hadoop wordmean源码及注释

wordmean是用来统计单词平均长度的程序

package org.apache.hadoop.examples;
//求单词长度的平均数

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 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
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 * See the License for the specific language governing permissions and
 * limitations under the License.
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import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import com.google.common.base.Charsets;

public class WordMean extends Configured implements Tool {

  private double mean = 0;

  private final static Text COUNT = new Text("count");
  private final static Text LENGTH = new Text("length");
  private final static LongWritable ONE = new LongWritable(1);

  /**
   * Maps words from line of text into 2 key-value pairs; one key-value pair for
   * counting the word, another for counting its length.
   */
  public static class WordMeanMapper extends
      Mapper<Object, Text, Text, LongWritable> {

    private LongWritable wordLen = new LongWritable();

    /**
     * Emits 2 key-value pairs for counting the word and its length. Outputs are
     * (Text, LongWritable).
     *
     * @param value
     *          This will be a line of text coming in from our input file.
     */
    public void map(Object key, Text value, Context context)
        throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        String string = itr.nextToken();
        this.wordLen.set(string.length());
        context.write(LENGTH, this.wordLen);//单词长度（length，len）
        context.write(COUNT, ONE);//单词次数（count，1）
      }
    }
  }

  /**
   * Performs integer summation of all the values for each key.
   */
  public static class WordMeanReducer extends
      Reducer<Text, LongWritable, Text, LongWritable> {

    private LongWritable sum = new LongWritable();

    /**
     * Sums all the individual values within the iterator and writes them to the
     * same key.
     *
     * @param key
     *          This will be one of 2 constants: LENGTH_STR or COUNT_STR.
     * @param values
     *          This will be an iterator of all the values associated with that
     *          key.
     */
    public void reduce(Text key, Iterable<LongWritable> values, Context context)
        throws IOException, InterruptedException {

      int theSum = 0;
      for (LongWritable val : values) {
        theSum += val.get();
      }
      sum.set(theSum);
      context.write(key, sum);
    }
  }
  //reduce输出两个键值对，一个为（length，总长度），另一个为（count，单词总数量）

  /**
   * Reads the output file and parses the summation of lengths, and the word
   * count, to perform a quick calculation of the mean.
   *
   * @param path
   *          The path to find the output file in. Set in main to the output
   *          directory.
   * @throws IOException
   *           If it cannot access the output directory, we throw an exception.
   */
  private double readAndCalcMean(Path path, Configuration conf)
      throws IOException {
    FileSystem fs = FileSystem.get(conf);//HDFS API
    Path file = new Path(path, "part-r-00000");

    if (!fs.exists(file))
      throw new IOException("Output not found!");

    BufferedReader br = null;

    // average = total sum / number of elements;
    try {
      br = new BufferedReader(new InputStreamReader(fs.open(file), Charsets.UTF_8));

      long count = 0;
      long length = 0;

      String line;
      while ((line = br.readLine()) != null) {
        StringTokenizer st = new StringTokenizer(line);

        // grab type
        String type = st.nextToken();//类型，count或者length

        // differentiate
        if (type.equals(COUNT.toString())) {
          String countLit = st.nextToken();//读取单词总数量
          count = Long.parseLong(countLit);//转为long
        } else if (type.equals(LENGTH.toString())) {
          String lengthLit = st.nextToken();//读取单词总长度
          length = Long.parseLong(lengthLit);//转为long
        }
      }

      double theMean = (((double) length) / ((double) count));
      System.out.println("The mean is: " + theMean);
      return theMean;
    } finally {
      if (br != null) {
        br.close();
      }
    }
  }

  public static void main(String[] args) throws Exception {
    ToolRunner.run(new Configuration(), new WordMean(), args);
  }

  @Override
  public int run(String[] args) throws Exception {
    if (args.length != 2) {
      System.err.println("Usage: wordmean  ");
      return 0;
    }

    Configuration conf = getConf();

    @SuppressWarnings("deprecation")
    Job job = new Job(conf, "word mean");
    job.setJarByClass(WordMean.class);
    job.setMapperClass(WordMeanMapper.class);
    job.setCombinerClass(WordMeanReducer.class);
    job.setReducerClass(WordMeanReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(LongWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    Path outputpath = new Path(args[1]);
    FileOutputFormat.setOutputPath(job, outputpath);
    boolean result = job.waitForCompletion(true);
    mean = readAndCalcMean(outputpath, conf);

    return (result ? 0 : 1);
  }

  /**
   * Only valuable after run() called.
   *
   * @return Returns the mean value.
   */
  public double getMean() {
    return mean;
  }
}