package com.fly.stream;import java.security.SecureRandom;import java.util.*;import java.util.stream.Collectors;import java.util.stream.Stream;/*** Stream极大的简化了集合的操作,相比较Collection Stream用于处理集合之间的计算,是面向于CPU的,通过 * CPU实现计算,而Collection是一种静态的内存数据结构,它主要是面向内存的。Stream具有如下特性* (1).Stream不会存储数据。* (2).Stream不会改变源对象,相反,他们会返回一个持有结果的新Stream* (3).Stream的操作是延迟执行的。这意味着他们会等到需要结果的时候才执行。* (4).Stream一旦被关闭就无法再使用。Stream还有一个中间状态,例如用于对源数据的处理,例如分组、过滤等等操作*/public class Test01 {static List<Emp> empList=new ArrayList<>();static Random random=new SecureRandom();static {empList.add(new Emp(1001,"刘强东",230,7000.0));empList.add(new Emp(1002,"马云",20,3000.0));empList.add(new Emp(1003,"马化腾",50,4500.0));empList.add(new Emp(1004,"马超",10,10000.0));empList.add(new Emp(1005,"张一鸣",30,12000.0));empList.add(new Emp(1005,"张一鸣",30,12000.0));empList.add(new Emp(1006,"张三鸣",90,12000.0));empList.add(new Emp(1007,"司马大圣",111,100000.0));empList.add(new Emp(1008,"欧阳神奇",880,20000.0));}public static void main(String[] args) {method01();method02();}/*** 创建Stream*/public static void method01(){/*** 方式1:通过集合创建Stream,列举了List、Set、Map数据结构创建Stream*///获取一个Stream顺序流,在读取数据时会顺序读取Stream<String> stream01 = new ArrayList<String>().stream();//获取一个Stream并行流,在读取数据时会像多线程并行读取,在处理大量数据时能提供处理效率Stream<String> stream02 = new ArrayList<String>().parallelStream();Stream<String> stream03=new HashSet<String>().stream();Stream<Map.Entry<String, String>> stream04=new HashMap<String,String>().entrySet().stream();/*** 方式2:通过数组创建Stream,本质上通过Arrays类进行转换*/Stream<String> stream05=Arrays.stream(new String[]{"1","2"});/*** 方式3:通过Stream的静态方法创建Stream* of(T t):of方法可以将参数转为Stream,本质上还是Arrays.stream(values)。* empty():创建一个空的Stream。* generate(Supplier<T> s):生成流数据。* iterate(final T seed, final UnaryOperator<T> f):通过迭代生出流数据。* concat(Stream<? extends T> a, Stream<? extends T> b):连接2个Stream*/Stream<String> stream06=Stream.of();Stream<String> stream07=Stream.empty();Stream<Integer> stream08 = Stream.generate(new SecureRandom()::nextInt);Stream<Integer> stream09=Stream.iterate(0,t->t+2);Stream<Integer> stream10=Stream.concat(Stream.empty(),Stream.empty());}/*** Stream Api* filter(Predicate<? super T> predicate):根据布尔值表达式过滤数据。* forEach(Consumer<? super T> action):遍历Stream处理的数据。* limit(long maxSize):limit用于截断数据,maxSize为截断的最大长度,limit(3)表示截断Stream中前三个元素。* skip(long n):跳过元素,此方法会返回n条之后的数据,不包含第n条。* distinct():排除Stream中重复的数据。* map(Function<? super T, ? extends R> mapper):遍历数据,并根据表达式返回给Stream。* count():获取Stream中剩余数据的长度。* sorted(Comparator<? super T> comparator):根据comparator表达式进行排序。* max(Comparator<? super T> comparator):根据表达式获取Stream值最大的元素,返回一个Optional对象。* min(Comparator<? super T> comparator):根据表达式获取Stream值最小的元素,返回一个Optional对象。* findFirst():返回Stream第一个数据。* findAny():返回Stream任意一个数据。* allMatch(Predicate<? super T> predicate):根据表达式判断Stream中所有的元素是否匹配,全部匹配返回true,否则返回false。* anyMatch(Predicate<? super T> predicate):根据表达式判断Stream中匹配元素,只要有一个元素匹配成功就返回true,否则返回false。* noneMatch(Predicate<? super T> predicate):根据表达式判断Stream中所有的元素是否都不匹配,全部都不匹配返回true,否则返回false。* reduce(T identity, BinaryOperator<T> accumulator):可以将流中的元素反复结合起来,得到一个值并返回元素累加之后的值。* flatMap:将多维数据拍平* collect:collect允许将流转换为其他形式。*/public static void method02(){System.out.println("--------------filter------------------------");//过滤工资大于7000的员工empList.stream().filter(emp->emp.getSalary() > 7000).forEach(System.out::println);System.out.println("--------------forEach------------------------");empList.stream().forEach(emp->{System.out.println("id:"+emp.getId()+",name:"+emp.getName());});System.out.println("--------------limit------------------------");//截取Stream中前三个元素empList.stream().limit(3).forEach(System.out::println);System.out.println("--------------skip------------------------");//跳过前三条元素,返回第三条之后所有的数据,不含第三条empList.stream().skip(3).forEach(System.out::println);System.out.println("--------------distinct------------------------");empList.stream().distinct().forEach(System.out::println);System.out.println("--------------map------------------------");//遍历Stream根据数据项的名称,返回一个新的Stream,然后过滤map返回的Stream中名字长度大于3的员工empList.stream().map(emp->emp.getName()).filter(s->s.length()>3).forEach(System.out::println);System.out.println("--------------count()------------------------");long