在hadoop里面处理的数据，默认按输入内容的key进行排序的

在hadoop里面处理的数据，默认按输入内容的key进行排序的，大部分情况下，都可以满足的我们的业务需求，但有时候，可能出现类似以下的需求，输入内容：

Java代码  

1. 秦东亮;72  
2. 秦东亮;34  
3. 秦东亮;100  
4. 三劫;899  
5. 三劫;32  
6. 三劫;1  
7. a;45  
8. b;567  
9. b;12  

秦东亮;72
秦东亮;34
秦东亮;100
三劫;899
三劫;32
三劫;1
a;45
b;567
b;12

要求输出1：

Java代码  

1. a   45  
2. b   12,567  
3. 三劫  1,32,899  
4. 秦东亮 34,72,100  

a	45
b	12,567
三劫	1,32,899
秦东亮	34,72,100

要求输出2：

Java代码  

1. a   45  
2. b   12  
3. b   567  
4. 三劫  1  
5. 三劫  32  
6. 三劫  899  
7. 秦东亮 34  
8. 秦东亮 72  
9. 秦东亮 100  

a	45
b	12
b	567
三劫	1
三劫	32
三劫	899
秦东亮	34
秦东亮	72
秦东亮	100

注意上面的输出1，和输出2，其实都是一样的逻辑，只不过，输出的形式稍微改了下，那么今天散仙，就来分析下，怎么在hadoop里面，实现这样的需求。

其实这样的需求，就类似数据库的标准SQL分组
SELECT A,B FROM TABLE GROUP BY  A,B ORDER BY A,B
当然也不一定，是2个字段分组，可能有2个或2个以上的多个字段分组。
下面，我们先来看下MapReduce内部执行2次排序的流程图，这图是散仙收集的，画的很不错。



由上图可知，Map在处理数据时，先由InputFormat组件提供输入格式，然后Split一行数据，默认的是TextInputFormat，Key为字节偏移量，Value为内容，然后把这行数据，传给Map，Map根据某种约定的分隔符，进行拆分数据，进行业务处理，如果是计数的直接在Value上输出1，在Map输出前，如果有Combine组件，则会执行Combine阶段，进行本地Reduce，一般是用来优化程序用的，Combine执行完后，会执行Partition组件，进行数据分区，默认的是HashPartition，按照输出的Key的哈希值与上Integer的最大值，然后对reduce的个数进行取余得到的值，经过Partition后，数据就会被按桶输出到本地磁盘上，在输出的时候，会按照Key进行排序，然后等所有的Map执行完毕后，就会进入Reduce阶段，这个阶段会进行一个大的混洗阶段，术语叫shuffle，每个reduce都会去每个map输出的分区里面，拉取对应的一部分数据，这个时候，是最耗网络IO，以及磁盘IO的，是影响性能的一个重要瓶颈，当Reduce把所有的数据拉取完毕后，就会进行分组并按照Key进行排序，每处理好一个分组，都会调用一次Reduce函数，进行累加，或其他的业务处理，处理完毕后，就会通过OutputFormat进行输出到HDFS上，至此，整个流程就执行完毕。


