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Table of contents
1.
Introduction
2.
Basic MapReduce Interview Questions
2.1.
1. What is Hadoop MapReduce?
2.2.
2. What is Mapper in MapReduce?
2.3.
3. What is the purpose of Mapper in Hadoop?
2.4.
4. What is the difference between HDFS block and input split?
2.5.
5. What is Combiner in Hadoop MapReduce?
2.6.
6. Comparison between MapReduce and Spark.
2.7.
7. What is Shuffling and Sorting in MapReduce?
3.
Intermediate MapReduce Interview Questions
3.1.
8. What is JobTracker?
3.2.
9. How to set reducers and mappers for Hadoop jobs?
3.3.
10. What is RecordReader?
3.4.
11. How much space will the split occupy?
3.5.
12. What is Partitioner?
3.6.
13. Which main configuration parameters are specified in MapReduce?
3.7.
14. What are the functions of InputFormat in Hadoop?
4.
Advanced MapReduce Interview Questions
4.1.
15. What are the parameters of mappers?
4.2.
16. What are the parameters of Reducers?
4.3.
17. Can the MapReduce program be written in any other programming language other than Java?
4.4.
18. What is OutputCommitter?
4.5.
19. What are the operations performed by OutputCommitter?
4.6.
20. Explain the uses of PIG?
4.7.
21. What Java version and platform is required to run Hadoop?
5.
Conclusion
Last Updated: Jun 27, 2024
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MapReduce Interview Questions and Answers

Author yuvatimankar
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Ashwin Goyal
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Introduction

MapReduce is a framework that helps us write applications to process large amounts of data in parallel on huge clusters of commodity hardware in an authentic manner. The MapReduce algorithm consists of two essential tasks, Map and Reduce.

MapReduce Interview Questions

The Map takes a set of data and turns it into a different set of data, in which individual elements are divided into tuples. While Reduce takes the output from a map as an input and merges those data tuples into a smaller set of tuples. As the name suggests, the Map job is done before the Reduce task.

Now, let's discuss some of the MapReduce interview Questions with answers. Let's get started!

Basic MapReduce Interview Questions

1. What is Hadoop MapReduce?

MapReduce is the programming example that permits huge scalability across thousands of servers in a Hadoop cluster. It is basically a process layer in Hadoop. In MapReduce, there are two processes: Reducer and Mapper. The Mapper processes the input data, which is in the form of a file/directory that resides in HDFS. And reducer takes a moderate pair of keys and values produced by Map.

2. What is Mapper in MapReduce?

Mapper is the user-defined program that directs the input split in key/value pairs according to the code design.

3. What is the purpose of Mapper in Hadoop?

Mapper helps to convert the input split into (key, value) pairs. For each data block on HDFS, there will be a mapper.

4. What is the difference between HDFS block and input split?

HDFS block is the physical section of the disk that contains a minimum amount of data that can be read or written. While InputSplit is the logical section of data generated by the InputFormat specified in the MapReduce job configuration.

5. What is Combiner in Hadoop MapReduce?

Combiner, also known as semi-reducer, is an optional class to merge the map-out records using the same key. The main role of the combiner is to accept input from Map Class and pass those (key, value) pairs to the reducer class.

6. Comparison between MapReduce and Spark.

CriteriaSparkMapReduce
Standalone modeCan work independentlyRequire Hadoop
Processing speedsExceptional speedGood
Ease of useAPIs for Java, Python, and Scala.Require extensive Java program
VersatilityNot optimized for machine learning and real-time applicationsOptimized for machine learning and real-time applications

7. What is Shuffling and Sorting in MapReduce?

Sorting and shuffling are two important processes operating in parallel during the working of the Mapper & reducer. Shufflin is the process of transferring data from the Mapper to the reducer. MapReduce automatically sortsMapReduce automatically sorts the output key and value pairs between the reduce & map phases before moving to the reducer.

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Intermediate MapReduce Interview Questions

8. What is JobTracker?

It is a Hadoop service that helps with the processing of MapReduce jobs in the cluster. It tracks and submits the jobs to particular nodes which has data. Only one JobTracker runs on one Hadoop cluster on its JVM process. All the jobs come to a halt if JobTracker goes down.

9. How to set reducers and mappers for Hadoop jobs?

You can configure the variable JobConf to set a number of mappers & reducers. 

Syntax:

job.setNumMaptasks()
job.setNumReduceTasks()

10. What is RecordReader?

RecordReader basically converts the byte-oriented view of the input that is given by InputSplit and shows a record-oriented view for the Reducer and Mapper tasks for processing. 

11. How much space will the split occupy?

Input split is the logical presentation of Block size. Usually, one split is equal to one block, but both block and split sizes can be customized. 

12. What is Partitioner?

A partitioner partition the key/value pair of intermediate Map outputs. It partitions the data with the help of a user-defined condition, which works similarly to the hash function. The total number of partitions is equal to the number of Reducer tasks for the job. 

13. Which main configuration parameters are specified in MapReduce?

The configuration parameters that are required to be specified to perform the Map and reduce jobs are as follows:

  • The Output location of the job in HDFS.
     
  • The input location of the job in HDFS.
     
  • The .jar file for reducer, mapper, and driver classes
     
  • Format of the input and output.

14. What are the functions of InputFormat in Hadoop?

InputFormat describes the input specifications for a job. The functions performed by the InputFormat are as follows:

  • It validates the input specification of the job.
     
  • It provides an implementation of RecordReader for extracting input records from the instances above for further processing of Mapper.
     
  • It splits the input files into logical instances. 

Advanced MapReduce Interview Questions

15. What are the parameters of mappers?

The parameters of mappers are ;

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

The first two parameters LongWritable and text represents input parameters and the last two represent output parameters. 

16. What are the parameters of Reducers?

The parameters of reducers are:

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

The first two parameters IntWritable and text represent intermediate output parameters and the last two parameters IntWritable and text represent final output parameters. 

17. Can the MapReduce program be written in any other programming language other than Java?

Yes, we can write MapReduce in many languages, such as C++, Java, R, and scripting languages( PHP, Python). Any language that is able to read from stdin and write from stdout can work.

18. What is OutputCommitter?

It describes the commit of the MapReduce task. The default class available for OutputCommitter in MapReduce is FileOutputCommitter. 

19. What are the operations performed by OutputCommitter?

Operations performed by OutputCommitter are as follows:

  • It creates a short-term output directory for the job at the time of initialization.
     
  • After this, it removes the short-term output directory after job completion.
     
  • Then it sets up the task's temporary output.
     
  • After this, it identifies is a task requires commit or not. And if required, commit is applied.
     
  • JobCleanup, JobSetup, and TaskCleanup are important tasks at the time of output commit.

20. Explain the uses of PIG?

PIG can be used in three categories:

  • To research raw data.
     
  • For iterative processing
     
  • ETL data pipeline: It helps in populating data warehouses. The pig can pipeline this data to an external application, it waits until it is finished, so that it receives the processed data and continue from there. 

21. What Java version and platform is required to run Hadoop?

For Hadoop, Java 1.6.x or higher versions are good, preferable for Sun Microsystems. Windows and Linux are the supported Operating Systems, but MAC OS/X, BSD, and Solaris are more popular to work with.

Conclusion

This blog has seen easy, intermediate, and advanced MapReduce interview questions with detailed answers. If you liked this MapReduce interview questions and answers, you can check out other blogs related to MapReduce. You can check out  MapReduce fundamentalsthe synchronization of tasks in MapReduce, and Hadoop MapReduce.

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