如何使用Hadoop的MultipleOutputs进行多文件输出

╰半橙微兮° 2022-04-21 19:30 179阅读 0赞

\[b\]\[color=olive\]\[size=large\]有时候，我们使用Hadoop处理数据时，在Reduce阶段，我们可能想对每一个输出的key进行单独输出一个目录或文件，这样方便数据分析，比如根据某个时间段对日志文件进行时间段归类等等。这时候我们就可以使用MultipleOutputs类，来搞定这件事，  
  
下面，先来看下散仙的测试数据：  
\[/size\]\[/color\]\[/b\]

中国;我们
    美国;他们
    中国;123
    中国人;善良
    美国;USA
    美国;在北美洲

\[b\]\[color=olive\]\[size=large\]输出结果：预期输出结果是：  
中国一组，美国一组，中国人一组  
核心代码如下：  
\[/size\]\[/color\]\[/b\]

package com.partition.test;
    
    import java.io.IOException;
    
    import org.apache.hadoop.fs.FileSystem;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    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
     * 
     * 大数据技术交流群:324714439
     * **/
    public class TestMultiOutput {
       
    
    
    	/**
    	 * map任务
    	 * 
    	 * **/
    	public static class PMapper extends Mapper<LongWritable, Text, Text, Text>{
       
    
    		@Override
    		protected void map(LongWritable key, Text value,Context context)
    				throws IOException, InterruptedException {
       
    			 String ss[]=value.toString().split(";");
    			context.write(new Text(ss[0]), new Text(ss[1]));	
    		}
    
    
    	}
    
    
    	 public static class PReduce extends Reducer<Text, Text, Text, Text>{
       
    		 /**
    		  * 设置多个文件输出
    		  * */
    		 private MultipleOutputs mos;
    
    		 @Override
    		protected void setup(Context context)
    				throws IOException, InterruptedException {
       
    			  mos=new MultipleOutputs(context);//初始化mos
    		}
    		 @Override
    		protected void reduce(Text arg0, Iterable<Text> arg1, Context arg2)
    				throws IOException, InterruptedException {
       
    
    			  String key=arg0.toString();
    			 for(Text t:arg1){
       
    				   if(key.equals("中国")){ 
    					   /**
    					    * 一个参数
    					    * **/
    					   mos.write("china", arg0,t); 
    				   } else if(key.equals("美国")){
       
    					   mos.write("USA", arg0,t);    
    				   } else if(key.equals("中国人")){
       
    					   mos.write("cperson", arg0,t); 
    
    				   }
    
    				 //System.out.println("Reduce:  "+arg0.toString()+"   "+t.toString());
    			 }
    
    
    		}
    
    		 @Override
    		protected void cleanup(
    				 Context context)
    				throws IOException, InterruptedException {
       
    			 mos.close();//释放资源
    		}
    
    	 }
    
    
    	 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(TestMultiOutput.class);
    		System.out.println("模式：  "+conf.get("mapred.job.tracker"));;
    		// job.setCombinerClass(PCombine.class);
    		//job.setPartitionerClass(PPartition.class);
    		//job.setNumReduceTasks(5);
    		 job.setMapperClass(PMapper.class);
    
    		 /**
    		  * 注意在初始化时需要设置输出文件的名
    		  * 另外名称，不支持中文名，仅支持英文字符
    		  * 
    		  * **/
    		 MultipleOutputs.addNamedOutput(job, "china", TextOutputFormat.class, Text.class, Text.class);
    		 MultipleOutputs.addNamedOutput(job, "USA", TextOutputFormat.class, Text.class, Text.class);
    		 MultipleOutputs.addNamedOutput(job, "cperson", TextOutputFormat.class, Text.class, Text.class);
    		 job.setReducerClass(PReduce.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);  
    
    
    	}
    
    
    
    }

\[b\]\[color=green\]\[size=large\]如果是中文的路径名，则会报如下的一个异常：\[/size\]\[/color\]\[/b\]

