Hadoop Cluster Migrator

Hadoop Cluster Migrator tool provides a unified interface for copying data from one cluster to another cluster. Traditionally, DistCpy tool provided by Hadoop is used to migrate and copy the data from one Hadoop cluster to other. However, Distcpy works only when the connectivity between the source and target cluster is well established without any firewall rules blocking this connectivity. But in production scenarios, the edge node isolate the clusters from each other and Distcpy can’t be used for transfer of data or backup of the cluster. This is where Hadoop Cluster Migrator from Knowledge Lens can be very handy.

Hadoop Cluster Migrator is a cluster agnostic tool, which supports migration across different distribution, different version of Hadoop. Currently we support MapR, Cloudera, Hortworks, EMC Pivotal and Apache distribution of Hadoop, both in Kerberos enabled and disabled mode.

This completely Java based tool provides large scale data transfer between the cluster with transfer rate in the range of 10GB/s depending upon the bandwidth available. The tool is completely restartable and restarts from the point where the last transfer was stopped.

For more details refer : Hadoop Cluster Migrator



Decommissioning a Datanode in a Hadoop cluster

Sometimes we may require to remove a node from a hadoop cluster without loosing the data.
For this we have to do the decommissioning procedures.
Decommisioning will exclude a node from the cluster after replicating the data present in the decommissioning node to the other active nodes.

The decommissioning is very simple. The steps are explained below.
First stop the tasktracker in the node to be decommissioned.
In the namenode machine add the below property to the hdfs-site.xml


where dfs.exclude is a file that we have to create and place it in a safe location. Better to keep it in HADOOP_CONF_DIR (/etc/hadoop/conf).

Create a file named dfs.exclude and add the hostnames of machines that need to be decommissioned line by line.

Eg: dfs.exclude


After doing this, execute the following command from the superuser in the namenode machine.

hadoop dfsadmin -refreshNodes

After this, check the namenode UI. ie http://namenode:50070
You will be able to see the machines under decommissioning nodes.
The decommissioning process will take some time.
After the re-replication gets completed, the machine will be added to decommissioned nodes list.
After this, the decommissioned node can be safely removed from the cluster. 🙂

Back Up Mechanism for Namenode

Namenode is the single point of failure in hadoop cluster. Because it stores the metadata of the entire hadoop system.
So extra care should be given in maintaining it. We use the best hardware for namenode machines.
Even if we use best hardware, complete protection cannot be guarenteed, because hardware issues can happen at anytime. So a backup for namenode is very necessary.
One of the methods is creating a simple backup storage by mounting the partition of another machine located in a different place to the namenode machine.
The back up machine should have the same hardware/software specifications as of namenode machine and installed with hadoop similar to namenode machine. But hadoop services are not started in that machine.
Incase of failure, we can start namenode in this backup machine and it runs like normal namenode. The only thing we need to do is assigning ipaddress/hostname of actual namenode to the backup namenode.

In the hdfs-site.xml, we are giving an additional value to property.
ie actual location, backup location.


Determines where on the local filesystem the DFS name node should store the name table(fsimage). If this is a comma-delimited list of directories then the name table is replicated in all of the directories, for redundancy. 

here /app/hadoop/name is the actual namenode storage location and /app/hadoop/backup is the location where the partition is mounted for storing the namenode backup.
In case of failure of the first namenode machine, the namenode data will be safe in the second machine(backup), so we can start the namenode in the second machine.
The second machine is placed in different location and is provided with a differnt power supply, so that the dependencies of both the machines will be different, thus making an efficient backup.