java - 将WholeFileInputFormat与Hadoop MapReduce结合使用仍会导致Mapper一次处理1行

要在使用Hadoop 2.6 ..时扩展我的 header ，并且需要将整个文件发送到我的映射器，而不是一次发送一行。我已经按照《权威指南》中的Tom Whites代码创建了WholeFileInputFormat和WholeFileRecordReader，但是我的Mapper仍在一次处理1行文件。谁能看到我的代码中缺少的内容？我所看到的完全是本书的例子。任何指导将不胜感激。

WholeFileInputFormat.java

public class WholeFileInputFormat extends FileInputFormat <NullWritable, BytesWritable>{

@Override
protected boolean isSplitable(JobContext context, Path file){
    return false;
}

@Override
public RecordReader<NullWritable, BytesWritable> createRecordReader(
        InputSplit split, TaskAttemptContext context) throws IOException, InterruptedException {
    WholeFileRecordReader reader = new WholeFileRecordReader();
    reader.initialize(split, context);
    return reader;
}

}

WholeFileRecordReader.java

public class WholeFileRecordReader extends RecordReader<NullWritable, BytesWritable> {
private FileSplit fileSplit;
private Configuration conf;
private BytesWritable value = new BytesWritable();
private boolean processed = false;

@Override
public void initialize(InputSplit split, TaskAttemptContext context) throws IOException, InterruptedException{
    this.fileSplit = (FileSplit) split;
    this.conf = context.getConfiguration();
}

@Override
public boolean nextKeyValue() throws IOException, InterruptedException{
    if (!processed){
        byte[] contents = new byte[(int) fileSplit.getLength()];
        Path file = fileSplit.getPath();
        FileSystem fs = file.getFileSystem(conf);
        FSDataInputStream in = null;
        try{
            in = fs.open(file);
            IOUtils.readFully(in, contents, 0, contents.length);
            value.set(contents, 0, contents.length);
        }finally{
            IOUtils.closeStream(in);
        }
        processed = true;
        return  true;
    }
    return false;
}

@Override
public NullWritable getCurrentKey() throws IOException, InterruptedException{
    return NullWritable.get();
}

@Override
public BytesWritable getCurrentValue() throws IOException, InterruptedException{
    return value;
}

@Override
public float getProgress() throws IOException {
    return processed ? 1.0f : 0.0f;
}

@Override
public void close() throws IOException{
    //do nothing :)
}

}

而我的Mapreduce的主要方法

public class ECCCount {
public static void main(String[] args) throws Exception {

    if (args.length != 2) {
      System.out.printf("Usage: ProcessLogs <input dir> <output dir>\n");
      System.exit(-1);
    }

    //@SuppressWarnings("deprecation")
    Job job = new Job();
    job.setJarByClass(ECCCount.class);
    job.setJobName("ECCCount");

    //FileInputFormat.setInputPaths(job, new Path(args[0]));
    WholeFileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    job.setMapperClass(ECCCountMapper.class);
    job.setReducerClass(SumReducer.class);

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

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

}

和我的Mapper很好。现在，它只是简单地返回作为测试用例给出的值，以查看其返回的是一行还是整个文件

public class ECCCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
@Override
  public void map(LongWritable key, Text value, Context context)
      throws IOException, InterruptedException {

      context.write(new Text(value), new IntWritable(1));
  }

}

最佳答案

问题可以是映射器的输入格式。您有LongWritable和文本。但是，在上述示例中，他们使用了NullWritable，BytesWritable，因为这就是WholeFileInputFormat所具有的。另外，您需要给 job.setInputFormatClass(WholeFileInputFormat.class); Job类中的(主要方法)。希望对您有所帮助，祝您编码愉快