Mapreduce字数Hadoop最高频率字

Question

因此来自Hadoop教程网站(http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html#Source_Code)关于如何使用map reduce方法实现单词计数，我了解它是如何工作的，并且输出将是具有该频率的所有单词。

我想做的是只让输出是我所拥有的输入文件中频率最高的单词。

Example: Jim Jim Jim Jim Tom Dane

我希望输出只是

Jim 4

当前的单词输出计数是每个单词及其频率。有没有人编辑过单词计数，使其只打印频率最高的单词及其频率？

有人对如何做到这一点有什么建议吗？

我该如何编写另一个MapReducer，从WordCount的输出中找到频率最高的单词？

或者还有别的办法吗？

任何帮助都将不胜感激。

谢谢！

WordCount.jave:

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 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);
  }
}

Answer 1

一种可能的方法是将减速器的数量设置为"1"。然后让一个reducer记住频率最高的单词，并在cleanup中将其写入输出。像这样：

public static class IntSumReducer
       extends Reducer<Text,IntWritable,Text,IntWritable> {
    private Text tmpWord = new Text("");
    private int tmpFrequency = 0;
    @Override
    public void reduce(Text key, Iterable<IntWritable> values,
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      if(sum > tmpFrequency) {
         tmpFrequency = sum;
         tmpWord = key;
      }
    }
    @Override
    public void cleanup(Context context) {
    // write the word with the highest frequency
        context.write(tmpWord, new IntWritable(tmpFrequency));
    }
}

Answer 2

您不可能一步到位，reduce阶段是为每个键独立执行的（不可能同步）。解决方案是运行新的MapReduce作业，将原始WordCount作业的输出聚合在一个键中，只需选择最大GL！

Answer 3

如果您只使用一个Reduce任务强制运行MapReduce，那么在代码中，您将实现对循环中所有键的主频率的搜索。

最后，循环的输出包含具有主频率的键。这对可以发送到最终输出（context.write()语句应该在最后执行一次）。