Java Stream处理数百万个元素。Map-Reduce算法需要几毫秒的时间,因此任务完成大约需要20分钟。本文主要介绍Java Stream处理大数据量信息显示处理进度信息及示例代码。

提示进度信息类似如下:

 5% (08s)
10% (14s)
15% (20s)
...

1、通过peek显示进度信息

Stream<MyData> myStream = readData();
final AtomicInteger loader = new AtomicInteger();
int fivePercent = elementsCount / 20;
MyResult result = myStream
.map(row -> process(row))
.peek(stat -> {
if (loader.incrementAndGet() % fivePercent == 0) {
System.out.println(loader.get() + " elements on " + elementsCount + " treated");
System.out.println((5*(loader.get() / fivePercent)) + "%");
}
})
.reduce(MyStat::aggregate);

2、使用map显示进度信息

import java.util.Iterator;
import java.util.Locale;
import java.util.Spliterator;
import java.util.Spliterators;
import java.util.concurrent.atomic.AtomicLong;
import java.util.function.Function;
import java.util.function.LongConsumer;
import java.util.stream.Stream;
import java.util.stream.StreamSupport;
public class StreamProgress
{
    public static void main(String[] args)
    {
        int size = 250;
        Stream<Integer> stream = readData(size);
        LongConsumer progressConsumer = progress -> 
        {
            // "Filter" the output here: Report only every 10th element
            if (progress % 10 == 0)
            {
                double relative = (double) progress / (size - 1);
                double percent = relative * 100;
                System.out.printf(Locale.ENGLISH,
                    "Progress %8d, relative %2.5f, percent %3.2f\n",
                    progress, relative, percent);
            }
        };
        Integer result = stream
            .map(element -> process(element))
            .map(progressMapper(progressConsumer))
            .reduce(0, (a, b) -> a + b);
        System.out.println("result " + result);
    }
    private static <T> Function<T, T> progressMapper(
        LongConsumer progressConsumer)
    {
        AtomicLong counter = new AtomicLong(0);
        return t -> 
        {
            long n = counter.getAndIncrement();
            progressConsumer.accept(n);
            return t;
        };
    }
    private static Integer process(Integer element)
    {
        return element * 2;
    }
    private static Stream<Integer> readData(int size)
    {
        Iterator<Integer> iterator = new Iterator<Integer>()
        {
            int n = 0;
            @Override
            public Integer next()
            {
                try
                {
                    Thread.sleep(10);
                }
                catch (InterruptedException e)
                {
                    e.printStackTrace();
                }
                return n++;
            }
            @Override
            public boolean hasNext()
            {
                return n < size;
            }
        };
        return StreamSupport.stream(
            Spliterators.spliteratorUnknownSize(
                iterator, Spliterator.ORDERED), false);
    }
}