代码如下：

Java代码  

1. package com.qin.groupsort;  
2.   
3. import java.io.DataInput;  
4. import java.io.DataOutput;  
5. import java.io.IOException;  
6.   
7. import org.apache.hadoop.fs.FileSystem;  
8. import org.apache.hadoop.fs.Path;  
9. import org.apache.hadoop.io.IntWritable;  
10. import org.apache.hadoop.io.LongWritable;  
11. import org.apache.hadoop.io.Text;  
12. import org.apache.hadoop.io.WritableComparable;  
13. import org.apache.hadoop.io.WritableComparator;  
14. import org.apache.hadoop.mapred.JobConf;  
15. import org.apache.hadoop.mapreduce.Job;  
16. import org.apache.hadoop.mapreduce.Mapper;  
17. import org.apache.hadoop.mapreduce.Partitioner;  
18. import org.apache.hadoop.mapreduce.Reducer;  
19. import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;  
20. import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;  
21. import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;  
22. import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;  
23. import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;  
24. import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;  
25.   
26. import com.qin.operadb.PersonRecoder;  
27. import com.qin.operadb.ReadMapDB;  
28.    
29.   
30. /** 
31.  * @author qindongliang 
32.  *  
33.  * 大数据交流群：376932160 
34.  *  
35.  *  
36.  * **/  
37. public class GroupSort {  
38.       
39.     /** 
40.      * map任务 
41.      *  
42.      * */  
43.     public static class GMapper extends Mapper<LongWritable, Text, DescSort, IntWritable>{  
44.               
45.           
46.         private DescSort tx=new DescSort();  
47.         private IntWritable second=new IntWritable();  
48.           
49.         @Override  
50.         protected void map(LongWritable key, Text value,Context context)  
51.                 throws IOException, InterruptedException {  
52.             System.out.println("执行map");  
53.             // System.out.println("进map了");  
54.             //mos.write(namedOutput, key, value);  
55.             String ss[]=value.toString().split(";");  
56.             String mkey=ss[0];  
57.             int mvalue=Integer.parseInt(ss[1]);  
58.             tx.setFirstKey(mkey);  
59.             tx.setSecondKey(mvalue);  
60.             second.set(mvalue);  
61.             context.write(tx, second);  
62.         }  
63.           
64.           
65.     }  
66.       
67.        
68.        
69.    
70.      /*** 
71.       * Reduce任务 
72.       *  
73.       * **/  
74.      public static class GReduce extends Reducer<DescSort, IntWritable, Text, Text>{  
75.          @Override  
76.         protected void reduce(DescSort arg0, Iterable<IntWritable> arg1, Context ctx)  
77.                 throws IOException, InterruptedException {  
78.              System.out.println("执行reduce");  
79.              StringBuffer sb=new StringBuffer();  
80.                
81.              for(IntWritable t:arg1){  
82.                    
83.                 // sb.append(t).append(",");  
84.       
85.                    
86.                  //con  
87.                    
88.                  ctx.write(new Text(arg0.getFirstKey()), new Text(t.toString()));   
89.                   
90.                    
91.                  /**这种写法，是这种输出 
92.                  
93.                 *a  45 
94.                 *b  12 
95.                  b  567 
96.                    三劫   1 
97.                   三劫    32 
98.                   三劫    899 
99.                   秦东亮   34 
100.                   秦东亮   72 
101.                   秦东亮   100  
102.                  */   
103.                    
104.                    
105.              }  
106.                
107.              if(sb.length()>0){  
108.                  sb.deleteCharAt(sb.length()-1);//删除最后一位的逗号  
109.              }  
110.                
111.                
112. //           在循环里拼接，在循环外输出是这种格式  
113. //           b  12,567  
114. //           三劫 1,32,899  
115. //           秦东亮    34,72,100  
116.              // ctx.write(new Text(arg0.getFirstKey()), new Text(sb.toString()));   
117.                
118.                
119.         }  
120.            
121.        
122.            
123.      }  
124.        
125.        
126.      /*** 
127.       *  
128.       * 自定义组合键 
129.       * **/  
130.         public static class DescSort implements  WritableComparable{  
131.   
132.              public DescSort() {  
133.                 // TODO Auto-generated constructor stub  
134.             }  
135.             private String firstKey;  
136.             private int secondKey;  
137.               
138.               
139.              public String getFirstKey() {  
140.                 return firstKey;  
141.             }  
142.             public void setFirstKey(String firstKey) {  
143.                 this.firstKey = firstKey;  
144.             }  
145.             public int getSecondKey() {  
146.                 return secondKey;  
147.             }  
148.             public void setSecondKey(int secondKey) {  
149.                 this.secondKey = secondKey;  
150.             }  
151.            
152.               
153.               
154.               
155. //           @Override  
156. //          public int compare(byte[] arg0, int arg1, int arg2, byte[] arg3,  
157. //                  int arg4, int arg5) {  
158. //              return -super.compare(arg0, arg1, arg2, arg3, arg4, arg5);//注意使用负号来完成降序  
159. //          }  
160. //             
161. //           @Override  
162. //          public int compare(Object a, Object b) {  
163. //         
164. //              return   -super.compare(a, b);//注意使用负号来完成降序  
165. //          }  
166.             @Override  
167.             public void readFields(DataInput in) throws IOException {  
