java 函数式接口的使用

示例1

//@FunctionalInterface 注解修饰的接口只能有一个抽象方法
@FunctionalInterface
public interface IConvert<F , T> {
    //抽象方法  将F转换为T （convert 转换）
    T convert(F f);
}

public class Something {
    // constructor methods
    Something() {
    }
    Something(String something) {
        System.out.println(something);
    }
    // static methods
    static String startsWith(String s) {
        return String.valueOf(s.charAt(0));
    }
    // object methods
    String endWith(String s) {
        return String.valueOf(s.charAt(s.length() - 1));
    }
    void endWith() {
    }
    public static void main(String[] args) {
        //访问静态方法
        IConvert<String, String> convert = Something::startsWith;
        //访问构造函数
        IConvert<String,Something>  convert1 = Something::new;
        //访问对象方法
        Something  something = new Something();
        IConvert<String,String> convert2 = something::endWith;
        /**
         * 我们可以把类Something中的方法static String startsWith(String s){...}、
         * String endWith(String s){...}、Something(String something){...}看作是接口IConvert的实现，
         * 因为它们都符合接口定义的那个“模版”，有传参类型F以及返回值类型T。比如构造方法Something(String something)，
         * 它传参为String类型，返回值类型为Something。注解@FunctionalInterface保证了接口有且仅有一个抽象方法，
         * 所以JDK能准确地匹配到相应方法。
         **/

    }
}

示例2

 //anyMatch表示，判断的条件里，任意一个元素成功，返回true
boolean t   = transactions.stream().anyMatch(s -> "Alan".equals(s.getTrader().getCity()));

Optional<Integer> sum = transactions.stream()
		.filter(s -> "Alan".equals(s.getTrader().getCity()))
       .map(Transaction::getValue)
       .reduce(Integer::sum);

示例3

range，需要传入开始节点和结束节点两个参数，返回的是一个有序的LongStream。包含开始节点和结束节点两个参数之间所有的参数，间隔为1.
差别就是rangeClosed包含最后的结束节点，range不包含。
parallel() 开启并行流 parallelStream是线程不安全的
getAsLong 返回结果为 long

long result = LongStream.rangeClosed(1, n).parallel().reduce(Long::sum).getAsLong()

函数式的运用

//Objects.requireNonNull    判断对象空值
String username = Objects.requireNonNull(textInputLayoutUsername.getEditText(), "TextInputLayout must have an EditText as child").getText().toString();

public class ParallelStreams {

    public static long iterativeSum(long n) {
        long result = 0;
        for (long i = 0; i <= n; i++) {
            result += i;
        }
        return result;
    }

    public static long sequentialSum(long n) {
        return Stream.iterate(1L, i -> i + 1).limit(n).reduce(Long::sum).get();
    }

    public static long parallelSum(long n) {
        return Stream.iterate(1L, i -> i + 1).limit(n).parallel().reduce(Long::sum).get();
    }

    public static long rangedSum(long n) {
        return LongStream.rangeClosed(1, n).reduce(Long::sum).getAsLong();
    }

    public static long parallelRangedSum(long n) {
        return LongStream.rangeClosed(1, n).parallel().reduce(Long::sum).getAsLong();
    }

    public static long sideEffectSum(long n) {
        Accumulator accumulator = new Accumulator();
        LongStream.rangeClosed(1, n).forEach(accumulator::add);
        return accumulator.total;
    }

    public static long sideEffectParallelSum(long n) {
        Accumulator accumulator = new Accumulator();
        LongStream.rangeClosed(1, n).parallel().forEach(accumulator::add);
        return accumulator.total;
    }

    public static class Accumulator {
        private long total = 0;

        public void add(long value) {
            total += value;
        }
    }
}
public class ParallelStreamsHarness {


    public static void main(String[] args) {
        System.out.println("Iterative Sum done in: " + measurePerf(ParallelStreams::iterativeSum, 10_000_000L) + " msecs");
        System.out.println("Sequential Sum done in: " + measurePerf(ParallelStreams::sequentialSum, 10_000_000L) + " msecs");
        System.out.println("Parallel forkJoinSum done in: " + measurePerf(ParallelStreams::parallelSum, 10_000_000L) + " msecs" );
        System.out.println("Range forkJoinSum done in: " + measurePerf(ParallelStreams::rangedSum, 10_000_000L) + " msecs");
        System.out.println("Parallel range forkJoinSum done in: " + measurePerf(ParallelStreams::parallelRangedSum, 10_000_000L) + " msecs" );
        System.out.println("ForkJoin sum done in: " + measurePerf(ForkJoinSumCalculator::forkJoinSum, 10_000_000L) + " msecs" );
        System.out.println("SideEffect sum done in: " + measurePerf(ParallelStreams::sideEffectSum, 10_000_000L) + " msecs" );
        System.out.println("SideEffect prallel sum done in: " + measurePerf(ParallelStreams::sideEffectParallelSum, 10_000_000L) + " msecs" );
    }

    public static <T, R> long measurePerf(Function<T, R> f, T input) {
        long fastest = Long.MAX_VALUE;
        for (int i = 0; i < 10; i++) {
            long start = System.nanoTime();
            R result = f.apply(input);
            long duration = (System.nanoTime() - start) / 1_000_000;
            System.out.println("Result: " + result);
            if (duration < fastest) fastest = duration;
        }
        return fastest;
    }
}

参考

来源：CSDN

作者：chrislin9

链接：https://blog.csdn.net/qq_31964019/article/details/103919900