MapReduce之浅析Map接口和Reduce接口

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.mapred.InputFormat;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.Partitioner;
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.GenericOptionsParser;

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 {
        String line = value.toString();
      StringTokenizer itr = new StringTokenizer(line);
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken().toLowerCase());
        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, new IntWritable(sum));
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length != 2) {
      System.err.println("Usage: wordcount <in> <out>");
      System.exit(2);
    }
    Job job = new Job(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(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

http://www.cnblogs.com/xuqiang/archive/2011/06/05/2071935.html

关键语句：

Job job = new Job(conf, "word count");//构造一个job作业

job.setMapperClass(TokenizerMapper.class);//设置job作业的map类

job.setReducerClass(IntSumReducer.class);//设置job作业的reduce类

FileInputFormat.addInputPath(job, new Path(otherArgs[0]));//设置输入路径

FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//设置输出路径

System.exit(job.waitForCompletion(true) ? 0 : 1);//等待Job完成

图：数据流程图

InputDataFormat类将行记录变成<行号，行内容>对；

Mapper类将记录行<行号，行内容>变成<键值,键对应内容>；

MapReduceFramwok框架将相同键值组合成<键值，对应内容列表>;

Reduce类中就是把<键值，对应内容列表>对变成<键值,键对应内容>；

我们所关注的是Mapper类和Reduce类

前言：数据在整体框架上能够流动是因为key和value是可以序列化和反序列化的；

value值类型通过接口Writable来定义实现；key和value值类型可以通过WritableComparalbe<T>接口实现；这些通过类实现，那么这个类就是该key和value的数据类型。

系统已经预定义实现了如下类：

同理：对于Mapper类和Reduce类

一个map类必须实现Mapper接口，一个reduce类必须实现Reduce接口；

如何实现：

重点是实现Mapper接口下的函数map；Reduce接口的reduce函数。具体原型及其代码见wordcount代码。

其中Mapper接口继承于MapReduceBase类；Reduce接口继承于MapReduceBase类。