The decommissioning process is controlled by an exclude file, which for HDFS is set by the* dfs.hosts.exclude* property, and for MapReduce by the*mapred.hosts.exclude * property. In most cases, there is one shared file,referred to as the exclude file.This exclude file name should be specified as a configuration parameter *dfs.hosts.exclude *in the name node start up.
To remove nodes from the cluster: 1. Add the network addresses of the nodes to be decommissioned to the exclude file. 2. Restart the MapReduce cluster to stop the tasktrackers on the nodes being decommissioned. 3. Update the namenode with the new set of permitted datanodes, with this command: % hadoop dfsadmin -refreshNodes 4. Go to the web UI and check whether the admin state has changed to “Decommission In Progress” for the datanodes being decommissioned. They will start copying their blocks to other datanodes in the cluster. 5. When all the datanodes report their state as “Decommissioned,” then all the blocks have been replicated. Shut down the decommissioned nodes. 6. Remove the nodes from the include file, and run: % hadoop dfsadmin -refreshNodes 7. Remove the nodes from the slaves file. Decommission data nodes in small percentage(less than 2%) at time don't cause any effect on cluster. But it better to pause MR-Jobs before you triggering Decommission to ensure no task running in decommissioning subjected nodes. If very small percentage of task running in the decommissioning node it can submit to other task tracker, but percentage queued jobs larger then threshold then there is chance of job failure. Once triggering the 'hadoop dfsadmin -refreshNodes' command and decommission started, you can resume the MR jobs. *Source : The Definitive Guide [Tom White]* On Tuesday, February 12, 2013 5:20:07 PM UTC+5:30, Dhanasekaran Anbalagan wrote: > > Hi Guys, > > It's recommenced do with removing one the datanode in production cluster. > via Decommission the particular datanode. please guide me. > > -Dhanasekaran, > > Did I learn something today? If not, I wasted it. >
