hadoop 2.8 简单数字排序

package mapreduce;

import java.io.IOException;

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.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;


public class Sort {
	public static class Map extends Mapper<Object, Text, IntWritable, IntWritable>{
		private static IntWritable data = new IntWritable();
		
		public void map(Object key,Text value, Context context) throws IOException,InterruptedException{
			String line =value.toString();
			data.set(Integer.parseInt(line));
			context.write(data, new IntWritable(1));
		}
	}
	
	public static class Reduce extends Reducer<IntWritable ,IntWritable, IntWritable,IntWritable>{
		private static IntWritable linenum = new IntWritable(1);
		
		public void reduce(IntWritable key,Iterable<IntWritable> values,Context context) throws IOException,InterruptedException{
			for(IntWritable val:values)
			{
				context.write(linenum, key);
				linenum = new IntWritable(linenum.get()+1);
			}
		}
	}
	
	
	public static class Partition extends Partitioner<IntWritable,IntWritable>{
		@Override
		public int getPartition(IntWritable key, IntWritable value, int numPartitions){
			int maxnumber = 65536;
			int minnumber = -3000;
			int bound = (maxnumber-minnumber)/numPartitions+1;
			int keynumber = key.get();
			for(int i=1;i<=numPartitions;i++){
				if(keynumber < minnumber){
					return 0;
				}
				if(keynumber>=minnumber && keynumber<minnumber+i*bound){
					return i;
				}
			}
			return numPartitions+1;
		}
	}
	
	public static void main(String[] args) throws Exception{
		Configuration conf = new Configuration();
		Job job = Job.getInstance(conf, "Sort");
		job.setJarByClass(Sort.class);
		job.setMapperClass(Map.class);
		job.setReducerClass(Reduce.class);
		job.setPartitionerClass(Partition.class);
		job.setOutputKeyClass(IntWritable.class);
		job.setOutputValueClass(IntWritable.class);
		FileInputFormat.addInputPath(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
	
}

Hadoop在Reduct之前会自动对所有的元组进行基于Key的排序(数字从小到大哦,字符串按字母顺序), 上面这段代码正是利用了自动排序的这点, reduce里的:

linenum = new IntWritable(linenum.get()+1)

是给重复出现的数字排序的.

此外重载了Partition类， 这是在map之前，分配各个mapper处理数据的，比如0-10000000的数字给一号机来排序，

10000000-20000000的数字给二号机来排序，以此类推， 通过这样实现了Hadoop分而治之的思想。