count=empList.stream().filter(emp->emp.getSalary()>7000).count();System.out.println("Stream的长度:"+count);System.out.println("--------------sorted()------------------------");//根据员工的工资进行降序empList.stream().sorted((emp1,emp2)->Double.compare(emp2.getSalary(),emp1.getSalary())).forEach(System.out::println);System.out.println("--------------max()------------------------");//查找员工集合中工资最高的Optional<Emp> max = empList.stream().max(Comparator.comparingDouble(Emp::getSalary));System.out.println(max);System.out.println("--------------min()------------------------");//查找员工集合中工资最低的Optional<Emp> min = empList.stream().min(Comparator.comparingDouble(Emp::getSalary));System.out.println(min);System.out.println("--------------findFirst()------------------------");Optional<Emp> first = empList.stream().findFirst();System.out.println(first);System.out.println("--------------findAny()------------------------");Optional<Emp> findAny = empList.stream().findAny();System.out.println(findAny);System.out.println("--------------allMatch()------------------------");//判断员工集合中所有人的工资是否都大于7000boolean bool1 = empList.stream().allMatch(emp -> emp.getSalary() > 7000);System.out.println(bool1);System.out.println("--------------anyMatch()------------------------");//判断员工集合中任意一个人的工资是否大于7000boolean bool2 = empList.stream().anyMatch(emp -> emp.getSalary() > 7000);System.out.println(bool2);//trueSystem.out.println("--------------noneMatch()------------------------");//判断员工集合中所有人的工资是否都小于7000000,与allMatch相反。boolean bool3 = empList.stream().noneMatch(emp -> emp.getSalary() > 7000000);System.out.println(bool3);//falseSystem.out.println("--------------reduce()------------------------");//计算员工工资之和,0.0为遍历项的初识值Double salarySum01 = empList.stream().map(Emp::getSalary).reduce(0.0, (emp1, emp2) -> emp1 + emp2);System.out.println(salarySum01);//计算员工工资之和简写Double salarySum02 = empList.stream().map(Emp::getSalary).reduce(0.0, Double::sum);System.out.println(salarySum02);System.out.println("--------------flatMap()------------------------");List<String> fun1 = Arrays.asList("one", "two", "three");List<String> fun2 = Arrays.asList("four", "five", "six");List<List<String>> nestedList = Arrays.asList(fun1, fun2);nestedList.stream().flatMap(x -> x.stream()).map(x->x.toUpperCase()).forEach(System.out::println);System.out.println("--------------collect()------------------------");//Stream转ListList<Emp> collectList = empList.stream().collect(Collectors.toList());//Stream转SetSet<Emp> collectSet = empList.stream().collect(Collectors.toSet());//Stream转Map,如果有相同的id就会转换失败,这里加了个随机数Map<Integer, String> collectMap = empList.stream().collect(Collectors.toMap(e->e.getId()+random.nextInt(), Emp::getName));System.out.println(collectList);System.out.println(collectSet);System.out.println(collectMap);}}
package com.fly.stream;import java.util.Objects;public class Emp {private Integer id;private String name;private Integer age;private Double salary;public Emp(Integer id, String name, Integer age, Double salary) {this.id = id;this.name = name;this.age = age;this.salary = salary;}@Overridepublic String toString() {return "Emp{" +"id=" + id +", name='" + name + '\'' +", age=" + age +", salary=" + salary +'}';}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 Double getSalary() {return salary;}public void setSalary(Double salary) {this.salary = salary;}@Overridepublic boolean equals(Object o) {if (this == o) {return true;}if (o == null || getClass() != o.getClass()) {return false;}Emp emp = (Emp) o;return id.equals(emp.id) &&name.equals(emp.name) &&age.equals(emp.age) &&salary.equals(emp.salary);}@Overridepublic int hashCode() {return Objects.hash(id, name, age, salary);}}
