以前对Hadoop有过一点了解，但没有深入，现在越来越感觉这东西挺有意思的，打算学习下，前两天买了两本Hadoop相关的书，先粗略的翻了下，今天就动手先把环境搭起来。

      环境：centos6.2,jdk7_u45,

      下载，解压过程就不说了，直接环境配置（包括JAVA_HOME的配置，以及HADOOP_HOME的环境变量配置，都略过了）。参考文档

       1，修改hadoop-env.sh中修改JAVA_HOME

       2，修改core-site.xml配置文件

    
    
    	
       		
      
       hadoop.tmp.dir
        		
      
       /data/hadoop/tmp
      	
     		
       	  
      
       fs.defaultFS
        	  
      
       hdfs://localhost:9000
        	  
      
       true
        	
       	
    

     3，修改hdfs-site.xml配置文件

    
    
    	
     	  
      
       dfs.namenode.name.dir
      	  
      
       file:///data/hadoop/dfs/name
      	  
      
       true
      	
     	
     	  
      
       dfs.datanode.data.dir
      	  
      
       file:///data/hadoop/dfs/data
      	  
      
       true
      	
     	
     	  
      
       dfs.replication
      	  
      
       1
      	
     	
     	  
      
       dfs.permissions.enabled
      	  
      
       false
      	
     
    

     4，复制mapred-site.xml.template成mapred-site.xml，修改mapred-site.xml

cp mapred-site.xml.template mapred-site.xmlvi mapred-site.xml

    
    
    	
     	  
      
       mapreduce.framework.name
      	  
      
       yarn
      	
     	
     
    

   5,修改yarn-site.xml配置文件

    
    	
     	  
      
       yarn.resourcemanager.hostname
      	  
      
       localhost
      	  
      
       hostanem of RM
      	
     	
         
      
       yarn.resourcemanager.resource-tracker.address
          
      
       localhost:5274
          
      
       host is the hostname of the resource manager and     port is the port on which the NodeManagers contact the Resource Manager.    
        
       
         
      
       yarn.resourcemanager.scheduler.address
          
      
       localhost:5273
          
      
       host is the hostname of the resourcemanager and port is the port    on which the Applications in the cluster talk to the Resource Manager.    
        
       
         
      
       yarn.resourcemanager.scheduler.class
          
      
       org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler
          
      
       In case you do not want to use the default scheduler
        
       
         
      
       yarn.resourcemanager.address
          
      
       localhost:5271
          
      
       the host is the hostname of the ResourceManager and the port is the port on    which the clients can talk to the Resource Manager. 
        
       
         
      
       yarn.nodemanager.local-dirs
          
          
      
       the local directories used by the nodemanager
        
       
         
      
       yarn.nodemanager.address
          
      
       localhost:5272
          
      
       the nodemanagers bind to this port
        
         
         
      
       yarn.nodemanager.resource.memory-mb
          
      
       10240
          
      
       the amount of memory on the NodeManager in GB
        
        
         
      
       yarn.nodemanager.remote-app-log-dir
          
      
       /app-logs
          
      
       directory on hdfs where the application logs are moved to 
        
        
         
      
       yarn.nodemanager.log-dirs
          
          
      
       the directories used by Nodemanagers as log directories
        
       
         
      
       yarn.nodemanager.aux-services
          
      
       mapreduce_shuffle
          
      
       shuffle service that needs to be set for Map Reduce to run 
        
     	
    

到此为止，hadoop单机版配置已经完成。

1）接下来我们先格式化namenode,然后启动namenode

hadoop namenode –format

hadoop-daemon.sh start namenode

可以查看http://localhost:50070/dfshealth.jsp中logs的日志 （带namenode*.log字眼），确认是否启动成功，如果没有报错则启动成功。

2）接着启动hdfs datanode

hadoop-daemon.sh start datanode

同时也可以在开始页面上查询对应的日志文件（带datanode *.log字眼），如果没有报错，和namenode通信成功，即启动成功。

还可以在命令行数据Jps查看是否有结果

3)继续启动yarn

yarn-daemon.sh start resourcemanageryarn-daemon.sh start nodemanager

判断启动成功与否方法同上面一致。

最后进入hadoop-2.2.0\share\hadoop\mapreduce录入中，测试运行

hadoop jar hadoop-mapreduce-examples-2.2.0.jar randomwriter out

查看运行是否成功