代码设计,hadoop的单词计数功能

Hadoop的单词计数功能主要由两个部分组成：Map和Reduce。

首先是Map阶段，Map任务的输入是一个文本文件，每一行是一个字符串。Map任务会对每一行进行处理，将字符串分割为单词，并对每个单词进行标记，然后将<单词，1>键值对输出。

接下来是Reduce阶段，Reduce任务的输入是Map任务输出的键值对。Reduce任务会对相同的单词进行聚合，并将<单词，总数>键值对输出。

下面是Hadoop单词计数功能的代码示例（使用Java语言）：

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

  public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      context.write(key, new IntWritable(sum));
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();

    Job job = Job.getInstance(conf, "wordcount");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(Map.class);
    job.setCombinerClass(Reduce.class);
    job.setReducerClass(Reduce.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);
  }
}

在这段代码中，Map任务继承自Hadoop中的Mapper类，Reduce任务继承自Hadoop中的Reducer类。在Main函数中，我们设置了Map和Reduce任务的类，并指定了输入和输出文件路径。最后，我们调用job.waitForCompletion方法来运行作业。