Java8-Stream:2 万字 20 个实例,玩转集合的筛选
最长的字符串:weoujgsd
案例二:获取 Integer 集合中的最大值。
public class StreamTest {public static void main(String[] args) {List<Integer> list = Arrays.asList(7, 6, 9, 4, 11, 6); // 自然排序 Optional<Integer> max = list.stream().max(Integer::compareTo); // 自定义排序 Optional<Integer> max2 = list.stream().max(new Comparator<Integer>() {@Overridepublic int compare(Integer o1, Integer o2) {return o1.compareTo(o2);}});System.out.println("自然排序的最大值:" + max.get());System.out.println("自定义排序的最大值:" + max2.get());}}
输出结果:
自然排序的最大值:11 自定义排序的最大值:11
案例三:获取员工工资最高的人。
public class StreamTest {public static void main(String[] args) {List<Person> personList = new ArrayList<Person>();personList.add(new Person("Tom", 8900, 23, "male", "New York"));personList.add(new Person("Jack", 7000, 25, "male", "Washington"));personList.add(new Person("Lily", 7800, 21, "female", "Washington"));personList.add(new Person("Anni", 8200, 24, "female", "New York"));personList.add(new Person("Owen", 9500, 25, "male", "New York"));personList.add(new Person("Alisa", 7900, 26, "female", "New York"));
Optional<Person> max = personList.stream().max(Comparator.comparingInt(Person::getSalary));System.out.println("员工工资最大值:" + max.get().getSalary());}}
输出结果:
员工工资最大值:9500
案例四:计算 Integer 集合中大于 6 的元素的个数。
import java.util.Arrays;import java.util.List;
public class StreamTest {public static void main(String[] args) {List<Integer> list = Arrays.asList(7, 6, 4, 8, 2, 11, 9);
long count = list.stream().filter(x -> x > 6).count();System.out.println("list 中大于 6 的元素个数:" + count);}}
输出结果:
list 中大于 6 的元素个数:4
3.4 映射(map/flatMap)
映射,可以将一个流的元素按照一定的映射规则映射到另一个流中。分为 map 和 flatMap:
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。
案例一:英文字符串数组的元素全部改为大写。整数数组每个元素+3。
public class StreamTest {public static void main(String[] args) {String[] strArr = { "abcd", "bcdd", "defde", "fTr" };List<String> strList = Arrays.stream(strArr).map(String::toUpperCase).collect(Collectors.toList());
List<Integer> intList = Arrays.asList(1, 3, 5, 7, 9, 11);List<Integer> intListNew = intList.stream().map(x -> x + 3).collect(Collectors.toList());
System.out.println("每个元素大写:" + strList);System.out.println("每个元素+3:" + intListNew);}}
输出结果:
每个元素大写:[ABCD, BCDD, DEFDE, FTR] 每个元素+3:[4, 6, 8, 10, 12, 14]
案例二:将员工的薪资全部增加 1000。
public class StreamTest {public static void main(String[] args) {List<Person> personList = new ArrayList<Person>();personList.add(new Person("Tom", 8900, 23, "male", "New York"));personList.add(new Person("Jack", 7000, 25, "male", "Washington"));personList.add(new Person("Lily", 7800, 21, "female", "Washington"));personList.add(new Person("Anni", 8200, 24, "female", "New York"));personList.add(new Person("Owen", 9500, 25, "male", "New York"));personList.add(new Person("Alisa", 7900, 26, "female", "New York")); // 不改变原来员工集合的方式 List<Person> personListNew = personList.stream().map(person -> {Person personNew = new Person(person.getName(), 0, 0, null, null);personNew.setSalary(person.getSalary() + 10000);return personNew;}).collect(Collectors.toList());System.out.println("一次改动前:" + personList.get(0).getName() + "-->" + personList.get(0).getSalary());System.out.println("一次改动后:" + personListNew.get(0).getName() + "-->" + personListNew.get(0).getSalary()); // 改变原来员工集合的方式 List<Person> personListNew2 = personList.stream().map(person -> {person.setSalary(person.getSalary() + 10000);return person;}).collect(Collectors.toList());System.out.println("二次改动前:" + personList.get(0).getName() + "-->" + personListNew.get(0).getSalary());System.out.println("二次改动后:" + personListNew2.get(0).getName() + "-->" + personListNew.get(0).getSalary());}}
输出结果:
一次改动前:Tom–>8900 一次改动后:Tom–>18900 二次改动前:Tom–>18900 二次改动后:Tom–>18900
案例三:将两个字符数组合并成一个新的字符数组。
public class StreamTest {public static void main(String[] args) {List<String> list = Arrays.asList("m,k,l,a", "1,3,5,7");List<String> listNew = list.stream().flatMap(s -> { // 将每个元素转换成一个 stream String[] split = s.split(",");Stream<String> s2 = Arrays.stream(split);return s2;}).collect(Collectors.toList());
System.out.println("处理前的集合:" + list);System.out.println("处理后的集合:" + listNew);}}
