已经在eclipse中通过local的模式可以正确的调试hadoop2.2，那么本篇，散仙将重点叙述下，如何在eclipse中，真真正正的提交作业到yarn上，开启分布式模式的调试，通过在eclipse上调试，hadoop的MapReduce程序，可以使我们学习Hadoop更加容易，清晰。

2014-06-11 17:32:19,761 WARN org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor: Exception from container-launch with container ID: container_1401177251807_0034_01_000001 and exit code: 1  org.apache.hadoop.util.Shell$ExitCodeException: /bin/bash: line 0: fg: no job control        at org.apache.hadoop.util.Shell.runCommand(Shell.java:505)      at org.apache.hadoop.util.Shell.run(Shell.java:418)      at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:650)      at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:195)      at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:300)      at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:81)      at java.util.concurrent.FutureTask.run(FutureTask.java:262)      at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)      at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)      at java.lang.Thread.run(Thread.java:744)

// Setup the command to run the AM      List
    
      vargs = new ArrayList
     
      (8);      vargs.add(Environment.JAVA_HOME.$() + "/bin/java");
     
    

vargs.add("$JAVA_HOME/bin/java");

private void replaceEnvironment(Map
    
      environment) {        String tmpClassPath = environment.get("CLASSPATH");        tmpClassPath=tmpClassPath.replaceAll(";", ":");        tmpClassPath=tmpClassPath.replaceAll("%PWD%", "\\$PWD");        tmpClassPath=tmpClassPath.replaceAll("%HADOOP_MAPRED_HOME%", "\\$HADOOP_MAPRED_HOME");        tmpClassPath= tmpClassPath.replaceAll("\\\\", "/" );        environment.put("CLASSPATH",tmpClassPath);  }
    

replaceEnvironment(environment);

package com.qin.wordcount;import java.io.IOException;import org.apache.hadoop.conf.Configuration;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.mapred.JobConf;import org.apache.hadoop.mapred.YARNRunner;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;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;/*** *  * Hadoop2.2.0完全分布式测试 * 放WordCount的例子 *  * @author qindongliang *  * hadoop技术交流群：  376932160 *  *  * */public class MyWordCount {			/**	 * Mapper	 * 	 * **/	private static class WMapper extends Mapper
    
     {						private IntWritable count=new IntWritable(1);		private Text text=new Text();		@Override		protected void map(LongWritable key, Text value,Context context)				throws IOException, InterruptedException {			String values[]=value.toString().split("#");			//System.out.println(values[0]+"========"+values[1]);			count.set(Integer.parseInt(values[1]));			text.set(values[0]);			context.write(text,count);					}			}		/**	 * Reducer	 * 	 * **/	private static class WReducer extends Reducer
     
      {				private Text t=new Text();		@Override		protected void reduce(Text key, Iterable
      
        value,Context context)				throws IOException, InterruptedException {			int count=0;			for(IntWritable i:value){				count+=i.get();			}			t.set(count+"");			context.write(key,t);					}			}			/**	 * 改动一	 * (1)shell源码里添加checkHadoopHome的路径	 * (2)974行，FileUtils里面	 * **/		public static void main(String[] args) throws Exception{						Configuration conf=new Configuration();			    conf.set("mapreduce.job.jar", "myjob.jar");		conf.set("fs.defaultFS","hdfs://192.168.46.28:9000");		conf.set("mapreduce.framework.name", "yarn");  		conf.set("yarn.resourcemanager.address", "192.168.46.28:8032"); 		/**Job任务**/	   //Job job=new Job(conf, "testwordcount");//废弃此API	   Job job=Job.getInstance(conf, "new api");		job.setJarByClass(MyWordCount.class);		System.out.println("模式：  "+conf.get("mapreduce.jobtracker.address"));;		// job.setCombinerClass(PCombine.class);			 		 		// job.setNumReduceTasks(3);//设置为3		 job.setMapperClass(WMapper.class);		 job.setReducerClass(WReducer.class);		 job.setInputFormatClass(TextInputFormat.class);		 job.setOutputFormatClass(TextOutputFormat.class); 	 				 job.setMapOutputKeyClass(Text.class);		 job.setMapOutputValueClass(IntWritable.class);		 job.setOutputKeyClass(Text.class);		 job.setOutputValueClass(Text.class);				String path="hdfs://192.168.46.28:9000/qin/output";			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://192.168.46.28:9000/qin/input");		FileOutputFormat.setOutputPath(job,p );		System.exit(job.waitForCompletion(true) ? 0 : 1);  									}	}
      
