样例文件hadoop.doc如下，试编写一个MapReduce程序，统计文件中的关键词Haddop出现的次数。样例文件Hadoop.doc的内容：The Hadoop documentation includes the information you need to get started using Hadoop. Begin with the Single Node Setuo which shows you how to set up a single-node Hadoop installation. Then move on to the Cluster Setup to learn how to set up a multi-node Hadoop installation

好的，这是一个典型的词频统计问题，需要编写一个MapReduce程序来解决。下面是MapReduce程序的实现过程：

1. Map阶段

对于输入文件中的每一行，我们将其切分成若干个单词。对于每个单词，我们将其作为Map的输出键，输出键值对(key, 1)，表示这个单词出现了一次。同时，我们需要过滤掉非关键词“Hadoop”的单词。

2. Reduce阶段

对于每个单词，Reduce阶段将对其所有键值对(key, 1)的值进行求和，即可得到这个单词出现的总次数。对于关键词“Hadoop”，我们就能得到它在文件中出现的次数。

下面是MapReduce程序的代码实现：

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;

public class HadoopCount {

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

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

    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        String token = itr.nextToken();
        if (token.equals("Hadoop")) {
          context.write(word, one);
        }
      }
    }
  }

  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);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "hadoop count");
    job.setJarByClass(HadoopCount.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(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

我们可以在Hadoop集群上运行该MapReduce程序，统计Hadoop.doc文件中关键词“Hadoop”出现的次数。