输出结果:
处理前的集合:[m-k-l-a, 1-3-5] 处理后的集合:[m, k, l, a, 1, 3, 5]
3.5 归约(reduce)
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。
案例一:求 Integer 集合的元素之和、乘积和最大值。
public class StreamTest {public static void main(String[] args) {List<Integer> list = Arrays.asList(1, 3, 2, 8, 11, 4); // 求和方式 1 Optional<Integer> sum = list.stream().reduce((x, y) -> x + y); // 求和方式 2 Optional<Integer> sum2 = list.stream().reduce(Integer::sum); // 求和方式 3 Integer sum3 = list.stream().reduce(0, Integer::sum); // 求乘积 Optional<Integer> product = list.stream().reduce((x, y) -> x * y); // 求最大值方式 1 Optional<Integer> max = list.stream().reduce((x, y) -> x > y ? x : y); // 求最大值写法 2 Integer max2 = list.stream().reduce(1, Integer::max);
System.out.println("list 求和:" + sum.get() + "," + sum2.get() + "," + sum3);System.out.println("list 求积:" + product.get());System.out.println("list 求和:" + max.get() + "," + max2);}}
输出结果:
list 求和:29,29,29 list 求积:2112 list 求和:11,11
案例二:求所有员工的工资之和和最高工资。
public class StreamTest {public static void main(String[] args) {List<Person> personList = new ArrayList<Person>();personList.add(new Person("Tom", 8900, 23, "male", "New York"));personList.add(new Person("Jack", 7000, 25, "male", "Washington"));personList.add(new Person("Lily", 7800, 21, "female", "Washington"));personList.add(new Person("Anni", 8200, 24, "female", "New York"));personList.add(new Person("Owen", 9500, 25, "male", "New York"));personList.add(new Person("Alisa", 7900, 26, "female", "New York")); // 求工资之和方式 1: Optional<Integer> sumSalary = personList.stream().map(Person::getSalary).reduce(Integer::sum); // 求工资之和方式 2: Integer sumSalary2 = personList.stream().reduce(0, (sum, p) -> sum += p.getSalary(),(sum1, sum2) -> sum1 + sum2); // 求工资之和方式 3: Integer sumSalary3 = personList.stream().reduce(0, (sum, p) -> sum += p.getSalary(), Integer::sum); // 求最高工资方式 1: Integer maxSalary = personList.stream().reduce(0, (max, p) -> max > p.getSalary() ? max : p.getSalary(),Integer::max); // 求最高工资方式 2: Integer maxSalary2 = personList.stream().reduce(0, (max, p) -> max > p.getSalary() ? max : p.getSalary(),(max1, max2) -> max1 > max2 ? max1 : max2);
System.out.println("工资之和:" + sumSalary.get() + "," + sumSalary2 + "," + sumSalary3);System.out.println("最高工资:" + maxSalary + "," + maxSalary2);}}
输出结果:
工资之和:49300,49300,49300 最高工资:9500,9500
3.6 收集(collect)
collect,收集,可以说是内容最繁多、功能最丰富的部分了。从字面上去理解,就是把一个流收集起来,最终可以是收集成一个值也可以收集成一个新的集合。
collect 主要依赖 java.util.stream.Collectors 类内置的静态方法。
3.6.1 归集(toList/toSet/toMap)
因为流不存储数据,那么在流中的数据完成处理后,需要将流中的数据重新归集到新的集合里。toList、toSet 和 toMap 比较常用,另外还有 toCollection、toConcurrentMap 等复杂一些的用法。
下面用一个案例演示 toList、toSet 和 toMap:
public class StreamTest {public static void main(String[] args) {List<Integer> list = Arrays.asList(1, 6, 3, 4, 6, 7, 9, 6, 20);List<Integer> listNew = list.stream().filter(x -> x % 2 == 0).collect(Collectors.toList());Set<Integer> set = list.stream().filter(x -> x % 2 == 0).collect(Collectors.toSet());
List<Person> personList = new ArrayList<Person>();personList.add(new Person("Tom", 8900, 23, "male", "New York"));personList.add(new Person("Jack", 7000, 25, "male", "Washington"));personList.add(new Person("Lily", 7800, 21, "female", "Washington"));personList.add(new Person("Anni", 8200, 24, "female", "New York"));
Map<?, Person> map = personList.stream().filter(p -> p.getSalary() > 8000).collect(Collectors.toMap(Person::getName, p -> p));System.out.println("toList:" + listNew);System.out.println("toSet:" + set);System.out.println("toMap:" + map);}}
运行结果:
toList:[6, 4, 6, 6, 20] toSet:[4, 20, 6] toMap:{Tom=mutest.Person@5fd0d5ae, Anni=mutest.Person@2d98a335}
3.6.2 统计(count/averaging)
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
案例:统计员工人数、平均工资、工资总额、最高工资。
public 《一线大厂 Java 面试题解析+后端开发学习笔记+最新架构讲解视频+实战项目源码讲义》无偿开源 威信搜索公众号【编程进阶路】 class StreamTest {public static void main(String[] args) {List<Person> personList = new ArrayList<Person>();personList.add(new Person("Tom", 8900, 23, "male", "New York"));personList.add(new Person("Jack", 7000, 25, "male", "Washington"));personList.add(new Person("Lily", 7800, 21, "female", "Washington")); // 求总数 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("员工总数:" + count);System.out.println("员工平均工资:" + average);System.out.println("员工工资总和:" + sum);System.out.println("员工工资所有统计:" + collect);}}
