map/reduce实现 排序

 import java.io.IOException;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.conf.Configured;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.io.IntWritable;
 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.Partitioner;
 import org.apache.hadoop.mapreduce.Reducer;
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
 import org.apache.hadoop.util.Tool;
 import org.apache.hadoop.util.ToolRunner;
 public class Sort extends Configured implements Tool {
     /*
      * 排序
      * 输入格式：每个数据占一行
      * 输出格式：
      * 1 21
      * 2 32
      * 3 62
      * 设计思路：
      * 使用reduce自带的默认排序规则。MapReduce按照key值进行排序。如果Key值为Intwritable类型，则按照数字大小排序
      * 如果key值为Text类型，则按照字典顺序对字符串进行排序。
      * 注意：要重写Partition函数。Reduce排序只能保证自己局部的数据顺序，并不能保证全局的。
      * */
     public static class Map extends Mapper<LongWritable,Text,IntWritable,IntWritable>{
         private IntWritable line=new IntWritable();
         public void map(LongWritable key,Text value,Context context)throws IOException,InterruptedException{
             line.set(Integer.parseInt(value.toString()));
             context.write(line, new IntWritable(1));
         }

     }

     public static class Reduce extends Reducer<IntWritable,IntWritable,IntWritable,IntWritable>{
         private IntWritable num=new IntWritable(1);
         public void reduce(IntWritable key,Iterable<IntWritable> values,Context context)throws IOException,InterruptedException{
             for(IntWritable var:values){
             context.write(num, key);
             num=new IntWritable(num.get()+1);
             }
         }

     }

     public static class Partition extends Partitioner<IntWritable ,IntWritable>{

         @Override
         public int getPartition(IntWritable key, IntWritable value, int numPartitions) {
             // TODO Auto-generated method stub
             System.out.println(numPartitions);
             int maxnum=65223;
             int bound=maxnum/numPartitions+1;
             for(int i=0;i<numPartitions;i++)
             {
                 if(key.get()>=bound*(i-1)&&key.get()<=bound*i)
                 {
                     return i;
                 }
             }
             return 0;
         }

     }

     public int run(String[] args)throws Exception{
         Configuration conf=new Configuration();
         Job job=new Job(conf,"Sort");
         job.setJarByClass(Sort.class);

         job.setOutputKeyClass(IntWritable.class);
         job.setOutputValueClass(IntWritable.class);

         job.setMapperClass(Map.class);
         job.setReducerClass(Reduce.class);
         job.setPartitionerClass(Partition.class);

         job.setInputFormatClass(TextInputFormat.class);
         job.setOutputFormatClass(TextOutputFormat.class);

         FileInputFormat.addInputPath(job, new Path(args[0]));
         FileOutputFormat.setOutputPath(job, new Path(args[1]));

         boolean success=job.waitForCompletion(true);
         return success?0:1;
     }

     public static void main(String[] args)throws Exception{
         int ret=ToolRunner.run(new Sort(), args);
         System.exit(ret);
     }

 }