我试图修改这段代码以生成一个完整的倒排列表。我的意思是，获取文件位置中每个单词的索引。如果我们有两个包含单词的文件

abc.txt =    I am coming to the park to play, yes i am.

  def.txt = Please come on over, i will be waiting for you

我应该有这样的东西：

i /home/abc.txt: 1 10 /home/def.txt: 5

这意味着字母i是.txt文件中的第一个和第十个单词，是def.txt文件中的第五个单词
我修改了代码以提供“单词位置和单词频率”，如下所示：

import java.io.IOException;
import java.util.*;

import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.*;
import org.apache.hadoop.mapreduce.lib.output.*;
import org.apache.hadoop.util.*;

public class WordCountByFile extends Configured implements Tool {

    public static void main(String args[]) throws Exception {
        String[] argsLocal = {
            "input#2", "output#2"
        };
        int res = ToolRunner.run(new WordCountByFile(), argsLocal);
        System.exit(res);
    }

    public int run(String[] args) throws Exception {
        Path inputPath = new Path(args[0]);
        Path outputPath = new Path(args[1]);

        Configuration conf = getConf();
        Job job = new Job(conf, this.getClass().toString());

        FileInputFormat.setInputPaths(job, inputPath);
        FileOutputFormat.setOutputPath(job, outputPath);

        job.setJobName("WordCountByFile");
        job.setJarByClass(WordCountByFile.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        job.setMapperClass(Map.class);
        job.setCombinerClass(Reduce.class);
        job.setReducerClass(Reduce.class);

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

    public static class Map extends Mapper < LongWritable, Text, Text, 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 {
            String line = value.toString();
            StringTokenizer tokenizer = new StringTokenizer(line);
            while (tokenizer.hasMoreTokens()) {

                String filePathString = ((FileSplit) context.getInputSplit()).getPath().toString();

                word.set(tokenizer.nextToken() + " " + filePathString + " : ");
                context.write(word, one);
            }
        }
    }

    public static class Reduce extends Reducer < Text, IntWritable, Text, IntWritable > {

        @Override
        public void reduce(Text key, Iterable < IntWritable > values, Context context) 
            throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable value: values) {
                sum += value.get();
            }
            context.write(key, new IntWritable(sum));
        }
    }
}

我知道它必须和一些索引一起使用，比如在java中，但是我正在尝试在hadoop map reduce中如何做到这一点。有人帮忙吗？