运行结果:
员工总数:3 员工平均工资:7900.0 员工工资总和:23700 员工工资所有统计:DoubleSummaryStatistics{count=3, sum=23700.000000,min=7000.000000, average=7900.000000, max=8900.000000}
3.6.3 分组(partitioningBy/groupingBy)
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。
案例: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) {List<Person> personList = new ArrayList<Person>();personList.add(new Person("Tom", 8900, "male", "New York"));personList.add(new Person("Jack", 7000, "male", "Washington"));personList.add(new Person("Lily", 7800, "female", "Washington"));personList.add(new Person("Anni", 8200, "female", "New York"));personList.add(new Person("Owen", 9500, "male", "New York"));personList.add(new Person("Alisa", 7900, "female", "New York")); // 将员工按薪资是否高于 8000 分组 Map<Boolean, List<Person>> part = personList.stream().collect(Collectors.partitioningBy(x -> x.getSalary() > 8000)); // 将员工按性别分组 Map<String, List<Person>> 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, Map<String, List<Person>>> 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);}}
输出结果:
员工按薪资是否大于 8000 分组情况:{false=[mutest.Person@2d98a335, mutest.Person@16b98e56, mutest.Person@7ef20235], true=[mutest.Person@27d6c5e0, mutest.Person@4f3f5b24, mutest.Person@15aeb7ab]}员工按性别分组情况:{female=[mutest.Person@16b98e56, mutest.Person@4f3f5b24, mutest.Person@7ef20235], male=[mutest.Person@27d6c5e0, mutest.Person@2d98a335, mutest.Person@15aeb7ab]}员工按性别、地区:{female={New York=[mutest.Person@4f3f5b24, mutest.Person@7ef20235], Washington=[mutest.Person@16b98e56]}, male={New York=[mutest.Person@27d6c5e0, mutest.Person@15aeb7ab], Washington=[mutest.Person@2d98a335]}}
3.6.4 接合(joining)
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。
public class StreamTest {public static void main(String[] args) {List<Person> personList = new ArrayList<Person>();personList.add(new Person("Tom", 8900, 23, "male", "New York"));personList.add(new Person("Jack", 7000, 25, "male", "Washington"));personList.add(new Person("Lily", 7800, 21, "female", "Washington"));
String names = personList.stream().map(p -> p.getName()).collect(Collectors.joining(","));System.out.println("所有员工的姓名:" + names);List<String> list = Arrays.asList("A", "B", "C");String string = list.stream().collect(Collectors.joining("-"));System.out.println("拼接后的字符串:" + string);}}
运行结果:
所有员工的姓名:Tom,Jack,Lily 拼接后的字符串:A-B-C
3.6.5 归约(reducing)
Collectors 类提供的 reducing 方法,相比于 stream 本身的 reduce 方法,增加了对自定义归约的支持。
public class StreamTest {public static void main(String[] args) {List<Person> personList = new ArrayList<Person>();personList.add(new Person("Tom", 8900, 23, "male", "New York"));personList.add(new Person("Jack", 7000, 25, "male", "Washington"));personList.add(new Person("Lily", 7800, 21, "female", "Washington")); // 每个员工减去起征点后的薪资之和(这个例子并不严谨,但一时没想到好的例子) Integer sum = personList.stream().collect(Collectors.reducing(0, Person::getSalary, (i, j) -> (i + j - 5000)));System.out.println("员工扣税薪资总和:" + sum); // stream 的 reduce Optional<Integer> sum2 = personList.stream().map(Person::getSalary).reduce(Integer::sum);System.out.println("员工薪资总和:" + sum2.get());}}
运行结果:
员工扣税薪资总和:8700 员工薪资总和:23700
3.7 排序(sorted)
sorted,中间操作。有两种排序:
sorted():自然排序,流中元素需实现 Comparable 接口
sorted(Comparator com):Comparator 排序器自定义排序
案例:将员工按工资由高到低(工资一样则按年龄由大到小)排序
public class StreamTest {public static void main(String[] args) {List<Person> personList = new ArrayList<Person>();
personList.add(new Person("Sherry", 9000, 24, "female", "New York"));personList.add(new Person("Tom", 8900, 22, "male", "Washington"));personList.add(new Person("Jack", 9000, 25, "male", "Washington"));personList.add(new Person("Lily", 8800, 26, "male", "New York"));personList.add(new Person("Alisa", 9000, 26, "female", "New York")); // 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);}}
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