模式：  local
    输出路径存在，已删除！
    WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
    WARN - JobClient.copyAndConfigureFiles(870) | No job jar file set.  User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
    INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1
    WARN - LoadSnappy.<clinit>(46) | Snappy native library not loaded
    INFO - JobClient.monitorAndPrintJob(1380) | Running job: job_local1533332464_0001
    INFO - LocalJobRunner$Job.run(340) | Waiting for map tasks
    INFO - LocalJobRunner$Job$MapTaskRunnable.run(204) | Starting task: attempt_local1533332464_0001_m_000000_0
    INFO - Task.initialize(534) |  Using ResourceCalculatorPlugin : null
    INFO - MapTask.runNewMapper(729) | Processing split: hdfs://192.168.75.130:9000/root/input/group.txt:0+91
    INFO - MapTask$MapOutputBuffer.<init>(949) | io.sort.mb = 100
    INFO - MapTask$MapOutputBuffer.<init>(961) | data buffer = 79691776/99614720
    INFO - MapTask$MapOutputBuffer.<init>(962) | record buffer = 262144/327680
    INFO - MapTask$MapOutputBuffer.flush(1289) | Starting flush of map output
    INFO - MapTask$MapOutputBuffer.sortAndSpill(1471) | Finished spill 0
    INFO - Task.done(858) | Task:attempt_local1533332464_0001_m_000000_0 is done. And is in the process of commiting
    INFO - LocalJobRunner$Job.statusUpdate(466) | 
    INFO - Task.sendDone(970) | Task 'attempt_local1533332464_0001_m_000000_0' done.
    INFO - LocalJobRunner$Job$MapTaskRunnable.run(229) | Finishing task: attempt_local1533332464_0001_m_000000_0
    INFO - LocalJobRunner$Job.run(348) | Map task executor complete.
    INFO - Task.initialize(534) |  Using ResourceCalculatorPlugin : null
    INFO - LocalJobRunner$Job.statusUpdate(466) | 
    INFO - Merger$MergeQueue.merge(408) | Merging 1 sorted segments
    INFO - Merger$MergeQueue.merge(491) | Down to the last merge-pass, with 1 segments left of total size: 101 bytes
    INFO - LocalJobRunner$Job.statusUpdate(466) | 
    WARN - LocalJobRunner$Job.run(435) | job_local1533332464_0001
    java.lang.IllegalArgumentException: Name cannot be have a '一' char
    	at org.apache.hadoop.mapreduce.lib.output.MultipleOutputs.checkTokenName(MultipleOutputs.java:160)
    	at org.apache.hadoop.mapreduce.lib.output.MultipleOutputs.checkNamedOutputName(MultipleOutputs.java:186)
    	at org.apache.hadoop.mapreduce.lib.output.MultipleOutputs.write(MultipleOutputs.java:363)
    	at org.apache.hadoop.mapreduce.lib.output.MultipleOutputs.write(MultipleOutputs.java:348)
    	at com.partition.test.TestMultiOutput$PReduce.reduce(TestMultiOutput.java:74)
    	at com.partition.test.TestMultiOutput$PReduce.reduce(TestMultiOutput.java:1)
    	at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:177)
    	at org.apache.hadoop.mapred.ReduceTask.runNewReducer(ReduceTask.java:649)
    	at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:418)
    	at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:398)
    INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 0%
    INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_local1533332464_0001
    INFO - Counters.log(585) | Counters: 17
    INFO - Counters.log(587) |   File Input Format Counters 
    INFO - Counters.log(589) |     Bytes Read=91
    INFO - Counters.log(587) |   FileSystemCounters
    INFO - Counters.log(589) |     FILE_BYTES_READ=177
    INFO - Counters.log(589) |     HDFS_BYTES_READ=91
    INFO - Counters.log(589) |     FILE_BYTES_WRITTEN=71111
    INFO - Counters.log(587) |   Map-Reduce Framework
    INFO - Counters.log(589) |     Map output materialized bytes=105
    INFO - Counters.log(589) |     Map input records=6
    INFO - Counters.log(589) |     Reduce shuffle bytes=0
    INFO - Counters.log(589) |     Spilled Records=6
    INFO - Counters.log(589) |     Map output bytes=87
    INFO - Counters.log(589) |     Total committed heap usage (bytes)=227737600
    INFO - Counters.log(589) |     Combine input records=0
    INFO - Counters.log(589) |     SPLIT_RAW_BYTES=112
    INFO - Counters.log(589) |     Reduce input records=0
    INFO - Counters.log(589) |     Reduce input groups=0
    INFO - Counters.log(589) |     Combine output records=0
    INFO - Counters.log(589) |     Reduce output records=0
    INFO - Counters.log(589) |     Map output records=6

\[color=olive\]\[size=large\]源码中关于名称的校验如下：\[/size\]\[/color\]

