为了能让MapReduce的key/value对能够在集群中移动，MapReduce框架提供了一个序列化key/value对的方法
但MapReduce并不允许任意的类都能做为key，只有实现了WriableComparable或者Wirable接口（说明，Wriable也实现了WriableComparable接口）的类才能做为键，因为在reduce阶段要根据key来进行排序，并将相同key的值进行归并。
所以要想自己写的类能做为key的话，则此类必须实现Comparable接口。
下面我们来写一个这样的类代表两个城市之间的航
package csdn.jtlyuan; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.WritableComparable; public class Edge implements WritableComparable<Edge>{ private String startNode; private String endNode; @Override public void readFields(DataInput in) throws IOException { this.startNode=in.readUTF(); this.endNode=in.readUTF(); } @Override public void write(DataOutput out) throws IOException { out.writeUTF(this.startNode); out.writeUTF(this.endNode); } @Override public int compareTo(Edge o) { return (this.startNode.compareTo(o.startNode) != 0)? this.startNode.compareTo(o.startNode): this.endNode.compareTo(o.endNode); } }
MapReduce程序的各种阶段：
Mapper：把输入映射为key value形式
Reducer：接受来自各个mapper的输出，它按照key/value对的key进行排序，并将相同key的值归并放在同一个reducer里。
Partition：重定向Mapper输出，默认的作法是对键的散列来觉得reducer，hadoop可以通过HashPartitioner类改变这种策略。
Combiner：本地reduce
Shuffle：当第一个map任务完成后，节点可能还要继续执行更多的map任务，但这时候也开始把map任务的中间输出交换到需要它们的reducer那里去，这个移动map输出到reducer的过程叫做shuffle
举例：WordCount程序：
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; 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 { 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);//Context接受reduce阶段的输出 } } 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");//根据配置来实例化一个Job,并给Job设置名字 job.setJarByClass(WordCount.class);//Set the Jar by finding where a given class came from. job.setMapperClass(TokenizerMapper.class);//Set the Mapper for the job job.setCombinerClass(IntSumReducer.class);//Set the combiner class for the job. job.setReducerClass(IntSumReducer.class);//Set the Reducer for the job job.setOutputKeyClass(Text.class);//Set the key class for the job output data. job.setOutputValueClass(IntWritable.class);//Set the value class for job outputs. FileInputFormat.addInputPath(job, new Path(otherArgs[0]));//设置输入路径 FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//设置输出位置 (在运行之前，该目录不应该存在，否则会报错并拒绝运行该任务) System.exit(job.waitForCompletion(true) ? 0 : 1);//job.waitForCompletion Submit the job to the cluster and wait for it to finish. } }
下面的图可以清晰得开到怎样对两个文件的单词进行统计的。