168.                 // TODO Auto-generated method stub  
169.                 firstKey=in.readUTF();  
170.                 secondKey=in.readInt();  
171.             }  
172.             @Override  
173.             public void write(DataOutput out) throws IOException {  
174.                 out.writeUTF(firstKey);  
175.                 out.writeInt(secondKey);  
176.                   
177.             }  
178.             @Override  
179.             public int compareTo(Object o) {  
180.                 // TODO Auto-generated method stub  
181.                  DescSort d=(DescSort)o;  
182.                  //this在前代表升序  
183.                 return this.getFirstKey().compareTo(d.getFirstKey());  
184.             }  
185.                
186.               
187.         }  
188.        
189.           
190.         /** 
191.          * 主要就是对于分组进行排序，分组只按照组建键中的一个值进行分组 
192.          *  
193.          * **/  
194.         public static class TextComparator extends WritableComparator{  
195.   
196.              public TextComparator() {  
197.                 // TODO Auto-generated constructor stub  
198.                  super(DescSort.class,true);//注册Comparator  
199.             }  
200.              @Override  
201.             public int compare(WritableComparable a, WritableComparable b) {  
202.                 System.out.println("执行TextComparator分组排序");  
203.                  DescSort d1=(DescSort)a;  
204.                  DescSort d2=(DescSort)b;  
205.                    
206.                 return  d1.getFirstKey().compareTo(d2.getFirstKey());  
207.             }  
208.               
209.            
210.               
211.         }  
212.           
213.         /** 
214.          * 组内排序的策略 
215.          * 按照第二个字段排序 
216.          *  
217.          * */  
218.         public static class TextIntCompartator extends WritableComparator{  
219.               
220.             public TextIntCompartator() {  
221.                 super(DescSort.class,true);  
222.             }  
223.               
224.             @Override  
225.             public int compare(WritableComparable a, WritableComparable b) {  
226.                 DescSort d1=(DescSort)a;  
227.                 DescSort d2=(DescSort)b;  
228.                 System.out.println("执行组内排序TextIntCompartator");  
229.                 if(!d1.getFirstKey().equals(d2.getFirstKey())){  
230.                     return d1.getFirstKey().compareTo(d2.getFirstKey());  
231.                 }else{  
232.                       
233.                     return d1.getSecondKey()-d2.getSecondKey();//0,-1,1  
234.                       
235.                 }  
236.             }  
237.               
238.         }  
239.           
240.         /** 
241.          * 分区策略 
242.          *  
243.          * */  
244.      public static class KeyPartition extends Partitioner<DescSort, IntWritable>{  
245.            
246.            
247.          @Override  
248.         public int getPartition(DescSort key, IntWritable arg1, int arg2) {  
249.             // TODO Auto-generated method stub  
250.              System.out.println("执行自定义分区KeyPartition");  
251.             return (key.getFirstKey().hashCode()&Integer.MAX_VALUE)%arg2;  
252.         }   
253.      }  
254.           
255.           
256.      public static void main(String[] args) throws Exception{  
257.          JobConf conf=new JobConf(ReadMapDB.class);  
258.          //Configuration conf=new Configuration();  
259.           conf.set("mapred.job.tracker","192.168.75.130:9001");  
260.         //读取person中的数据字段  
261.           conf.setJar("tt.jar");  
262.         //注意这行代码放在最前面，进行初始化，否则会报  
263.        
264.        
265.         /**Job任务**/  
266.         Job job=new Job(conf, "testpartion");  
267.         job.setJarByClass(GroupSort.class);  
268.         System.out.println("模式：  "+conf.get("mapred.job.tracker"));;  
269.         // job.setCombinerClass(PCombine.class);  
270.       
271.            
272.            
273.         // job.setNumReduceTasks(3);//设置为3  
274.          job.setMapperClass(GMapper.class);  
275.          job.setReducerClass(GReduce.class);  
276.           
277.          /**设置分区函数*/  
278.         job.setPartitionerClass(KeyPartition.class);  
279.           
280.         //分组函数，Reduce前的一次排序  
281.          job.setGroupingComparatorClass(TextComparator.class);  
282.         //组内排序Map输出完毕后，对key进行的一次排序  
283.           
284.            
285.            
286.          job.setSortComparatorClass(TextIntCompartator.class);  
287.           
288.         //TextComparator.class  
289.         //TextIntCompartator.class  
290.         // job.setGroupingComparatorClass(TextIntCompartator.class);  
291.         //组内排序Map输出完毕后，对key进行的一次排序  
292.          // job.setSortComparatorClass(TextComparator.class);  
293.           
294.           
295.           
296.          job.setMapOutputKeyClass(DescSort.class);  
297.          job.setMapOutputValueClass(IntWritable.class);  
298.          job.setOutputKeyClass(Text.class);  
299.          job.setOutputValueClass(Text.class);  
300.           
301.         String path="hdfs://192.168.75.130:9000/root/outputdb";  
302.         FileSystem fs=FileSystem.get(conf);  
303.         Path p=new Path(path);  
304.         if(fs.exists(p)){  
305.             fs.delete(p, true);  
306.             System.out.println("输出路径存在，已删除！");  
307.         }  
308.         FileInputFormat.setInputPaths(job, "hdfs://192.168.75.130:9000/root/input");  
309.         FileOutputFormat.setOutputPath(job,p );  
310.         System.exit(job.waitForCompletion(true) ? 0 : 1);    
311.            
312.            
313.     }  
314.       
315.       
316.   
317. }  

