Top 26 Hadoop Administration Interview Questions You Must Prepare 14.Jun.2024

If the user does not want to compress the data for a particular job then he should create his own configuration file and set the mapred.output.compress property to false. This configuration file then should be loaded as a resource into the job.

The easiest way of doing this is to run the command to stop running shell script i.e. click on Once this is done, restarts the NameNode by clicking on

Ubuntu or Linux is the most preferred operating system to run Hadoop. Though Windows OS can also be used to run Hadoop but it will lead to several problems and is not recommended.

Fair Scheduling is the process in which resources are assigned to jobs such that all jobs get to share equal number of resources over time.

Fair Scheduler can be used under the following circumstances:

i) If you wants the jobs to make equal progress instead of following the FIFO order then you must use Fair Scheduling.

ii) If you have slow connectivity and data locality plays a vital role and makes a significant difference to the job runtime then you must use Fair Scheduling.

iii) Use fair scheduling if there is lot of variability in the utilization between pools.

Capacity Scheduler allows runs the hadoop mapreduce cluster as a shared, multi-tenant cluster to maximize the utilization of the hadoop cluster and throughput.

Capacity Scheduler can be used under the following circumstances:

i) If the jobs require scheduler detrminism then Capacity Scheduler can be useful.

ii) CS's memory based scheduling method is useful if the jobs have varying memory requirements.

iii) If you want to enforce resource allocation  because you know very well about the cluster utilization and workload then use Capacity Scheduler.

Yes, it is possible to copy files across multiple Hadoop clusters and this can be achieved using distributed copy. DistCP command is used for intra or inter cluster copying.

This file provides an environment for Hadoop to run and consists of the following variables-HADOOP_CLASSPATH, JAVA_HOME and HADOOP_LOG_DIR. JAVA_HOME variable should be set for Hadoop to run.

Using the command – Hadoop job –list, gives the list of jobs running in a Hadoop cluster.

The NameNode should never be reformatted. Doing so will result in complete data loss. NameNode is formatted only once at the beginning after which it creates the directory structure for file system metadata and namespace ID for the entire file system.

The task will be started again on a new TaskTracker and if it fails more than 4 times which is the default setting (the default value can be changed), the job will be killed.

Hadoop FSCK (File System Check) command is used to check missing blocks.

FIFO Scheduler – This scheduler does not consider the heterogeneity in the system but orders the jobs based on their arrival times in a queue.

COSHH- This scheduler considers the workload, cluster and the user heterogeneity for scheduling decisions.

Fair Sharing-This Hadoop scheduler defines a pool for each user. The pool contains a number of map and reduce slots on a resource. Each user can use their own pool to execute the jobs.

  • SSH is required to run - to launch server processes on the slave nodes.
  • A password less SSH connection is required between the master, secondary machines and all the slaves.

Hadoop job –kill jobID

Text Mining, Graph Analysis, Semantic Analysis, Sentiment Analysis, Recommendation Systems.

jps command is used to verify whether the daemons that run the Hadoop cluster are working or not. The output of jps command shows the status of the NameNode, Secondary NameNode, DataNode, TaskTracker and JobTracker.

The file system goes offline whenever the NameNode is down.

  • To add new nodes to the HDFS cluster, the hostnames should be added to the slaves file and then DataNode and TaskTracker should be started on the new node.
  • To remove or decommission nodes from the HDFS cluster, the hostnames should be removed from the slaves file and –refreshNodes should be executed.

Memory-System’s memory requirements will vary between the worker services and management services based on the application.

Operating System - a 64-bit operating system avoids any restrictions to be imposed on the amount of memory that can be used on worker nodes.

Storage- It is preferable to design a Hadoop platform by moving the compute activity to data to achieve scalability and high performance.

Capacity- Large Form Factor (3.5”) disks cost less and allow to store more, when compared to Small Form Factor disks.

Network - Two TOR switches per rack provide better redundancy.

Computational Capacity- This can be determined by the total number of MapReduce slots available across all the nodes within a Hadoop cluster.

Nothing could have actually wrong, if there is huge volume of data because data replication usually takes times based on data size as the cluster has to copy the data and it might take a few hours.

It is always better to deploy a secondary NameNode on a separate standalone machine. When the secondary NameNode is deployed on a separate machine it does not interfere with the operations of the primary node.

The configuration files are located in “conf” sub directory. Hadoop has 3 different Configuration files- hdfs-site.xml, core-site.xml and mapred-site.xml.

NameNode, DataNode, TaskTracker and JobTracker