Big Data Hadoop Certification Training
- 164k Enrolled Learners
- Live Class
In last few years Apache Hadoop has emerged as the technology for solving Big Data problems and for improved Business Analytics. One example of this is how Sears Holding has moved to Hadoop from the traditional Oracle Exadata, Teradata, SAS system. Another recent big entrant to Hadoop bandwagon is Walmart’s Hadoop implementation.
In our previous blog we have discussed, how to create a Hadoop Cluster on AWS in 30 minutes.
In continuation to that, this blog talks about important Hadoop Cluster Configuration Files.
The following table lists the same.
All these files are available under ‘conf’ directory of Hadoop installation directory.
Here is a listing of these files in the File System:
Let’s look at the files and their usage one by one!
This file specifies environment variables that affect the JDK used by Hadoop Daemon (bin/hadoop).
As Hadoop framework is written in Java and uses Java Runtime environment, one of the important environment variables for Hadoop daemon is $JAVA_HOME in hadoop-env.sh. This variable directs Hadoop daemon to the Java path in the system.
This file is also used for setting another Hadoop daemon execution environment such as heap size (HADOOP_HEAP), hadoop home (HADOOP_HOME), log file location (HADOOP_LOG_DIR), etc.
Note: For the simplicity of understanding the cluster setup, we have configured only necessary parameters to start a cluster.
The following three files are the important configuration files for the runtime environment settings of a Hadoop cluster.
This file informs Hadoop daemon where NameNode runs in the cluster. It contains the configuration settings for Hadoop Core such as I/O settings that are common to HDFS and MapReduce.
Where hostname and port are the machine and port on which NameNode daemon runs and listens. It also informs the Name Node as to which IP and port it should bind. The commonly used port is 8020 and you can also specify IP address rather than hostname.
This file contains the configuration settings for HDFS daemons; the Name Node, the Secondary Name Node, and the data nodes.
You can also configure hdfs-site.xml to specify default block replication and permission checking on HDFS. The actual number of replications can also be specified when the file is created. The default is used if replication is not specified in create time.
The value “true” for property ‘dfs.permissions’ enables permission checking in HDFS and the value “false” turns off the permission checking. Switching from one parameter value to the other does not change the mode, owner or group of files or directories.
This file contains the configuration settings for MapReduce daemons; the job tracker and the task-trackers. The mapred.job.tracker parameter is a hostname (or IP address) and port pair on which the Job Tracker listens for RPC communication. This parameter specify the location of the Job Tracker to Task Trackers and MapReduce clients.
You can replicate all of the four files explained above to all the Data Nodes and Secondary Namenode. These files can then be configured for any node specific configuration e.g. in case of a different JAVA HOME on one of the Datanodes.
The following two file ‘masters’ and ‘slaves’ determine the master and salve Nodes in Hadoop cluster.
This file informs about the Secondary Namenode location to hadoop daemon. The ‘masters’ file at Master server contains a hostname Secondary Name Node servers.
The ‘masters’ file on Slave Nodes is blank.
The ‘slaves’ file at Master node contains a list of hosts, one per line, that are to host Data Node and Task Tracker servers.
The ‘slaves’ file on Slave server contains the IP address of the slave node. Notice that the ‘slaves’ file at Slave node contains only its own IP address and not of any other Data Nodes in the cluster.
Got a question for us? Please mention them in the comments section and we will get back to you.