我尝试在一个简单的mapreduce任务中从arraywritable获取输出。我发现了一些类似问题的问题，但我无法用自己的代码解决这个问题。所以我期待着你的帮助。谢谢：）！
输入：文本文件和一些句子。
输出应为：

<Word, <length, number of same words in Textfile>>
 Example: Hello  5  2

我在工作中得到的结果是：

hello WordLength_V01$IntArrayWritable@221cf05
test WordLength_V01$IntArrayWritable@799e525a

我认为问题出在intarraywritable的子类中，但是我没有得到正确的更正来解决这个问题。顺便说一下，我们有hadoop 2.5，我使用以下代码得到这个结果：
主要方法：

public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "word length V1");

    // Set Classes
    job.setJarByClass(WordLength_V01.class);
    job.setMapperClass(MyMapper.class);
    // job.setCombinerClass(MyReducer.class);
    job.setReducerClass(MyReducer.class);

    // Set Output and Input Parameters
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(IntWritable.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntArrayWritable.class);

    // Number of Reducers
    job.setNumReduceTasks(1);

    // Set FileDestination
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    System.exit(job.waitForCompletion(true) ? 0 : 1);
}

Map器：

public static class MyMapper extends Mapper<Object, Text, Text, IntWritable> {

    // Initialize Variables
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    // Map Method
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {

        // Use Tokenizer
        StringTokenizer itr = new StringTokenizer(value.toString());

        // Select each word
        while (itr.hasMoreTokens()) {
            word.set(itr.nextToken());

            // Output Pair
            context.write(word, one);
        }
    }
}

减速器：

public static class MyReducer extends Reducer<Text, IntWritable, Text, IntArrayWritable> {

    // Initialize Variables
    private IntWritable count = new IntWritable();
    private IntWritable length = new IntWritable();

    // Reduce Method
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {

        // Count Words
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }

        count.set(sum);

        // Wordlength
        length.set(key.getLength());

        // Define Output
        IntWritable[] temp = new IntWritable[2];
        IntArrayWritable output = new IntArrayWritable(temp);

        temp[0] = count;
        temp[1] = length;

        // Output
        output.set(temp);
        context.write(key, new IntArrayWritable(output.get()));
    }
}

子类

public static class IntArrayWritable extends ArrayWritable {
    public IntArrayWritable(IntWritable[] intWritables) {
        super(IntWritable.class);
    }

    @Override
    public IntWritable[] get() {
        return (IntWritable[]) super.get();
    }

    @Override
    public void write(DataOutput arg0) throws IOException {
        for(IntWritable data : get()){
            data.write(arg0);
        }
    }
}

我使用以下链接找到解决方案：
可写接口（hadoop.apache.org）
类arraywritable（hadoop.apache.org）
stackoverflow.com（1）
stackoverflow.com（2）
我真的很感激你的任何想法！
--------解决方案--------
新建子类：

public static class IntArrayWritable extends ArrayWritable {

    public IntArrayWritable(IntWritable[] values) {
        super(IntWritable.class, values);
    }

    @Override
    public IntWritable[] get() {
        return (IntWritable[]) super.get();
    }

    @Override
    public String toString() {
        IntWritable[] values = get();
        return values[0].toString() + ", " + values[1].toString();
    }
}

新reduce方法：

public void reduce(Text key, Iterable<IntWritable> values,
            Context context) throws IOException, InterruptedException {

        // Count Words
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }

        count.set(sum);

        // Wordlength
        length.set(key.getLength());

        // Define Output
        IntWritable[] temp = new IntWritable[2];
        temp[0] = count;
        temp[1] = length;

        context.write(key, new IntArrayWritable(temp));
}