hadoop WordCount

Hadoop WordCount是一个经典的MapReduce程序，用于对文本数据进行词频统计。实现方法如下：

1. Map阶段：将文本数据按照指定的分隔符进行切分，将每个单词作为Key，将频率作为Value传递给Reduce阶段。

2. Reduce阶段：将Map阶段传递过来的Key-Value对进行合并，统计每个单词在文本中出现的次数，输出最终的词频统计结果。

以下是Hadoop WordCount的Java代码示例：

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();

    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());
        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, "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(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

以上代码中，TokenizerMapper类实现了Map阶段的逻辑，IntSumReducer类实现了Reduce阶段的逻辑，main函数用于配置和提交MapReduce作业。