MapReduce Interview Questions and Answers

Hope you are good! You might be here because you want to appear for Hadoop MapReduce Developer interview either as a fresher or experienced. Well, the good news is that you have reached the right place today. Here, we have compiled a list of most important questions that are frequently asked during interviews. All the questions are prepared by the Hadoop MapReduce experts and we try to compile precise answers for each of the questions to guide you the best way to success. Do comment your experience. -- Happy job hunting!

MapReduce Interview Questions

1. How will you define shuffling and sorting in MapReduce?
2. Name the two major components in MapReduce?
3. What is MapReduce and how it is suitable for processing large datasets?
4. How will you differentiate the Identity Mapper and the Chain Mapper?
5. Do you know about the Job control options used in MapReduce?
6. Can you please explain the InputFormat in MapReduce?
7. Do you know the difference between HDFS and InputSplit?
8. Name the language to manage the data flow and datasets in organizations?
9. What is the TextInputFormat?
10. How can you define the job tracker?
11. Define the Record Reader in the MapReduce?
12. What is the difference between the Pig and the MapReduce?
13. What is YARN in Hadoop MapReduce?
14. How will you define data serialization in Hadoop MapReduce?
15. How will you define data deserialization in Hadoop MapReduce?
16. What is a combiner and how it works when compared to Reducer?
17. Do jobs are tasks are different in MapReduce or they have the same meaning?
18. Define the primary phases for the reducer?
19. How can you search files in Hadoop MapReduce?
20. How will you define the storage nodes and compute nodes in MapReduce?

MapReduce Interview Questions and Answers

MapReduce also termed as Hadoop Core, is a programming framework that has the capability to process large data sets and big data files across thousands of servers in a Hadoop cluster. MapReduce is made up of two main elements i.e. Map() and Reduce() functions. Map () collects the data from multiple sources and map the similar data together. Further, Reduce () function divided the large data sets outcome into smaller chunks for further processing.In this article, we will discuss on Hadoop interview questions and answers for freshers and experience to assess your knowledge of Hadoop MapReduce tool. 

MapReduce Interview Questions and Answers for Freshers

1. How will you define shuffling and sorting in MapReduce?

When data is transferred from mapper to shuffler, it is termed as shuffling. Once data is transferred to the reducer, it needs to be filtered based on Company requirement that is termed as sorting.

2. Name the two major components in MapReduce?

The two major components of MapReduce are Map () and Reduce () functions. Map () collects the data from multiple sources and map the similar data together. Further, Reduce () function divided the large datasets outcome into smaller chunks for further processing.

3. What is MapReduce and how it is suitable for processing large datasets?

MapReduce also termed as Hadoop Core, is a programming framework that has the capability to process large data sets and big data files across thousands of servers in a Hadoop cluster.

4. How will you differentiate the Identity Mapper and the Chain Mapper?

Identity Mapper is the default class in the MapReduce that executes automatically if no other class is defined in the scenario. At the same time, Chain Mapper class executes through chain operations through the output of one Mapper class becomes the input for the other class.

5. Do you know about the Job control options used in MapReduce?

There are two job control options in MapReduce. These are-

Read: Big Data Hadoop Developer Career Path & Future Scope

Job.Submit () – This control option submits the job to the cluster. Job.waitforCompletion () – Once the job is submitted to the cluster, you need to wait until it does not complete.

6. Can you please explain the InputFormat in MapReduce?

Input Format is another important feature in MapReduce that defines the Input specifications for a job. Let us see how it works actually –

Validates the Input specification for a job, Splits the Input into logical instances with InputSplit and each of the instances is mapped to the Mapper class further. Provides implementation to extract records from each of the instances.

7. Do you know the difference between HDFS and InputSplit?

HDFS (Hadoop Distributed File System) distributes data into physical divisions while InputSplit splits data into logical instances.

8. Name the language to manage the data flow and datasets in organizations?

To manage the large datasets, you should always opt for MapReduce in Hadoop while data flow form Input source to Output source can be managed through Pig programming language.

9. What is the TextInputFormat?

This is the default format for text files where data into files is broken into lines and mapped with the key values.

MapReduce Interview Questions and Answers for Experienced

10. How can you define the job tracker?

MapReduce job tracker is used to process jobs in a Hadoop cluster. It is responsible to submit the job to various nodes and track their status as well. If job tracker goes down then all jobs may halt in mid only.

Read: Teradata Interview Questions and Answers

11. What is the difference between the Pig and the MapReduce?

Pig is a data flow language that manages the data flow when data is transferred from input source to output source. At the same time,MapReduce is a programming framework that has the capability to process large data sets and big data files across thousands of servers in a Hadoop cluster.

12. Define the Record Reader in the MapReduce?

This function reads the records that are broken down into logical instances through Input Split function.

13. What is YARN in Hadoop MapReduce?

YARN stands for Yet Another Source Navigator and it is taken as the next generation MapReduce and works on flaws detected in the previous versions.The latest version is more scalable and robust to manage the jobs, resources or scheduler etc.

14. How will you define data serialization in Hadoop MapReduce?

When data is transmitted over a network across various nodes in a Hadoop cluster, it has to be converted into byte stream data from object data that is named as Serialization in Hadoop.

15. How will you define data deserialization in Hadoop MapReduce?

Deserialization is the reverse process of Data serialization where bytes are converted to data objects at the receiver end. Basically, the process is same as encoding and decoding of data in wireless networks.

16. What is a combiner and how it works when compared to the Reducer?

The Combiner is a mini reducer to perform to reduce jobs on the local network. It is generally used for network optimization when a number of outputs are generated from each mapped class.

17. Do jobs are tasks are different in MapReduce or they have the same meaning?

A job can be divided into multiple tasks in Hadoop cluster.

Read: Hadoop HDFS Commands Cheat Sheet

18. Define the primary phases for the reducer?

The three primary phases of the reducer are – Shuffle, Sort, and Reduce.

19. How can you search files in Hadoop MapReduce?

This is possible to search files in Hadoop MapReduce with wildcards

20. How will you define the storage nodes and compute nodes in MapReduce?

The storage node is the place where file system resides to store data for the further processing. And the compute node is the place where the actual logic of the business is executed.

Kindly, refer to the links given below to explore all the Hadoop related interview questions and Answers:

• Hadoop Interview Questions and Answers 
• Splunk Interview Questions and Answers 
• Spark Interview Questions and Answers 
• Ping Interview Questions and Answers
• Hive Interview Questions and Answers
• HBase Interview Questions and Answers
• HDFS Interview Questions and Answers
• Strom Interview Questions and Answers
• KafKa Interview Questions and Answers

FaceBook Twitter Google+ LinkedIn Pinterest Email