执行结果:
--------------filter------------------------Emp{id=1004, name='马超', age=10, salary=10000.0}Emp{id=1005, name='张一鸣', age=30, salary=12000.0}Emp{id=1005, name='张一鸣', age=30, salary=12000.0}Emp{id=1006, name='张三鸣', age=90, salary=12000.0}Emp{id=1007, name='司马大圣', age=111, salary=100000.0}Emp{id=1008, name='欧阳神奇', age=880, salary=20000.0}--------------forEach------------------------id:1001,name:刘强东id:1002,name:马云id:1003,name:马化腾id:1004,name:马超id:1005,name:张一鸣id:1005,name:张一鸣id:1006,name:张三鸣id:1007,name:司马大圣id:1008,name:欧阳神奇--------------limit------------------------Emp{id=1001, name='刘强东', age=230, salary=7000.0}Emp{id=1002, name='马云', age=20, salary=3000.0}Emp{id=1003, name='马化腾', age=50, salary=4500.0}--------------skip------------------------Emp{id=1004, name='马超', age=10, salary=10000.0}Emp{id=1005, name='张一鸣', age=30, salary=12000.0}Emp{id=1005, name='张一鸣', age=30, salary=12000.0}Emp{id=1006, name='张三鸣', age=90, salary=12000.0}Emp{id=1007, name='司马大圣', age=111, salary=100000.0}Emp{id=1008, name='欧阳神奇', age=880, salary=20000.0}--------------distinct------------------------Emp{id=1001, name='刘强东', age=230, salary=7000.0}Emp{id=1002, name='马云', age=20, salary=3000.0}Emp{id=1003, name='马化腾', age=50, salary=4500.0}Emp{id=1004, name='马超', age=10, salary=10000.0}Emp{id=1005, name='张一鸣', age=30, salary=12000.0}Emp{id=1006, name='张三鸣', age=90, salary=12000.0}Emp{id=1007, name='司马大圣', age=111, salary=100000.0}Emp{id=1008, name='欧阳神奇', age=880, salary=20000.0}--------------map------------------------司马大圣欧阳神奇--------------count()------------------------Stream的长度:6--------------sorted()------------------------Emp{id=1007, name='司马大圣', age=111, salary=100000.0}Emp{id=1008, name='欧阳神奇', age=880, salary=20000.0}Emp{id=1005, name='张一鸣', age=30, salary=12000.0}Emp{id=1005, name='张一鸣', age=30, salary=12000.0}Emp{id=1006, name='张三鸣', age=90, salary=12000.0}Emp{id=1004, name='马超', age=10, salary=10000.0}Emp{id=1001, name='刘强东', age=230, salary=7000.0}Emp{id=1003, name='马化腾', age=50, salary=4500.0}Emp{id=1002, name='马云', age=20, salary=3000.0}--------------max()------------------------Optional[Emp{id=1007, name='司马大圣', age=111, salary=100000.0}]--------------min()------------------------Optional[Emp{id=1002, name='马云', age=20, salary=3000.0}]--------------findFirst()------------------------Optional[Emp{id=1001, name='刘强东', age=230, salary=7000.0}]--------------findAny()------------------------Optional[Emp{id=1001, name='刘强东', age=230, salary=7000.0}]--------------allMatch()------------------------false--------------anyMatch()------------------------true--------------noneMatch()------------------------true--------------reduce()------------------------180500.0180500.0--------------flatMap()------------------------ONETWOTHREEFOURFIVESIX--------------collect()------------------------[Emp{id=1001, name='刘强东', age=230, salary=7000.0}, Emp{id=1002, name='马云', age=20, salary=3000.0}, Emp{id=1003, name='马化腾', age=50, salary=4500.0}, Emp{id=1004, name='马超', age=10, salary=10000.0}, Emp{id=1005, name='张一鸣', age=30, salary=12000.0}, Emp{id=1005, name='张一鸣', age=30, salary=12000.0}, Emp{id=1006, name='张三鸣', age=90, salary=12000.0}, Emp{id=1007, name='司马大圣', age=111, salary=100000.0}, Emp{id=1008, name='欧阳神奇', age=880, salary=20000.0}][Emp{id=1003, name='马化腾', age=50, salary=4500.0}, Emp{id=1007, name='司马大圣', age=111, salary=100000.0}, Emp{id=1001, name='刘强东', age=230, salary=7000.0}, Emp{id=1008, name='欧阳神奇', age=880, salary=20000.0}, Emp{id=1006, name='张三鸣', age=90, salary=12000.0}, Emp{id=1005, name='张一鸣', age=30, salary=12000.0}, Emp{id=1004, name='马超', age=10, salary=10000.0}, Emp{id=1002, name='马云', age=20, salary=3000.0}]{961248248=张一鸣, 572791696=司马大圣, 1924575248=欧阳神奇, 1467755674=张一鸣, 1536218504=马云, 422561622=马化腾, 1763656883=张三鸣, -1076432150=刘强东, -1822377697=马超}
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