package com.qin.groupsort;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.fs.FileSystem;
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.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.mapred.JobConf;
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.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import com.qin.operadb.PersonRecoder;
import com.qin.operadb.ReadMapDB;
 

/**
 * @author qindongliang
 * 
 * 大数据交流群：376932160
 * 
 * 
 * **/
public class GroupSort {
	
	/**
	 * map任务
	 * 
	 * */
	public static class GMapper extends Mapper<LongWritable, Text, DescSort, IntWritable>{
			
		
		private DescSort tx=new DescSort();
		private IntWritable second=new IntWritable();
		
		@Override
		protected void map(LongWritable key, Text value,Context context)
				throws IOException, InterruptedException {
			System.out.println("执行map");
			// System.out.println("进map了");
			//mos.write(namedOutput, key, value);
			String ss[]=value.toString().split(";");
			String mkey=ss[0];
			int mvalue=Integer.parseInt(ss[1]);
			tx.setFirstKey(mkey);
			tx.setSecondKey(mvalue);
			second.set(mvalue);
			context.write(tx, second);
		}
		
		
	}
	
	 
	 
 
	 /***
	  * Reduce任务
	  * 
	  * **/
	 public static class GReduce extends Reducer<DescSort, IntWritable, Text, Text>{
		 @Override
		protected void reduce(DescSort arg0, Iterable<IntWritable> arg1, Context ctx)
				throws IOException, InterruptedException {
			 System.out.println("执行reduce");
			 StringBuffer sb=new StringBuffer();
			 
			 for(IntWritable t:arg1){
				 
				// sb.append(t).append(",");
	
				 
				 //con
				 
				 ctx.write(new Text(arg0.getFirstKey()), new Text(t.toString())); 
				
				 
				 /**这种写法，是这种输出
				
				*a	45
				*b	12
				 b	567
				   三劫	1
				  三劫	32
				  三劫	899
				  秦东亮	34
				  秦东亮	72
				  秦东亮	100 
				 */ 
				 
				 
			 }
			 
			 if(sb.length()>0){
				 sb.deleteCharAt(sb.length()-1);//删除最后一位的逗号
			 }
			 
			 
//			 在循环里拼接，在循环外输出是这种格式
//			 b	12,567
//			 三劫	1,32,899
//			 秦东亮	34,72,100
			 // ctx.write(new Text(arg0.getFirstKey()), new Text(sb.toString())); 
			 
			 
		}
		 
	 
		 
