MapReduce编程实例（四），排序

排序
利用MapReduce默认的对Key进行排序
继承Partitioner类，重写getPartition使Mapper结果整体有序分到相应的Partition，
输入到Reduce分别排序。
利用全局变量统计位置

头文件：

.   import java.io.IOException;  
4.  
5.  import org.apache.hadoop.conf.Configuration;  
6.  import org.apache.hadoop.fs.Path;  
7.  import org.apache.hadoop.io.IntWritable;  
8.  import org.apache.hadoop.io.Text;  
9.  import org.apache.hadoop.mapreduce.Job;  
10. import org.apache.hadoop.mapreduce.Mapper;  
11. import org.apache.hadoop.mapreduce.Partitioner;  
12. import org.apache.hadoop.mapreduce.Reducer;  
13. import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;  
14. import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;  
15. import org.apache.hadoop.util.GenericOptionsParser;  
16.   

其他部分：

    public class Sort {  
26.     public static class SortMapper extends Mapper{  
27.
28.         //直接输出key,value，key为需要排序的值，value任意  
29.         @Override  
30.         protected void map(Object key, Text value,  
31.             Context context)throws IOException, InterruptedException {  
32.             System.out.println("Key: "+key+"  "+"Value: "+value);  
33.             context.write(new IntWritable(Integer.valueOf(value.toString())),new IntWritable(1));  
34.               
35.         }  
36.     }  
37.       
38.     public static class SortReducer extends Reducer{  
39.         public static IntWritable lineNum = new IntWritable(1);//记录该数据的位置  
40.           
41.         //查询value的个数，有多少个就输出多少个Key值。  
42.         @Override  
43.         protected void reduce(IntWritable key, Iterable value,  
44.                 Context context) throws IOException, InterruptedException {  
45.               
46.             System.out.println("lineNum: "+lineNum);  
47.               
48.             for(IntWritable i:value){  
49.                 context.write(lineNum, key);  
50.             }  
51.             lineNum = new IntWritable(lineNum.get()+1);  
52.         }  
53.     }  
54.       
55.       
56.     public static class SortPartitioner extends Partitioner{  
57.   
58.         //根据key对数据进行分派  
59.         @Override  
60.         public int getPartition(IntWritable key, IntWritable value, int partitionNum) {  
61.             System.out.println("partitionNum: "+partitionNum);  
62.             int maxnum = 23492;//输入的最大值，自己定义的。mapreduce 自带的有采样算法和partition的实现可以用，此例没有用。  
63.             int bound = maxnum/partitionNum;  
64.             int keyNum = key.get();  
65.             for(int i=0;ibound*i&&keyNum<=bound*(i+1)){  
67.                     return i;  
68.                 }  
69.             }  
70.             return -1;  
71.         }  
72. 
73.     }  
74.       
75.       
76.     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{  
77.         Configuration conf = new Configuration();  
78.         String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();  
79.           
80.         if(otherArgs.length<2){  
81.             System.out.println("input parameters errors");  
82.             System.exit(2);  
83.         }  
84.           
85.         Job job= new Job(conf);  
86.         job.setJarByClass(Sort.class);  
87.         job.setMapperClass(SortMapper.class);  
88.         job.setPartitionerClass(SortPartitioner.class);//此例不许要combiner，需要设置Partitioner  
89.         job.setReducerClass(SortReducer.class);  
90.         job.setOutputKeyClass(IntWritable.class);  
91.         job.setOutputValueClass(IntWritable.class);  
92.           
93.         FileInputFormat.addInputPath(job, new Path(otherArgs[0]));  
94.         FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));  
95.           
96.         System.exit(job.waitForCompletion(true)?0:1);  
97.     }  
98.       
99. }