MapReduce Interview Questions

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So, You still have the opportunity to move ahead in your career in MapReduce Development. Mindmajix offers Advanced MapReduce Interview Questions 2024 that helps you in cracking your interview & acquire a dream career as MapReduce Developer.

Top MapReduce Interview Questions 

1. How does MapReduce work?  

2. Give a short illustration of how MapReduce works in general? 

3. Define the main components of MapReduce? 

4. What is the meaning of Partitioner and what is its usage?

5. What is the meaning of shuffling in MapReduce?

6. Define Chain and Identity Mapper? 

7. What are the different parameters of mapper and reducer functions?

8. Where is the output of the mapper class stored?  

MapReduce Interview Questions and Answers

1. How does MapReduce work?  

Ans: MapReduce has two phases. The first phase being the map phase and the second phase being the reduce phase. The map phase includes the counting of words or sorting out data. The second phase that is reduced as the name suggests includes reducing the data and aggregating them. Hence, the data first gets divided for analysis.

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2. Give a short illustration of how MapReduce works in general? 

Ans: The data used in MapReduce work is generally massive and huge data. The file or the data consists of two factors, one is the key and the other is value. If five files are taken as an example, the data of some of the files must be repetitive. In this way, the MapReduce framework sorts the data in a particular format and sends it to the reducer for the aggregate calculation. 

3. Define the main components of MapReduce?  

Ans: Following are the main components of MapReduce: 

• Main Class: This includes providing the main parameters for the job like providing the different data files for sorting.  
• Mapper Class: Mapping is mainly done in this class. The map method is executed.  
• Reducer Class: The aggregate data is put forward in the reducer class. Data are reduced in this class. 

4. What is the meaning of Partitioner and what is its usage? 

Ans: Partitioner works with the hash function which helps in controlling the partitioning of the various output data of MapReduce. This process also helps in providing the input data to the reducer. The total number of partitioners is actually equal to the total number of reducers. 

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5. What is the meaning of shuffling in MapReduce?

Ans: Shuffling is part of the first phase of the MapReduce framework. The mapper and reduces are the two main components of this programming model. The process of transferring the output data from the mapper to providing input data to the reducer is known as shuffling. The data which is transferred through the shuffling process is already sorted for the reducing phase. Hence, that data acts as the input to the reducers.            

6. Define Chain and Identity Mapper? 

Ans: Chain Mapper is basically dividing the single mapping task across different mapper classes. Hence, the single data and information get sorted in this way through a series of chain operations. The data can be out through any number of mappers.

For example, the data gets sorted by the first mapper and then this output becomes the input for the second mapper where it gets sorted again. This continues till it reaches the last mapper whose output becomes the input for the reducer.  

Identity Mapper is a default mapper. Here, there are no specifically defined mapper calls and hence the data is just put through one single class of mapper and gets sorted in this way.  

7. What are the different parameters of mapper and reducer functions?

Ans:

Mapper function

1. Mapping or mapper function consists of the following parameters like LongWritabke and Text  
2. Text and InWritable  

The first one consists of Input data and the latter one consists of intermediate output data.

Reducer Function 

1. InWritable and Text 

The first one consists of Intermediate Output data and the latter one consists of Final Output Data.    

8. Where is the output of the mapper class stored?  

Ans: The mapper class consists of mapper nodes. The mapper output values are stored in these mapper nodes. They are basically kept in the local file system.  

9. List the different configuration parameters that are needed to do the job of the MapReduce framework?

Ans: The user has to most definitely specify the following types of parameters:

1. The input location of the data or the job needed to be specified in the file system 
2. The output location of the data also needed to be specified in the system 
3. The format of the input design 
4. The format of the output design  
5. Defining the specific class of the mapper function  
6. Defining the specific class of the reducer function  
7. JAR file which consists of all the mapper and reducer classes  

10. How can the reducers communicate?

Ans: Different reducers can't communicate with each other. They work in isolation.  

11. Define Text Input Format?

Ans: The text input format is just the default format for text files or the input data. The files are broken within the text input format. The line of the text refers to the value and the key is referred to the position. These two are the main components of data files.  

12. What is the meaning of Input Format?

Ans: Input Format is a type of MapReduce programming feature. This feature helps in specifying the different job requirements. It has the following function: 

1. Dividing the input files or the input data into different instances which are called Input Split. The total number of split files is then assigned to the different mapping classes to individual mappers. They are divided in a logical manner. 
2. The Input Format also helps in the validation of the input specification job. 
3. Due to more mapper processes, the text input also helps in the implementation of Record Reader which in turn helps in extracting inputs or data.  

13. Differentiate between Input Split and HDFS Block?

Ans: Both Input Split and HDFS Block divide the data into various files. However, the first one splits the data in a logical manner while the latter one makes a physical division of the data. Input Split controls the split size, the number of mappers but the HDFS Block is fixed that is 64 MB for 1 GB data.  

