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。

MapReduce之浅析Map接口和Reduce接口