Anwendung MapReduce implementieren

package org.apache.hadoop.examples;
 
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.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;
import org.apache.hadoop.util.GenericOptionsParser;
 
/**
 * Simple Word count application that defines Mapper and Reducer classes that counts the words in a give input file
 */
public class WordCount {
 
    public static class TokenizerMapper
        extends Mapper<Object, Text, Text, IntWritable>{
 
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
 
    // map function is called for every line in the input file
    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
        StringTokenizer itr = new StringTokenizer(value.toString());
        while (itr.hasMoreTokens()) {
         word.set(itr.nextToken());
 
        // Update count as 1 for every word read from input line
         context.write(word, one);
        }
    }
}
 
public static class IntSumReducer
        extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();
 
   // reduce function is called with list of values sent by map function
    public void reduce(Text key, Iterable<IntWritable> values,
                        Context context) throws IOException, InterruptedException {

        // perform necessary aggregate operation and write result to the context

        int sum = 0;
        for (IntWritable val : values) {
          sum += val.get();
        }
        result.set(sum);
        context.write(key, result);
    }
}
 
/**
 * Driver code to provide the mapper & reducer function classes for Hadoop Map Reduce framework
 */
public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length != 2) {
        System.err.println("Usage: wordcount <in> <out>");
        System.exit(2);
    }
    Job job = new Job(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}