代码设计，hadoop的单词计数功能

Hadoop的单词计数功能可以通过MapReduce编程模型来实现。具体步骤如下：

1. Mapper阶段：将输入文件划分成若干个小块，每个小块由一个Mapper进行处理。Mapper首先将输入的文本进行切分，将每个单词作为一个key，将其出现的次数作为value进行输出。

2. Shuffle阶段：将Mapper阶段输出的键值对按照key进行分组，将相同key的value合并在一起，得到一个key-value列表。

3. Reducer阶段：将Shuffle阶段输出的key-value列表按照key进行分组，然后对每个key的value进行汇总，得到最终的统计结果。

下面是一个简单的Hadoop单词计数的MapReduce程序实现：

Mapper代码：

public class WordCountMapper 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()) {
      word.set(tokenizer.nextToken());
      context.write(word, one);
    }
  }
}

Reducer代码：

public class WordCountReducer 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);
  }
}

在main函数中进行Job的配置和提交：

Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCountMapper.class);
job.setCombinerClass(WordCountReducer.class);
job.setReducerClass(WordCountReducer.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);

以上代码实现了一个简单的Hadoop单词计数程序。