/**
       * Checks if a named output name is valid token.
       *
       * @param namedOutput named output Name
       * @throws IllegalArgumentException if the output name is not valid.
       */
      private static void checkTokenName(String namedOutput) {
       
        if (namedOutput == null || namedOutput.length() == 0) {
       
          throw new IllegalArgumentException(
            "Name cannot be NULL or emtpy");
        }
        for (char ch : namedOutput.toCharArray()) {
       
          if ((ch >= 'A') && (ch <= 'Z')) {
       
            continue;
          }
          if ((ch >= 'a') && (ch <= 'z')) {
       
            continue;
          }
          if ((ch >= '0') && (ch <= '9')) {
       
            continue;
          }
          throw new IllegalArgumentException(
            "Name cannot be have a '" + ch + "' char");
        }
      }

\[b\]\[color=olive\]\[size=large\]程序运行成功输出：\[/size\]\[/color\]\[/b\]

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_201404101853_0006
    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_201404101853_0006
    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=9289
    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=13645
    INFO - Counters.log(587) |   File Output Format Counters 
    INFO - Counters.log(589) |     Bytes Written=0
    INFO - Counters.log(587) |   FileSystemCounters
    INFO - Counters.log(589) |     FILE_BYTES_READ=105
    INFO - Counters.log(589) |     HDFS_BYTES_READ=203
    INFO - Counters.log(589) |     FILE_BYTES_WRITTEN=113616
    INFO - Counters.log(589) |     HDFS_BYTES_WRITTEN=87
    INFO - Counters.log(587) |   File Input Format Counters 
    INFO - Counters.log(589) |     Bytes Read=91
    INFO - Counters.log(587) |   Map-Reduce Framework
    INFO - Counters.log(589) |     Map output materialized bytes=105
    INFO - Counters.log(589) |     Map input records=6
    INFO - Counters.log(589) |     Reduce shuffle bytes=105
    INFO - Counters.log(589) |     Spilled Records=12
    INFO - Counters.log(589) |     Map output bytes=87
    INFO - Counters.log(589) |     Total committed heap usage (bytes)=176033792
    INFO - Counters.log(589) |     CPU time spent (ms)=1880
    INFO - Counters.log(589) |     Combine input records=0
    INFO - Counters.log(589) |     SPLIT_RAW_BYTES=112
    INFO - Counters.log(589) |     Reduce input records=6
    INFO - Counters.log(589) |     Reduce input groups=3
    INFO - Counters.log(589) |     Combine output records=0
    INFO - Counters.log(589) |     Physical memory (bytes) snapshot=278876160
    INFO - Counters.log(589) |     Reduce output records=0
    INFO - Counters.log(589) |     Virtual memory (bytes) snapshot=1460908032
    INFO - Counters.log(589) |     Map output records=6

\[b\]\[color=green\]\[size=large\]运行成功后，生成的文件如下所示：\[/size\]\[/color\]  
\[/b\]  
\[img\]http://dl2.iteye.com/upload/attachment/0095/9727/027502c2-d60e-3784-ac34-daaaf826f8b5.jpg\[/img\]  
\[b\]\[color=olive\]\[size=large\]china-r-00000里面的数据如下：\[/size\]\[/color\]\[/b\]

中国	我们
    中国	123

\[b\]\[color=olive\]\[size=large\]USA-r-00000里面的数据如下：\[/size\]\[/color\]\[/b\]

美国	他们
    美国	USA
    美国	在北美洲

\[b\]\[color=olive\]\[size=large\]cperson-r-00000里面的数据如下：\[/size\]\[/color\]\[/b\]

中国人	 善良

\[b\]\[size=large\]\[color=olive\]在输出结果中，reduce自带的那个文件仍然会输出，但是里面没有任何数据，至此，我们已经在hadoop1.2.0的基于新的API里，测试多文件输出通过。 \[/color\]\[/size\]\[/b\]