Top 30 Hadoop Interview Questions You Must Prepare

Top 30 Hadoop Interview Questions You Must Prepare
Top 30 Hadoop Interview Questions You Must Prepare

Introduction

With the ingress of Hadoop in 2006, Apache gained momentum and began to deal with unstructured data such as audio, video, GIFs, machine learning training data, or data coming from the social media content, unapproved biological data. Over the last few years, numerous amendments were added to Hadoop.

Robust and reliable tools changed the entire structure of the database with the help of the cloud and other distributed computation technologies. Hadoop, Spark, and a few more contemporary data science tools have made database management efficient and easy to learn.

If you also intend to become a data scientist, then you need to have a command of Hadoop and its environment, read below to know more about Hadoop and Hadoop interview questions.

Read about: 10 Data Scientist Skills You Need in 2021

What is Hadoop?

Apache Hadoop is a data handling platform that manages large datasets in a distributed manner. This framework utilises the MapReduce technique for dividing the data into blocks and allotting these bits to nodes across a cluster. MapReduce computes the data parallelly on each node to generate a distinct output.

Each machine in the cluster can both store and process data. Hadoop stores the data into disks using HDFS. This software comes with limitless scalability options. You can even initiate the system with a single machine and then expand to thousands, by including various sorts of enterprise or commodity hardware.

The Hadoop ecosystem is highly fault-tolerant. Hadoop doesn’t rely on hardware for achieving high availability. In the core, Hadoop is built for detecting bugs at the application layer. When a hardware component malfunctions, the framework can reconstruct the missing parts from other locations by duplicating data across a cluster.

Top 30 Hadoop Interview Questions

For acquiring highly promising job positions such as Data Analyst, Data Scientist, Database Engineer, and other Big Data related fields a clear understanding of Hadoop is required. For having a distinct concept you need to work on both practical and theoretical concepts, interviewers solicit a handful of Hadoop interview questions for analysing the technical knowledge of the candidate.

Understanding the difference between Spark vs Hadoop.

For helping you with your last-minute revision and interview simulation, we have given below a list of Top 30 Hadoop Interview Questions that you must prepare :

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1. List a few vendor-specific distributions of Hadoop?

Cloudera, MAPR, Amazon EMR, Microsoft Azure, IBM InfoSphere, and Hortonworks (Cloudera) are a few different vendor-specific distributions of Hadoop.

2. Name a few different Hadoop configuration files.

A few different Hadoop configuration files are:

  • hadoop-env.sh
  • mapred-site.xml
  • core-site.xml
  • yarn-site.xml
  • hdfs-site.xml
  • Master and Slaves

3. State a few differences between a  regular file system and HDFS?

  • A regular file system has a small block size of data. (approximately 512 bytes) whereas HDFS has larger block sizes at around 64 MB).
  • Multiple disks seek for larger files in a regular file system whereas, in HDFS, data is read sequentially after performing one individual seek.

4. Why is the Hadoop HDFS fault-tolerant?

HDFS is considered to be fault-tolerant as it duplicates data on distinct DataNodes. By default, a block of data is duplicated on three different DataNodes. The data blocks are stored in varied DataNodes. In case one node fails, the data can still be retrieved from the remaining DataNodes. 

5. Name the different modes in which Hadoop runs.

The three modes in which Hadoop runs are :

  • Standalone mode
  • Pseudo-distributed mode
  • Fully-distributed mode

6. Which are the two types of metadata that a NameNode server holds?

The two types of metadata that a NameNode server holds are :

  • Metadata in Disk – This stores the edit log and the FSImage
  • Metadata in RAM – This stores the information about the DataNodes

7. In case you are given an input file of 360 MB, calculate the number of input splits the  HDFS would create and the size of each input split?

By default, each block in HDFS is divided into 128 MB. The size of all the blocks, except for the last block, will be 128 MB. For an input file of 360 MB, there are three input splits in total. The size of each split is 128 MB, 128MB, and 104 MB.

8. Write down the command used for finding the status of blocks and FileSystem health?

For checking the status of the blocks, use the command:

  • hdfs fsck <path> -files -blocks

For checking the health status of FileSystem, use the command:

  • hdfs fsck / -files –blocks –locations > dfs-fsck.log

9. Which command is used for copying data from the local system to the HDFS? 

The following command is used for copying data from the local file system to the HDFS:

hadoop fs –copyFromLocal [source] [destination]

10. When do we use the dfsadmin -refreshNodes command?

It is used to run the HDFS client and refresh the node configuration for the NameNode. 

11. When do we use the rmadmin -refreshNodes command?

It is used to perform administrative tasks for ResourceManager.

12. In a Hadoop cluster who takes care of replication consistency?

In a Hadoop cluster, the NameNode always takes care of the replication consistency. The fsck command is used for providing information about the over and under-replicated block. 

13. What do you mean by under-replicated blocks?

The blocks that do not meet their target replication for the files they belong to are known as under-replicated blocks. HDFS automatically creates new replicas of under-replicated blocks until the target replication is met.

14. What do you mean by over-replicated blocks?

Over-replicated blocks are the blocks that exceed the target replication of the files they belong to. Usually, over-replication doesn’t affect the system, as the HDFS automatically eliminates excess replicas.

15. List the major configuration parameters required in a MapReduce program.

The major configuration parameters required in a MapReduce program are :

  • Input location of the job in HDFS
  • Output location of the job in HDFS
  • Input and output formats
  • Classes containing a map and reduce functions
  • JAR file for mapper, reducer, and driver classes 

16. What has replaced JobTracker from MapReduce v1?

The ResourceManager has replaced JobTracker from MapReduce v1. It is a master process in Hadoop v2.

