本博客系轉載,原文鏈接:https://blog.csdn.net/baidu_38083619/article/details/87891206
一個例子:
public static void main(String[] args) {
List<Student> list = Lists.newArrayList();
list.add(new Student("測試", "男", 18));
list.add(new Student("開發", "男", 20));
list.add(new Student("運維", "女", 19));
list.add(new Student("DBA", "女", 22));
list.add(new Student("運營", "男", 24));
list.add(new Student("產品", "女", 21));
list.add(new Student("經理", "女", 25));
list.add(new Student("產品", "女", 21));
//求性別爲男的學生集合
List<Student> l1 = list.stream().filter(student -> student.sex.equals("男")).collect(toList());
//map的key值true爲男,false爲女的集合
Map<Boolean, List<Student>> map = list.stream().collect(partitioningBy(student -> student.getSex().equals("男")));
//求性別爲男的學生總歲數
Integer sum = list.stream().filter(student -> student.sex.equals("男")).mapToInt(Student::getAge).sum();
//按性別進行分組統計人數
Map<String, Integer> map = list.stream().collect(Collectors.groupingBy(Student::getSex, Collectors.summingInt(p -> 1)));
//判斷是否有年齡大於25歲的學生
boolean check = list.stream().anyMatch(student -> student.getAge() > 25);
//獲取所有學生的姓名集合
List<String> l2 = list.stream().map(Student::getName).collect(toList());
//求所有人的平均年齡
double avg = list.stream().collect(averagingInt(Student::getAge));
//求年齡最大的學生
Student s = list.stream().reduce((student, student2) -> student.getAge() > student2.getAge() ? student:student2).get();
Student stu = list.stream().collect(maxBy(Comparator.comparing(Student::getAge))).get();
//按照年齡從小到大排序
List<Student> l3 = list.stream().sorted((s1, s2) -> s1.getAge().compareTo(s2.getAge())).collect(toList());
//求年齡最小的兩個學生
List<Student> l4 = l3.stream().limit(2).collect(toList());
//獲取所有的名字,組成一條語句
String str = list.stream().map(Student::getName).collect(Collectors.joining(",", "[", "]"));
//獲取年齡的最大值、最小值、平均值、求和等等
IntSummaryStatistics intSummaryStatistics = list.stream().mapToInt(Student::getAge).summaryStatistics();
System.out.println(intSummaryStatistics.getMax());
System.out.println(intSummaryStatistics.getCount());
}
@Data
@AllArgsConstructor
static class Student{
String name;
String sex;
Integer age;
}
最後給大家說一個並行化流操作parallelStream(),跟stream()的用法一樣。使用parallelStream就能立即獲得一個擁有並行能力的流,利用好並行化非常重要。不過並不是所有的流計算都要使用並行化流操作,只有當計算機是多核並且集合數據量較大(超過一萬)的時候使用並行化流操作可以提高效率。
影響性能的五要素:數據大小、源數據結構、值是否裝箱、可用的CPU數量以及處理每個元素所花的時間。
關於利用stream求和的一個例子,可以學習下這種用法:
package lambda;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
public class StreamTest {
public static void main(String[] args) {
List<List<String>> finalList = new ArrayList<>();
finalList.add(new ArrayList<>(Arrays.asList("1", "2", "3", "4")));
finalList.add(new ArrayList<>(Arrays.asList("5", "6", "7", "8")));
finalList.add(new ArrayList<>(Arrays.asList("9", "10", "11", "12")));
int checkCount1 = finalList.stream()
// .map(l -> l.size())
.map(List::size)
.reduce(0, (res, n) -> res + n); // 後面可以加.intValue()
System.out.println("checkCount1: " + checkCount1);
int checkCount2 = finalList.stream()
// .mapToInt(l -> l.size())
.mapToInt(List::size)
.sum();
System.out.println("checkCount2: " + checkCount2);
}
}