	 }
	 
	 
	 /***
	  * 
	  * 自定义组合键
	  * **/
	 	public static class DescSort implements  WritableComparable{

	 		 public DescSort() {
				// TODO Auto-generated constructor stub
			}
	 		private String firstKey;
	 		private int secondKey;
	 		
	 		
	 		 public String getFirstKey() {
				return firstKey;
			}
			public void setFirstKey(String firstKey) {
				this.firstKey = firstKey;
			}
			public int getSecondKey() {
				return secondKey;
			}
			public void setSecondKey(int secondKey) {
				this.secondKey = secondKey;
			}
		 
			
			
			
//	 		 @Override
//	 		public int compare(byte[] arg0, int arg1, int arg2, byte[] arg3,
//	 				int arg4, int arg5) {
//	 			return -super.compare(arg0, arg1, arg2, arg3, arg4, arg5);//注意使用负号来完成降序
//	 		}
//	 		 
//	 		 @Override
//	 		public int compare(Object a, Object b) {
//	 	 
//	 			return   -super.compare(a, b);//注意使用负号来完成降序
//	 		}
			@Override
			public void readFields(DataInput in) throws IOException {
				// TODO Auto-generated method stub
				firstKey=in.readUTF();
				secondKey=in.readInt();
			}
			@Override
			public void write(DataOutput out) throws IOException {
				out.writeUTF(firstKey);
				out.writeInt(secondKey);
				
			}
			@Override
			public int compareTo(Object o) {
				// TODO Auto-generated method stub
				 DescSort d=(DescSort)o;
				 //this在前代表升序
				return this.getFirstKey().compareTo(d.getFirstKey());
			}
			 
	 		
	 	}
	 
	 	
	 	/**
	 	 * 主要就是对于分组进行排序，分组只按照组建键中的一个值进行分组
	 	 * 
	 	 * **/
	 	public static class TextComparator extends WritableComparator{

			 public TextComparator() {
				// TODO Auto-generated constructor stub
				 super(DescSort.class,true);//注册Comparator
			}
			 @Override
			public int compare(WritableComparable a, WritableComparable b) {
				System.out.println("执行TextComparator分组排序");
				 DescSort d1=(DescSort)a;
				 DescSort d2=(DescSort)b;
				 
				return  d1.getFirstKey().compareTo(d2.getFirstKey());
			}
	 		
	 	 
	 		
	 	}
	 	
	 	/**
	 	 * 组内排序的策略
	 	 * 按照第二个字段排序
	 	 * 
	 	 * */
	 	public static class TextIntCompartator extends WritableComparator{
	 		
	 		public TextIntCompartator() {
				super(DescSort.class,true);
			}
	 		
	 		@Override
	 		public int compare(WritableComparable a, WritableComparable b) {
	 			DescSort d1=(DescSort)a;
				DescSort d2=(DescSort)b;
	 			System.out.println("执行组内排序TextIntCompartator");
				if(!d1.getFirstKey().equals(d2.getFirstKey())){
					return d1.getFirstKey().compareTo(d2.getFirstKey());
				}else{
					
					return d1.getSecondKey()-d2.getSecondKey();//0,-1,1
					
				}
	 		}
	 		
	 	}
	 	
	 	/**
	 	 * 分区策略
	 	 * 
	 	 * */
	 public static class KeyPartition extends Partitioner<DescSort, IntWritable>{
		 
		 
		 @Override
		public int getPartition(DescSort key, IntWritable arg1, int arg2) {
			// TODO Auto-generated method stub
			 System.out.println("执行自定义分区KeyPartition");
			return (key.getFirstKey().hashCode()&Integer.MAX_VALUE)%arg2;
		} 
	 }
	 	
	 	
	 public static void main(String[] args) throws Exception{
		 JobConf conf=new JobConf(ReadMapDB.class);
		 //Configuration conf=new Configuration();
	  	  conf.set("mapred.job.tracker","192.168.75.130:9001");
		//读取person中的数据字段
	  	  conf.setJar("tt.jar");
		//注意这行代码放在最前面，进行初始化，否则会报
	 
	 
		/**Job任务**/
		Job job=new Job(conf, "testpartion");
		job.setJarByClass(GroupSort.class);
		System.out.println("模式：  "+conf.get("mapred.job.tracker"));;
		// job.setCombinerClass(PCombine.class);
	
		 
		 
		// job.setNumReduceTasks(3);//设置为3
		 job.setMapperClass(GMapper.class);
		 job.setReducerClass(GReduce.class);
		