17. Which YARN command is used to check the status of an application. and kill an application.

The command used to check the status of an application:

yarn application -status ApplicationID

18. Which YARN command is used to kill an application?

The command used to kill or terminate an application:

yarn application -kill ApplicationID

19. Name the three different schedulers available in YARN?

The three different schedulers available in YARN are:

  • FIFO scheduler
  • Capacity scheduler
  • Fair scheduler 

20. What are the building blocks of a Hive architecture?

The building blocks of the Hive architecture are :

  • User Interface
  • Metastore
  • Compiler
  • Execution Engine

21. In how many ways a Pig script can be executed?

A Pig Script can be executed in three different ways :

  • Grunt shell
  • Script file
  • Embedded script

22. What is the code for opening a connection in HBase?

To open a connection in HBase, we use the following code:

Configuration myConf = HBaseConfiguration.create();
HTableInterface usersTable = new HTable(myConf, “users”);

23. What are the components of a Hive query processor?

The components of a Hive query processor are:

  • Parser
  • Semantic Analyzer
  • Execution Engine
  • User-Defined Functions
  • Logical Plan Generation
  • Physical Plan Generation
  • Optimizer
  • Operators
  • Type checking

24. Write down a query for inserting a new column(new_col INT) into a hive table (h_table) at a position before an existing column (x_col).

The following query is used for inserting a new column:

ALTER TABLE h_table
CHANGE COLUMN new_col INT
BEFORE x_col

25. What are the different types of complex data types in Pig?

Pig has three complex data types, they are:

  • Tuple 

A tuple is an ordered set of fields that can contain different data types for each field. It is represented by braces ().

Example: (1,3)

  • Bag 

A bag is a collection of tuples enclosed within curly braces {}.

Example: {(1,4), (3,5), (4,6)}

  • Map 

A map is a set of key-value pairs used to represent data elements. It is represented by square brackets [ ].

Example: [key#value, key1#value1,….]

26. What are the various kinds of diagnostic operators present in Apache Pig?

The diagnostic operators present within Apache Pig are:

  • Dump 
  • Describe 
  • Explain 
  • Illustrate 

27. Using which command we can set the number of mappers and reducers for a MapReduce job?

The number of mappers and reducers for a MapReduce job can be set using the following command :

-D mapred.map.tasks=5 –D mapred.reduce.tasks=2

28. What are the different formats for the output of MapReduce?

The input and output formats supported by Hadoop are:

  • TextOutputFormat
  • SequenceFileOutputFormat
  • MapFileOutputFormat
  • SequenceFileAsBinaryOutputFormat
  • DBOutputFormat

29. What is the difference between Apache Pig and MapReduce?

Apache PigMapReduce
A high-level language used to perform join operations.A low-level language that cannot perform join operation efficiently.
Compatible with all versions of Hadoop.It is not backward compatible.
It has fewer lines of code.It has higher lines of code.

30. What is the function of the eval tool in Sqoop?

The Sqoop eval tool is used for executing user-defined queries against their respective database server and previewing the desired results in the command-line console.

Hadoop Interview Questions on CodeStudio

You should practise a few data-related coding problems so that you get familiarised with Data Manipulation libraries. A few relevant problems are:

Frequently Asked Questions

What is the main goal of Hadoop?

The prime goal of Apache’s Hadoop is to back up everything associated with any client’s data. All the data in the future will not be stored on just one SQL server, rather it will be placed on a Hadoop cluster and will be retrieved in real-time.

What is Hadoop used for?

Hadoop is used for big data storage and manipulation. In Hadoop, data is preserved using affordable commodity servers that function as clusters. It is a distributed file system that allows parallel computing and fault tolerance. Hadoop MapReduce programming model is used for swift storage and retrieval of data from its host nodes.

How do I prepare for a big data interview?

Follow these steps to crack a big data interview:

Step 1: Read carefully about the latest tools and technologies. After attaining the idea, you need to update your skillset with the required technologies and tools.
Step 2: Follow Big Data Interview Preparation Tips from previously selected candidates.
Step 3: Do a few projects on Big Data Frameworks.
Step 4: Read about the latest interview questions on Big Data.

What is Hadoop not good for?

Hadoop is not good for small files, it cannot handle the live data firmly, provides slow processing speed, and is not efficient for iterative processing. It is not recommended for caching, etc. You must never use Hadoop for problems that are not compatible with the MapReduce technique.

What is replacing Hadoop?

Apache Spark is often regarded as the successor to the widely used Hadoop, Apache Spark is used as a computational engine for Hadoop data. In contrast to Hadoop, Spark comes with a hike in computational speed and completely backs up the various applications offered by the tool.

What is better than Hadoop?

Apache’s Spark runs 100 times faster in-memory, and 10 times faster on disk in comparison to Hadoop. It is also widely used for sorting hundred TB of data three times faster than Hadoop MapReduce on around ten percent of the machines.

Key Takeaways

After knowing the latest Hadoop interview questions you can start applying for the profile of Data Scientist, Data Analyst, Database Engineer, and other Hadoop-related profiles. We have covered the prime topics including HDFS, MapReduce, YARN, Hive, Pig, HBase, and Sqoop.

Data Science offers a lot of statistical insights to the top tech-based enterprises such as Facebook, Google, Microsoft, Zomato, etc. Thereby these firms are hiring data scientists and analysts at a very large scale.

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If you intend to build a career in data science then you should be familiar with the latest concepts of Hadoop, this will assist you in cracking placement interviews and amplify your chances of getting shortlisted. If you want to get hands-on experience on Hadoop, you can check out the problems on CodeStudio.

You can check out our courses on Data Science and Machine Learning with Hadoop in case you wish to build a few projects on your own under the guidance of our mentors.

By Vanshika Singolia