1. 实现Runnable线程案例
使用() -> {} 替代匿名类:
//Before Java 8: new Thread(new Runnable() { @Override public void run() { System.out.println("Before Java8 "); } }).start(); //Java 8 way: new Thread( () -> System.out.println("In Java8!") ).start(); Output: too much code, for too little to do Lambda expression rocks !!
你可以使用 下面语法实现Lambda:
(params) -> expression
(params) -> statement
(params) -> { statements }
如果你的方法并不改变任何方法参数,比如只是输出,那么可以简写如下:
() -> System.out.println("Hello Lambda Expressions");
如果你的方法接受两个方法参数,如下:
(int even, int odd) -> even + odd
2.实现事件处理
如果你曾经做过Swing 编程,你将永远不会忘记编写事件侦听器代码。使用lambda表达式如下所示写出更好的事件侦听器的代码。
// Before Java 8: JButton show = new JButton("Show"); show.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent e) { System.out.println("without lambda expression is boring"); } }); // Java 8 way: show.addActionListener((e) -> { System.out.println("Action !! Lambda expressions Rocks"); });
在java 8中你可以使用Lambda表达式替代丑陋的匿名类。
3.使用Lambda表达式遍历List集合
//Prior Java 8 : List features = Arrays.asList("Lambdas", "Default Method", "Stream API", "Date and Time API"); for (String feature : features) { System.out.println(feature); } //In Java 8: List features = Arrays.asList("Lambdas", "Default Method", "Stream API", "Date and Time API"); features.forEach(n -> System.out.println(n)); // Even better use Method reference feature of Java 8 // method reference is denoted by :: (double colon) operator // looks similar to score resolution operator of C++ features.forEach(System.out::println); Output: Lambdas Default Method Stream API Date and Time API
方法引用是使用两个冒号::这个操作符号。
4.使用Lambda表达式和函数接口
为了支持函数编程,Java 8加入了一个新的包java.util.function,其中有一个接口java.util.function.Predicate是支持Lambda函数编程:
public static void main(args[]){ List languages = Arrays.asList("Java", "Scala", "C++", "Haskell", "Lisp"); System.out.println("Languages which starts with J :"); filter(languages, (str)->str.startsWith("J")); System.out.println("Languages which ends with a "); filter(languages, (str)->str.endsWith("a")); System.out.println("Print all languages :"); filter(languages, (str)->true); System.out.println("Print no language : "); filter(languages, (str)->false); System.out.println("Print language whose length greater than 4:"); filter(languages, (str)->str.length() > 4); } public static void filter(List names, Predicate condition) { for(String name: names) { if(condition.test(name)) { System.out.println(name + " "); } } } } Output: Languages which starts with J : Java Languages which ends with a Java Scala Print all languages : Java Scala C++ Haskell Lisp Print no language : Print language whose length greater than 4: Scala Haskell //Even better public static void filter(List names, Predicate condition) { names.stream().filter((name) -> (condition.test(name))) .forEach((name) -> {System.out.println(name + " "); }); }
你能看到来自Stream API 的filter方法能够接受 Predicate参数, 能够允许测试多个条件。
5.复杂的结合Predicate 使用
java.util.function.Predicate提供and(), or() 和 xor()可以进行逻辑操作,比如为了得到一串字符串中以"J"开头的4个长度:
// We can even combine Predicate using and(), or() And xor() logical functions // for example to find names, which starts with J and four letters long, you // can pass combination of two Predicate Predicate<String> startsWithJ = (n) -> n.startsWith("J"); Predicate<String> fourLetterLong = (n) -> n.length() == 4; names.stream() .filter(startsWithJ.and(fourLetterLong)) .forEach((n) -> System.out.print("\nName, which starts with 'J' and four letter long is : " + n));
其中startsWithJ.and(fourLetterLong)是使用了AND逻辑操作。
6.使用Lambda实现Map 和 Reduce
最流行的函数编程概念是map,它允许你改变你的对象,在这个案例中,我们将costBeforeTeax集合中每个元素改变了增加一定的数值,我们将Lambda表达式 x -> x*x传送map()方法,这将应用到stream中所有元素。然后我们使用 forEach() 打印出这个集合的元素.