     
    

    
     mapred.remote.os
      
    
     Linux
     
    
     RemoteMapReduce framework's OS, can be either Linux orWindows
      

模式：  hp1:8021输出路径存在，已删除！INFO - RMProxy.createRMProxy(56) | Connecting to ResourceManager at /192.168.46.28:8032WARN - JobSubmitter.copyAndConfigureFiles(149) | Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.INFO - FileInputFormat.listStatus(287) | Total input paths to process : 1INFO - JobSubmitter.submitJobInternal(394) | number of splits:1INFO - Configuration.warnOnceIfDeprecated(840) | user.name is deprecated. Instead, use mapreduce.job.user.nameINFO - Configuration.warnOnceIfDeprecated(840) | mapred.jar is deprecated. Instead, use mapreduce.job.jarINFO - Configuration.warnOnceIfDeprecated(840) | fs.default.name is deprecated. Instead, use fs.defaultFSINFO - Configuration.warnOnceIfDeprecated(840) | mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.classINFO - Configuration.warnOnceIfDeprecated(840) | mapred.mapoutput.value.class is deprecated. Instead, use mapreduce.map.output.value.classINFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.classINFO - Configuration.warnOnceIfDeprecated(840) | mapred.job.name is deprecated. Instead, use mapreduce.job.nameINFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.classINFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.classINFO - Configuration.warnOnceIfDeprecated(840) | mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdirINFO - Configuration.warnOnceIfDeprecated(840) | mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdirINFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.classINFO - Configuration.warnOnceIfDeprecated(840) | mapred.map.tasks is deprecated. Instead, use mapreduce.job.mapsINFO - Configuration.warnOnceIfDeprecated(840) | mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.classINFO - Configuration.warnOnceIfDeprecated(840) | mapred.mapoutput.key.class is deprecated. Instead, use mapreduce.map.output.key.classINFO - Configuration.warnOnceIfDeprecated(840) | mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dirINFO - JobSubmitter.printTokens(477) | Submitting tokens for job: job_1402492118962_0004INFO - YarnClientImpl.submitApplication(174) | Submitted application application_1402492118962_0004 to ResourceManager at /192.168.46.28:8032INFO - Job.submit(1272) | The url to track the job: http://hp1:8088/proxy/application_1402492118962_0004/INFO - Job.monitorAndPrintJob(1317) | Running job: job_1402492118962_0004INFO - Job.monitorAndPrintJob(1338) | Job job_1402492118962_0004 running in uber mode : falseINFO - Job.monitorAndPrintJob(1345) |  map 0% reduce 0%INFO - Job.monitorAndPrintJob(1345) |  map 100% reduce 0%INFO - Job.monitorAndPrintJob(1345) |  map 100% reduce 100%INFO - Job.monitorAndPrintJob(1356) | Job job_1402492118962_0004 completed successfullyINFO - Job.monitorAndPrintJob(1363) | Counters: 43	File System Counters		FILE: Number of bytes read=58		FILE: Number of bytes written=159667		FILE: Number of read operations=0		FILE: Number of large read operations=0		FILE: Number of write operations=0		HDFS: Number of bytes read=147		HDFS: Number of bytes written=27		HDFS: Number of read operations=6		HDFS: Number of large read operations=0		HDFS: Number of write operations=2	Job Counters 		Launched map tasks=1		Launched reduce tasks=1		Data-local map tasks=1		Total time spent by all maps in occupied slots (ms)=6155		Total time spent by all reduces in occupied slots (ms)=4929	Map-Reduce Framework		Map input records=4		Map output records=4		Map output bytes=44		Map output materialized bytes=58		Input split bytes=109		Combine input records=0		Combine output records=0		Reduce input groups=3		Reduce shuffle bytes=58		Reduce input records=4		Reduce output records=3		Spilled Records=8		Shuffled Maps =1		Failed Shuffles=0		Merged Map outputs=1		GC time elapsed (ms)=99		CPU time spent (ms)=1060		Physical memory (bytes) snapshot=309071872		Virtual memory (bytes) snapshot=1680531456		Total committed heap usage (bytes)=136450048	Shuffle Errors		BAD_ID=0		CONNECTION=0		IO_ERROR=0		WRONG_LENGTH=0		WRONG_MAP=0		WRONG_REDUCE=0	File Input Format Counters 		Bytes Read=38	File Output Format Counters 		Bytes Written=27