Java MapReduce Hadoop字长频率不工作

关于这一页，我和他有一个类似的问题。我需要提供一个映射和减少方法来计算字长（1到n）频率。我已经尝试了答案的方法来实现这一点

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCount {

  //Mapper which implement the mapper() function
  public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
  //public static class TokenizerMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable> {

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

    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        //check whether word is start from a or b
        String wordToCheck = itr.nextToken();
        word.set(String.valueOf(wordToCheck.length()));
        context.write(word, one);
        //if (wordToCheck.startsWith("a")||wordToCheck.startsWith("b")){
        //  word.set(wordToCheck);
        //  context.write(word, one);
        //}
        //check for word length
        //if (wordToCheck.length() > 8) {
        // }
      }
    }
  }
  //Reducer which implement the reduce() function
  public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }
  //Driver class to specific the Mapper and Reducer
  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

---

我在EclipseKepler中开发了这个类，并在UbuntuLTXTerminal中使用Hadoop2.6.3将这个类作为jar文件运行。有什么问题？我也试着按照答案中的建议使用IntWritable，但是，它也有类似的反应。

我不是100%确定，但当您使用文件作为输入时，mapper应该为键（对应于文件中的行号）键入

LongWritable

，为值键入

Text

（文件行为文本）

因此，可能的解决办法是更换

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

公共静态类TokenizerMapper扩展映射器{

与

公共静态类TokenizerMapper扩展映射器{

问题回答得很好。非常感谢。

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

public static class TokenizerMapper extends Mapper<LongWritable, Text, Text, IntWritable> {