// applying 12% VAT on each purchase // Without lambda expressions: List costBeforeTax = Arrays.asList(100, 200, 300, 400, 500); for (Integer cost : costBeforeTax) { double price = cost + .12*cost; System.out.println(price); } // With Lambda expression: List costBeforeTax = Arrays.asList(100, 200, 300, 400, 500); costBeforeTax.stream().map((cost) -> cost + .12*cost) .forEach(System.out::println); Output 112.0 224.0 336.0 448.0 560.0 112.0 224.0 336.0 448.0 560.0
reduce() 是将集合中所有值结合进一个,Reduce类似SQL语句中的sum(), avg() 或count(),
// Applying 12% VAT on each purchase// Old way:List costBeforeTax =Arrays.asList(100, 200, 300, 400, 500); double total =0; for (Integer cost :costBeforeTax) { double price = cost + .12*cost; total = total + price; } System.out.println("Total : " + total); // New way:List costBeforeTax =Arrays.asList(100, 200, 300, 400, 500); double bill = costBeforeTax.stream().map((cost) -> cost + .12*cost) .reduce((sum, cost) -> sum + cost) .get(); System.out.println("Total : " + bill); OutputTotal :1680.0Total:1680.0
7.通过filtering 创建一个字符串String的集合
Filtering是对大型Collection操作的一个通用操作,Stream提供filter()方法,接受一个Predicate对象,意味着你能传送lambda表达式作为一个过滤逻辑进入这个方法:
// Create a List with String more than 2 characters List<String> filtered = strList.stream().filter(x -> x.length()> 2) .collect(Collectors.toList()); System.out.printf("Original List : %s, filtered list : %s %n", strList, filtered); Output : Original List : [abc, , bcd, , defg, jk], filtered list : [abc, bcd, defg]
8.对集合中每个元素应用函数
我们经常需要对集合中元素运用一定的功能,如表中的每个元素乘以或除以一个值等等.
// Convert String to Uppercase and join them using coma List<String> G7 = Arrays.asList("USA", "Japan", "France", "Germany", "Italy", "U.K.","Canada"); String G7Countries = G7.stream().map(x -> x.toUpperCase()) .collect(Collectors.joining(", ")); System.out.println(G7Countries); Output : USA, JAPAN, FRANCE, GERMANY, ITALY, U.K., CANADA
上面是将字符串转换为大写,然后使用逗号串起来。
9.通过复制不同的值创建一个子列表
使用Stream的distinct()方法过滤集合中重复元素。
// Create List of square of all distinct numbers List<Integer> numbers = Arrays.asList(9, 10, 3, 4, 7, 3, 4); List<Integer> distinct = numbers.stream().map( i -> i*i).distinct() .collect(Collectors.toList()); System.out.printf("Original List : %s, Square Without duplicates : %s %n", numbers, distinct); Output : Original List : [9, 10, 3, 4, 7, 3, 4], Square Without duplicates : [81, 100, 9, 16, 49]
10.计算List中的元素的最大值,最小值,总和及平均值
//Get count, min, max, sum, and average for numbers List<Integer> primes = Arrays.asList(2, 3, 5, 7, 11, 13, 17, 19, 23, 29); IntSummaryStatistics stats = primes.stream().mapToInt((x) -> x) .summaryStatistics(); System.out.println("Highest prime number in List : " + stats.getMax()); System.out.println("Lowest prime number in List : " + stats.getMin()); System.out.println("Sum of all prime numbers : " + stats.getSum()); System.out.println("Average of all prime numbers : " + stats.getAverage()); Output : Highest prime number in List : 29 Lowest prime number in List : 2 Sum of all prime numbers : 129 Average of all prime numbers : 12.9