14. Splitting 100 lines of input in the form of a single split in MapReduce. Is this possible?

Ans: Splitting such a huge data worth of input I'd possible in the form of a single split is only possible by using the Class NLine Input Format.  

15. What are the differences between a reducer and a combiner?  

Ans: All the local tasks of reducing the local data files are done with the help of a combiner. This mainly works on the Map Output. Just like a reducer, it also produces the output for the reducer's input. Combiner has other uses too like it is often used for the job of network optimization especially when the outputs increase in numbers by the map generator.

Combiner also varies from the reducer in many ways like for example, a reducer is limited but however, a combiner has limitations like the input data or the output data, and the values must be similar to the output data of the mapper.

A combiner can also work on the commutative function like for example; it can operate on subsets of the values and keys of the data. A combiner can get its input from only one single mapper whereas; a reducer gets its input from several numbers of mappers. 

16. In MapReduce, when is the best time to use a combiner?

Ans: The efficiency of MapReduce is increased by using a combiner. It helps in aggregating the data locally and hence helps in reducing the huge bulk of data while transferring them to the reducers. The combiner uses the reducer code when the function is commutative.  

17. What do you mean by Job Tracker? What are the functions performed by it?  

Ans: A job tracker is the best way to track the submitted output data. It has the following functions -  

1. The job tracker takes the application of the data submission.  
2. To know the data location, the job tracker communicates with one of the nodes that are the name mode.  
3. Another such function of the job tracker is to determine what to do after the task failure.  
4. The job of the task tracker is also monitored by the Job Tracker.  

18. What is the meaning of the term heartbeat which is used on HDFS? 

Ans: The signal which is used in HDFS is known as the heartbeat. This signal is mainly passed between two types of nodes namely data nodes and name nodes. This occurs between the job tracker and the task tracker. It is considered to have a poor heartbeat if the signal doesn't work properly and if some issues arise with the two nodes or the trackers.

19. MapReduce framework consists of distributed caches. What is meant by distributed caches?  

Ans: Sharing files and data is mainly done by the distributed cache. This is considered to be an important feature as it allows the distribution of files across all the nodes is it data nodes or name nodes.  

20. Explain the consequences of the failure of a data node?

Ans: The following are the consequences of the failure of a data node -

1. All the tasks get rescheduled as the failed nodes don't allow the data to get through the mapping and reducing processes and hence it gets rescheduled for the completion of the process.  
2. The failure of the data node is mainly determined by the other type of node, the name mode, and also the job tracker.  
3. The data is replicated to another node by the name node. This is mainly done due to the completion of the process.  

21. Define speculative execution?

Ans: Speculative execution is a type of feature that allows the launch of several tasks on different kinds of nodes. Sometimes, even some multiple copies as also made by the speculative execution. Generally, duplicate copies of the task are actually created using the feature if one task takes a long time to get completed.  

22. Mention the use of Context Object?

Ans: The interaction of the mapper with the rest of the system is done with the help of Context Object. It also consists of data configuration.  

23. What do you mean by Sequence File Input Format?  

Ans: Sequence file input format is a type of feature of MapReduce which allows the data to be transferred from the various mapping classes to the reducing class. 

24. How can the files be searched in the MapReduce job? 

Ans: The files in the MapReduce job can be searched with the help of wildcards.  

25. In MapReduce, what is the default input type?

Ans: The Input type in MapReduce is Text.  

26. How can the output file in the MapReduce job be renamed?

Ans: The output file can be renamed with the help of the implementation of the various format output classes.  

27. What is the difference between the storage nodes and compute nodes?  

Ans: The system in which the execution of the business logic is performed is known as the compute node. On the other hand, the storage node is the system in which the entire file system containing numerous kinds of data is stored which is later used for processing.  

28. What is the meaning of Stragglers?  

Ans: Stragglers are referred to the process of MapReduce during which the task takes a long time to get completed.  

29. What is the difference between identity mapper and identity reducer? 

Ans: Identity Mapper refers to the default mapper class and identity reducer refers to the default reducer class. When the number of the mapper is not defined during the work process, it is known as the identity mapper. When the reducer class is undefined, it is known as an identity reducer. Hence, this class transfers the key values to the output directory. 

30. Differentiate between MapReduce and PIG?

Ans: PIG is basically the data flow language that manages the data flow from one source to another. It also manages the data storage system and also helps in compressing them. Pig rearranges the steps for faster and better processing. The output data of the MapReduce job is basically managed by PIG. Some functions of MapReduce processing are also added in the processing of PIG. The functions include grouping, ordering, and counting data.  

MapReduce is basically the framework for writing code for the developers. This is a data processing paradigm that separated the application of two types of developers, one who writes it and another who scales it.