		 /**设置分区函数*/
		job.setPartitionerClass(KeyPartition.class);
		
		//分组函数，Reduce前的一次排序
		 job.setGroupingComparatorClass(TextComparator.class);
		//组内排序Map输出完毕后，对key进行的一次排序
		
		 
		 
		 job.setSortComparatorClass(TextIntCompartator.class);
		
		//TextComparator.class
		//TextIntCompartator.class
		// job.setGroupingComparatorClass(TextIntCompartator.class);
		//组内排序Map输出完毕后，对key进行的一次排序
		 // job.setSortComparatorClass(TextComparator.class);
		
		
		
		 job.setMapOutputKeyClass(DescSort.class);
		 job.setMapOutputValueClass(IntWritable.class);
		 job.setOutputKeyClass(Text.class);
		 job.setOutputValueClass(Text.class);
	    
		String path="hdfs://192.168.75.130:9000/root/outputdb";
		FileSystem fs=FileSystem.get(conf);
		Path p=new Path(path);
		if(fs.exists(p)){
			fs.delete(p, true);
			System.out.println("输出路径存在，已删除！");
		}
		FileInputFormat.setInputPaths(job, "hdfs://192.168.75.130:9000/root/input");
		FileOutputFormat.setOutputPath(job,p );
		System.exit(job.waitForCompletion(true) ? 0 : 1);  
		 
		 
	}
	
	

}

在eclipse下，执行，打印日志内容如下：

Java代码  

1. 模式：  192.168.75.130:9001  
2. 输出路径存在，已删除！  
3. WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.  
4. INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1  
5. WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicable  
6. WARN - LoadSnappy.<clinit>(46) | Snappy native library not loaded  
7. INFO - JobClient.monitorAndPrintJob(1380) | Running job: job_201404152114_0003  
8. INFO - JobClient.monitorAndPrintJob(1393) |  map 0% reduce 0%  
9. INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 0%  
10. INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 33%  
11. INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 100%  
12. INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_201404152114_0003  
13. INFO - Counters.log(585) | Counters: 29  
14. INFO - Counters.log(587) |   Job Counters   
15. INFO - Counters.log(589) |     Launched reduce tasks=1  
16. INFO - Counters.log(589) |     SLOTS_MILLIS_MAPS=7040  
17. INFO - Counters.log(589) |     Total time spent by all reduces waiting after reserving slots (ms)=0  
18. INFO - Counters.log(589) |     Total time spent by all maps waiting after reserving slots (ms)=0  
19. INFO - Counters.log(589) |     Launched map tasks=1  
20. INFO - Counters.log(589) |     Data-local map tasks=1  
21. INFO - Counters.log(589) |     SLOTS_MILLIS_REDUCES=9807  
22. INFO - Counters.log(587) |   File Output Format Counters   
23. INFO - Counters.log(589) |     Bytes Written=86  
24. INFO - Counters.log(587) |   FileSystemCounters  
25. INFO - Counters.log(589) |     FILE_BYTES_READ=162  
26. INFO - Counters.log(589) |     HDFS_BYTES_READ=205  
27. INFO - Counters.log(589) |     FILE_BYTES_WRITTEN=111232  
28. INFO - Counters.log(589) |     HDFS_BYTES_WRITTEN=86  
29. INFO - Counters.log(587) |   File Input Format Counters   
30. INFO - Counters.log(589) |     Bytes Read=93  
31. INFO - Counters.log(587) |   Map-Reduce Framework  
32. INFO - Counters.log(589) |     Map output materialized bytes=162  
33. INFO - Counters.log(589) |     Map input records=9  
34. INFO - Counters.log(589) |     Reduce shuffle bytes=162  
35. INFO - Counters.log(589) |     Spilled Records=18  
36. INFO - Counters.log(589) |     Map output bytes=138  
37. INFO - Counters.log(589) |     Total committed heap usage (bytes)=176033792  
38. INFO - Counters.log(589) |     CPU time spent (ms)=970  
39. INFO - Counters.log(589) |     Combine input records=0  
40. INFO - Counters.log(589) |     SPLIT_RAW_BYTES=112  
41. INFO - Counters.log(589) |     Reduce input records=9  
42. INFO - Counters.log(589) |     Reduce input groups=4  
43. INFO - Counters.log(589) |     Combine output records=0  
44. INFO - Counters.log(589) |     Physical memory (bytes) snapshot=258830336  
45. INFO - Counters.log(589) |     Reduce output records=9  
46. INFO - Counters.log(589) |     Virtual memory (bytes) snapshot=1461055488  
47. INFO - Counters.log(589) |     Map output records=9  

