11.ForkJoin

ForkJoin

ForkJoin在jdk1.7，并行执行任务！提高效率，大数据量！

大数据：Map Reduce（把大任务拆分成小任务）

ForkJoin特点：工作窃取（如下图）

ForkJoin操作

如何使用。累加求和

1. forkJoinPool来执行

2. 计算任务forkJoinPool.execute(ForkJoinTask<?> task)

3. ForkJoinTask.计算任务要集成此类
   

1.四大函数式接口，Function,Predicate,Consumer,Supplier

2.forkJoinPool拆分式计算，demo测试

public class Test {
    public static void main(String[] args) throws ExecutionException, InterruptedException {
        //test1();//普通for循环 328
        //test2();//forkjoin 4813
        test3();//并行流，247
    }

    //普通
    public static void test1() {
        long start = System.currentTimeMillis();
        long sum = 0L;
        for (long i = 1L; i <= 10_0000_0000L; i++) {
            sum += i;
        }
        long end = System.currentTimeMillis();
        System.out.println("时间：" + (end - start) + "，sum=" + sum);
    }

    //forkjoin
    public static void test2() throws ExecutionException, InterruptedException {
        long start = System.currentTimeMillis();
        ForkJoinPool forkJoinPool = new ForkJoinPool();
        ForkJoinTask<Long> submit = forkJoinPool.submit(new ForkJoinDemo(1L, 10_0000_0000L));
        Long sum = submit.get();
        long end = System.currentTimeMillis();
        System.out.println("时间：" + (end - start) + "，sum=" + sum);
    }

    //并行流
    public static void test3() {
        long start = System.currentTimeMillis();
        long sum = 0L;
        sum = LongStream.rangeClosed(0L, 10_0000_0000L).parallel().reduce(0, Long::sum);
        long end = System.currentTimeMillis();
        System.out.println("时间：" + (end - start) + "，sum=" + sum);
    }
}