Top MapReduce Interview Questions and Answers

MapReduce refers to two different tasks that Hadoop performs. It is a programming paradigm and a connected implementation for processing big data sets with a distributed algorithm. It is simple for those who know clustered scale-out data processing solutions but might be difficult to grasp for someone who is new to this topic. If you are preparing for a big data interview and wondering what questions related to MapReduce will be asked, then this post will help you. This article covers the top frequently asked MapReduce interview questions to help you ace your next job interview.

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Top MapReduce Interview Questions and Answers

The following are the most important MapReduce interview questions and answers for freshers and experienced candidates.

Q1. How can we rename the output file?

Ans. We can rename by implementing multiple format output class.

Q2. Define distributed cache?

Ans. It is used on web servers to provide non-local storage for serving multiple regions and transactions throughout.

Q3. Name some of the components of MapReduce Job?

Ans. Some of the components of MapReduce Job are:

• Mapper class
• Main driver class
• Reducer class

Q4. What are the benefits of MapReduce programming?

Ans. The advantages of MapReduce programming are:

• Scalability
• Flexibility
• Security
• Parallel Processions
• Cost-effective

Q5. Can we write a MapReduce program in any language other than Java?

Ans. Yes, we can write a MapReduce program in a variety of programming languages such as Python, PHP, C++, and R.

Q6. What is the purpose of shuffling and sorting?

Ans. It determines which reducer instance will receive which intermediate values and keys. The process of sending data to the reducer from the mapper is known as shuffling, while sorting is used to sort the output key-value pairs from the mapper.

Q7. What are the main job control options specified by MapReduce?

Ans. The main job control options specified by MapReduce are:

• submit ()
• waitforcompletion(boolean)

Q8. Can Reducers communicate with each other?

Ans. According to the Hadoop MapReduce programming paradigm, reducers work in isolation. Thus, they cannot communicate with each other. 

Q9. What is the use of MapReduce partitioner?

Ans. The use is to ensure that all the value of a single key gets to the same reducer, ultimately which helps the distribution of map output over the reducers.

Also Read: Mastering Hadoop – Pros and Cons of Using Hadoop technology

Q10. Name some important parameters of a mapper?

Ans. Following are the important parameters of a mapper:

• Text and Intwritable
• Longwritable and text

Q11. What happens when a node fails during the write process?

Ans. In that case, a new mode that has the other data nodes opens up until the file is closed.

Q12. How can you split 100 lines of input as a single split?

Ans. This can be done using class NLineInputFormat.

Q13. What is InputFormat?

Ans. It explains the input specification for a MapReduce Job. It depends on the InputFormat of the job to split up the input file into logical InputSplit instances.

Also Read: Career Advantages of Hadoop Certification!

Q14. What are the benefits of map side join?

Ans. The benefits of map side join are:

• Helps in decreasing the cost that is incurred for sorting in the reduce stages
• Helps in developing the performance of the task by reducing the time to finish the task

Q15. What are the primary phases of a reducer?

Ans. The primary phases of a reducer are:

• Sort
• Shuffle
• Reduce

Q16. How can you control reporting in Hadoop?

Ans. By using Hadoop-metrics.properties

Also Read: Top Hadoop Interview Questions & Answers

Q17. Is it possible to search files using wildcards?

Ans. Yes.

Q18. What is YARN?

Ans. YARN stands for Yet Another Resource Negotiator is a cluster management technology.

Q19. Explain the difference between Input Split and HDFS block.

Ans. The difference between Input Split and HDFS block is that HDFS block is a physical location to store data while Input Split is a logical reference to data. The Input Split does not contain any data.

Q20. Name the configuration parameters specified in MapReduce.

Ans. Below are the configuration parameters specified in MapReduce:

• The input location of the job in HDFs
• The output location of the job in HDFS
• The input’s and output’s format
• The classes that contain the map and reduce functions

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Conclusion

That sums up our MapReduce interview questions and answers blog. We hope these interview questions will help you prepare well for your job interview.