模式：  192.168.75.130:9001
输出路径存在，已删除！
WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1
WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
WARN - LoadSnappy.<clinit>(46) | Snappy native library not loaded
INFO - JobClient.monitorAndPrintJob(1380) | Running job: job_201404152114_0003
INFO - JobClient.monitorAndPrintJob(1393) |  map 0% reduce 0%
INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 0%
INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 33%
INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 100%
INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_201404152114_0003
INFO - Counters.log(585) | Counters: 29
INFO - Counters.log(587) |   Job Counters 
INFO - Counters.log(589) |     Launched reduce tasks=1
INFO - Counters.log(589) |     SLOTS_MILLIS_MAPS=7040
INFO - Counters.log(589) |     Total time spent by all reduces waiting after reserving slots (ms)=0
INFO - Counters.log(589) |     Total time spent by all maps waiting after reserving slots (ms)=0
INFO - Counters.log(589) |     Launched map tasks=1
INFO - Counters.log(589) |     Data-local map tasks=1
INFO - Counters.log(589) |     SLOTS_MILLIS_REDUCES=9807
INFO - Counters.log(587) |   File Output Format Counters 
INFO - Counters.log(589) |     Bytes Written=86
INFO - Counters.log(587) |   FileSystemCounters
INFO - Counters.log(589) |     FILE_BYTES_READ=162
INFO - Counters.log(589) |     HDFS_BYTES_READ=205
INFO - Counters.log(589) |     FILE_BYTES_WRITTEN=111232
INFO - Counters.log(589) |     HDFS_BYTES_WRITTEN=86
INFO - Counters.log(587) |   File Input Format Counters 
INFO - Counters.log(589) |     Bytes Read=93
INFO - Counters.log(587) |   Map-Reduce Framework
INFO - Counters.log(589) |     Map output materialized bytes=162
INFO - Counters.log(589) |     Map input records=9
INFO - Counters.log(589) |     Reduce shuffle bytes=162
INFO - Counters.log(589) |     Spilled Records=18
INFO - Counters.log(589) |     Map output bytes=138
INFO - Counters.log(589) |     Total committed heap usage (bytes)=176033792
INFO - Counters.log(589) |     CPU time spent (ms)=970
INFO - Counters.log(589) |     Combine input records=0
INFO - Counters.log(589) |     SPLIT_RAW_BYTES=112
INFO - Counters.log(589) |     Reduce input records=9
INFO - Counters.log(589) |     Reduce input groups=4
INFO - Counters.log(589) |     Combine output records=0
INFO - Counters.log(589) |     Physical memory (bytes) snapshot=258830336
INFO - Counters.log(589) |     Reduce output records=9
INFO - Counters.log(589) |     Virtual memory (bytes) snapshot=1461055488
INFO - Counters.log(589) |     Map output records=9

执行完，我们在输出目录里里面查看



执行完，内容如下：

Java代码  

1. a   45  
2. b   12  
3. b   567  
4. 三劫  1  
5. 三劫  32  
6. 三劫  899  
7. 秦东亮 34  
8. 秦东亮 72  
9. 秦东亮 100  

a	45
b	12
b	567
三劫	1
三劫	32
三劫	899
秦东亮	34
秦东亮	72
秦东亮	100

我们发现，跟我们预期的结果一致，熟悉MapReduce的执行原理，可以帮助我们更好的使用Hive，因为Hive本身就是一个或多个MapReduce作业构成的，Hive语句的优化，对MapReduce作业的影响的性能也是不容忽视的，所以我们一定要多熟悉熟悉MapReduce编程的模型，以便于我们对它有一个更清晰的认识和了解。