student) {
student = new Student();
return Stream.of(student);
}
}Student
public class Student implements Comparable<Student> {
private Integer id;
private String name;
private Integer age;
private Integer sex;
public Student() {
}
public Student(Integer id, String name, Integer age, Integer sex) {
this.id = id;
this.name = name;
this.age = age;
this.sex = sex;
}
public Integer getId() {
return id;
}
public void setId(Integer id) {
this.id = id;
}
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public Integer getAge() {
return age;
}
public void setAge(Integer age) {
this.age = age;
}
public Integer getSex() {
return sex;
}
public void setSex(Integer sex) {
this.sex = sex;
}
@Override
public String toString() {
return "Student{" +
"id='" + id + '\'' +
", name='" + name + '\'' +
", age='" + age + '\'' +
", sex=" + sex +
'}';
}
@Override
public int compareTo(Student o) {
return this.getAge() - o.getAge();
}
}The termination operation of Stream , the most commonly used The purpose is to collect the processed data into a new container, and at the same time, it can achieve some effects of grouping to Sql aggregation functions
package com.zhj.java8.stream;
import com.zhj.java8.bean.Student;
import java.util.*;
import java.util.stream.Collectors;
public class TerminationStreamDemo {
public static void main(String[] args) {
List<Student> students = new ArrayList<>();
students.add(new Student(1,"小华",23,1));
students.add(new Student(2,"小米",20,2));
students.add(new Student(3,"小果",30,3));
students.add(new Student(4,"小维",18,2));
students.add(new Student(5,"小华",23,2));
System.out.println("--------------------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
boolean allMatch = students.stream().allMatch(stu -> stu.getAge() > 10);
System.out.println("全部符合大于10岁条件：" + allMatch);
// 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
boolean noneMatch = students.stream().noneMatch(stu -> stu.getAge() > 10);
System.out.println("全部不符合大于10岁条件：" + noneMatch);
// 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
boolean anyMatch = students.stream().anyMatch(stu -> stu.getAge() > 20);
System.out.println("含有任意符合大于20岁条件：" + anyMatch);
// findFirst：返回流中第一个元素
Student findFirst = students.stream().findFirst().get();
System.out.println("第一个学生：" + findFirst);
// findAny：返回流中的任意元素
Student findAny = students.stream().findAny().get();
System.out.println("任意一个学生：" + findAny);
// count：返回流中元素的总个数
long count = students.stream().count();
System.out.println("学生总数：" + count);
// max：返回流中元素最大值
Student max = students.stream().max(Student::compareTo).get();
System.out.println("年龄最大学生：" + max);
// max：返回流中元素最大值
Student min = students.stream().min(Student::compareTo).get();
System.out.println("年龄最小学生：" + min);
System.out.println("--------------------规约操作--------------------");
System.out.println("学生年龄总和：" + students.stream().map(Student::getAge).reduce(Integer::sum));
System.out.println("学生年龄最大：" + students.stream().map(Student::getAge).reduce(Integer::max));
System.out.println("--------------------收集操作--------------------");
List<Student> list = students.stream().collect(Collectors.toList());
Set<Student> set = students.stream().collect(Collectors.toSet());
Map<Integer, String> map = students.stream().collect(Collectors.toMap(Student::getId, Student::getName));
String joinName = students.stream().map(Student::getName).collect(Collectors.joining(",", "(", ")"));
// 总数
students.stream().collect(Collectors.counting());
// 最大年龄
students.stream().map(Student::getAge).collect(Collectors.maxBy(Integer::compare)).get();
// 年龄和
students.stream().collect(Collectors.summingInt(Student::getAge));
// 平均年龄
students.stream().collect(Collectors.averagingDouble(Student::getAge));
// 信息合集
DoubleSummaryStatistics statistics = students.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>> collect = students.stream().collect(Collectors.groupingBy(Student::getSex));
System.out.println(collect);
//多重分组,先根据性别分再根据年龄分
Map<Integer, Map<Integer, List<Student>>> typeAgeMap = list.stream().collect(Collectors.groupingBy(Student::getSex, Collectors.groupingBy(Student::getAge)));
//分区
//分成两部分，一部分大于20岁，一部分小于等于20岁
Map<Boolean, List<Student>> partMap = list.stream().collect(Collectors.partitioningBy(v -> v.getAge() > 20));
//规约
Integer allAge = list.stream().map(Student::getAge).collect(Collectors.reducing(Integer::sum)).get();
System.out.println(allAge);
}
}Lazy execution of intermediate operations
If there are multiple intermediate operations, they will not cycle multiple times.Multiple conversion operations will only be merged when the operation is terminated, and one cycle will be completed.
Exception: stream has already been operated upon or closed
stream is closed and cannot be called again. If we want to call it repeatedly, we can only reopen a new stream.
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