Hadoop Interview Questions: Essential Examples To Help You Prepare

hadoop interview questionsApache Hadoop is a free, open-source, Java-based software framework used to store, maintain, and process large-scale sets of data across numerous clusters of commodity hardware. If you’ve applied for a job as an analytics engineer, a Hadoop developer, or even a Java engineer who might end up using Hadoop on the job, it’s worth it to review some Hadoop interview questions in preparation.

Even if you’re still training to be a Hadoop developer, you can still use this guide as a way to review important points while you study. Don’t forget to check out a course like this Hadoop training tutorial for additional study help.

Why is Hadoop useful?

Hadoop is fault tolerant, meaning the system will simply redirect to another location and resume work when a node is lost. Hadoop is also schema-less and can absorb data of all types, sources, and structures, allowing for deeper analysis.

If you want to learn more reasons why Hadoop is useful, check out this course on working with big data.

Which directory does Hadoop install to?

Hadoop is installed in cd/usr/lib/hadoop-0.20/

What are the four modules that make up the Apache Hadoop framework?

  • Hadoop Common, which contains the common utilities and libraries necessary for Hadoop’s other modules.
  • Hadoop YARN, the framework’s platform for resource-management
  • Hadoop Distributed File System, or HDFS, which stores information on commodity machines
  • Hadoop MapReduce, a programming model used to process  large-scale sets of data

Which modes can Hadoop be run in? List a few features for each mode.

  • Standalone, or local mode, which is one of the least commonly used environments. When it is used, it’s usually only for running MapReduce programs. Standalone mode lacks a distributed file system, and uses a local file system instead.
  • Pseudo-distributed mode, which runs all daemons on a single machine. It is most commonly used in QA and development environments.
  • Fully distributed mode, which is most commonly used in production environments. Unlike pseudo-distributed mode, fully distributed mode runs all daemons on a cluster of machines rather than a single one.

Where are Hadoop’s configuration files located?

Hadoop’s configuration files can be found inside the conf sub-directory.

List Hadoop’s three configuration files.

  • hdfs-site.xml
  • core-site.xml
  • mapred-site.xml

What are “slaves” and “masters” in Hadoop?

In Hadoop, slaves are a list of hosts for task tracker servers and datanodes. Masters list hosts for secondary namenode servers.

What is /etc/init.d?

/etc/init.d is a Linux directory. In Hadoop, you use this to check the status of daemons or check where they’re located.

What is a Namenode?

Namenode exists at the center of the Hadoop distributed file system cluster. It manages metadata for the file system, and datanodes, but does not store data itself.

How many Namenodes can run on a single Hadoop cluster?

Only one Namenode process can run on a single Hadoop cluster. The file system will go offline if this Namenode goes down.

What is a datanode?

Unlike Namenode, a datanode actually stores data within the Hadoop distributed file system. Datanodes run on their own Java virtual machine process.

How many datanodes can run on a single Hadoop cluster?

Hadoop slave nodes contain only one datanode process each.

What is job tracker in Hadoop?

Job tracker is used to submit and track jobs in MapReduce.

How many job tracker processes can run on a single Hadoop cluster?

Like datanodes, there can only be one job tracker process running on a single Hadoop cluster. Job tracker processes run on their own Java virtual machine process. If job tracker goes down, all currently active jobs stop.

What sorts of actions does the job tracker process perform?

  • Client applications send the job tracker jobs.
  • Job tracker determines the location of data by communicating with Namenode.
  • Job tracker finds nodes in task tracker that has open slots for the data.
  • Job tracker submits the job to task tracker nodes.
  • Job tracker monitors the task tracker nodes for signs of activity. If there is not enough activity, job tracker transfers the job to a different task tracker node.
  • Job tracker receives a notification from task tracker if the job has failed. From there, job tracker might submit the job elsewhere, as described above. If it doesn’t do this, it might blacklist either the job or the task tracker.

Learn more about Hadoop with this course on the fundamentals of Apache Hadoop.

How does job tracker schedule a job for the task tracker?

When a client application submits a job to the job tracker, job tracker searches for an empty node to schedule the task on the server that contains the assigned datanode.

What does the mapred.job.tracker command do?

The mapred.job.tracker command will provide a list of nodes that are currently acting as a job tracker process.

What is “PID”?

PID stands for Process ID.

What is “jps”?

jps is a command used to check if your task tracker, job tracker, datanode, and Namenode are working.

Is there another way to check whether Namenode is working?

Besides the jps command, you can also use: /etc/init.d/hadoop-0.20-namenode status.

How would you restart Namenode?

To restart Namenode, you could either write:

  • sudo hdfs
  • su-hdfs
  • /etc/init.d/ha, press enter, then /etc/init.d/hadoop-0.10-namenode start

and then press Enter, or you could simply click stop-all.sh, then select start-all.sh.

What is “fsck”?

fsck standards for File System Check.

What are the port numbers for job tracker, task tracker, and Namenode?

The port number for job tracker is 30, the port number for task tracker is 60, and the port number for Namenode is 70.

What is a “map” in Hadoop?

In Hadoop, a map is a phase in HDFS query solving. A map reads data from an input location, and outputs a key value pair according to the input type.

What is a “reducer” in Hadoop?

In Hadoop, a reducer collects the output generated by the mapper, processes it, and creates a final output of its own.

What are the parameters of mappers and reducers?

The four parameters for mappers are:

  • LongWritable (input)
  • text (input)
  • text (intermediate output)
  • IntWritable (intermediate output)

The four parameters for reducers are:

  • Text (intermediate output)
  • IntWritable (intermediate output)
  • Text (final output)
  • IntWritable (final output)

Is it possible to rename the output file, and if so, how?

Yes, it is possible to rename the output file by utilizing a multi-format output class.

List the network requirements for using Hadoop.

  • Secure Shell (SSH) for launching server processes
  • Password-less SSH connection

Which port does SSH work on?

SSH works on the default port number, 22.

What is streaming in Hadoop?

As part of the Hadoop framework, streaming is a feature that lets engineers code with MapReduce in any language, as long as that programming language is able to accept and produce standard output. Even though Hadoop is Java-based, the chosen language doesn’t have to be Java. It can be Perl, Ruby, etc. If you want to use customization in MapReduce, however, Java must be used.

Hadoop is Java-based, remember, so it’s pretty useful to know Java if you want to work with the framework. Check out this guide on learning Java for more information.

What is the difference between Input Split and an HDFS Block?

InputSplit and HDFS Block both refer to the division of data, but InputSplit handles the logical division while HDFS Block handles the physical division.

What does the file hadoop-metrics.properties do?

The hadoop-metrics.properties file controls reporting in Hadoop.

You can prepare for your analytics engineering interview with the above Hadoop interview questions, or check out this Apache Hadoop essentials training course for additional reference material and guidance. If you’re looking for a more expert-level approach, check out this advanced Hadoop course

For more on data analytics, check out this beginners data analytics course, or this data analytics training course for all skill levels.