MapReduce Interview Questions with sample answers

MapReduce Interview Questions

Preparing for a MapReduce interview is very difficult if you fail to formulate it in the right way. So, this article focuses on interview questions to help you feel confident while preparing. Each question has sample answers so you figure out the correct format to use. Let’s see the MapReduce Interview Questions with sample answers.

MapReduce interviews focus on behavioral and technical questions that bring out your character as a whole. Every question tries to evaluate what you know about the company to figure out if you’re the right person for that position. But, you need to execute the right kind of confidence while speaking to help you better portray yourself. 

What Is MapReduce?

MapReduce is an architecture developed from Java to help systematically distribute computer processing. Map and Reduce are two vital jobs in the MapReduce algorithm. Map transforms collective data into another set by breaking down individual components into tuples (key/value pairs).

Secondly, the reduction of jobs results in processing a map into smaller sets. The reduction work is always done after the map job, as the name MapReduce suggests.

MapReduce’s main benefit is that it’s simple to expand data processing over numerous computer nodes. Mappers and reducers are data processing primitives in MapReduce. The job of breaking down data in an application into reducers and mappers is quite difficult. 

Scaling an application to operate over hundreds, thousands, or tens of thousands of computers in a cluster is just a configuration update once we build it in MapReduce style. Many programmers have been drawn to the MapReduce paradigm because of its straightforward scalability.

MapReduce Interview Questions With Sample Answers

Explain the concept of combiners and when they should be used in a MapReduce job.

Combiners are used to improve the efficiency of the MapReduce program. The quantity of data that has to be sent across to the reducers can be decreased with the aid of combiners. The reducer code can be used during commutative as well as associative operations. However, the combiner execution isn’t guaranteed. 

What is the difference between Identity Mapper and Chain Mapper?

Hadoop’s default Mapper class is called Identity Mapper. Identify will be used if no other Mapper class is specified. It does not do any computations or calculations on the incoming data and just publishes it to the output.

Within a single map job, Chain Mapper is the implementation of a basic Mapper class using chain operations over a series of Mapper classes. The output of the first mapper serves as the input of the second mapper, and the second mapper’s output is used for the third mapper’s input; the process is repeated until the last mapper. 

State some advantages of MapReduce 

The following are some of the benefits of MapReduce programming:

  • MapReduce works with HDFS and HBase security in this regard, allowing only authorized users to access data stored in the system.
  • Processing in parallel – One of the essential features of MapReduce programming is that it splits jobs in such a way that they can be executed in parallel.
  • Multiple processors can take on these split jobs in parallel, allowing whole programs to be performed in less time.

Why is it safe to use MapReduce?

Any application’s security is essential. If an unauthorized individual or group had access to many petabytes of your company’s data, it might cause significant damage to your business transactions and operations.

Secure MapReduce must offer access along with confidentiality, fair storage, and execution, along with accounting data of audits. To solve all security problems during MapReduce calculations, you need secure storage along with computation.

Describe the scalability of Hadoop. 

Hadoop is a massively scalable platform. This is due to its capacity to store and distribute big data sets over large numbers of computers. These servers are often low-cost and may run in parallel. And as the number of servers grows, so does the amount of computing power available.

How flexible is MapReduce?

Flexibility – Business companies may utilize Hadoop MapReduce programming to get access to a variety of new data sources and to work on a variety of data kinds, both organized and unstructured. This allows them to derive values from all the data available. 

What is Speculative Execution, and how does it work?

A specific number of duplicate jobs are launched in Hadoop during Speculative Execution. Speculative Execution makes several copies of a map or even the reduced job on a drudge node. To put it another way, if a driver takes a long time to perform a job, Hadoop will repeat the operation on another disc. A disc that completes the task first is kept, whereas discs that do not complete the task first are destroyed.

What is the purpose of the MapReduce partitioner?

That position ensures all values are delivered to the same reducer through a single key. This results in a systematic distribution between the map output only through reducers. 

What is WebDAV in Hadoop and how does it work?

WebDAV is a set of HTTP extensions that allows you to edit and update files. Because WebDAV shares can be mounted as filesystems on most operating systems, HDFS may be accessed as a normal filesystem by exposing it over WebDAV.

What is partitioning and how does it work?

Partitioning is a set of algorithms used to find the mapper’s output through a reducer instance. The mapper needs to search for and identify the right reducer as a recipient before the data transfer can commence. All of the keys must be in the same reducer, regardless of which mapper produced them.

What is the process through which JobTracker schedules a task?

Jobtracker receives heartbeat signals from the task tracker every few minutes to ensure that it is active and operating. The message also tells JobTracker of the number of open slots, allowing JobTracker to keep track of where cluster work can be outsourced.

Why should you utilize MapReduce?

Traditional Enterprise Systems often store and analyze data on a centralized server. The conventional approach is unsuitable for processing large amounts of scalable data, and ordinary database servers cannot handle it. The centralized approach produces too much of a bottleneck when processing many files at the same time.

Google used the MapReduce method to tackle the problem. MapReduce breaks down a task into tiny chunks and distributes them across several machines. The findings are then gathered in one location and combined to produce the outcome dataset. It greatly simplifies data processing.

In MapReduce, what does shuffling and sorting mean?

When the mapper and reducer start functioning, the two primary operations that will be running simultaneously are shuffling and sorting.

Shuffling: The process of moving data from a Mapper to a Reducer is referred to as shuffling. Because the shuffling process will be used as an input for the reduced duties, it is one of the essential activities for the reducers to continue or continue with their work.

Sorting: Before proceeding to the reducer, MapReduce will automatically sort the output key-value pairs that occur between the map and reduce phases (after the mapper). The sorting feature comes in handy in applications that require some sorting at different stages. It also helps the coder save time in general.

What Is The Maximum Number Of Mappers And Reducers That Can Be Used?

Hadoop can execute two mappers and two reducers in one data node by default. Each node also includes two map slots and two reducer slots. These default variables in Mapreduce.xml can be changed in a conf file.

What’s the distinction between an HDFS block and an InputSplit?

The HDFS block divides data into physical divisions, but the processing is different. In MapReduce, the InputSplit function divides input files logically. It may also be used to govern a group of mappers, with split sizes set by the user. The HDFS block size, on the other hand, is set at 64 MB, thus for 1GB of data, there will be 1GB/64MB = 16 splits/blocks.

Bottom Line

This article focuses on providing information to help you ace a MapReduce interview. Interview questions with sample answers are provided to help you understand how an interview works, along with the right way to answer interview questions with confidence.


Which MapReduce join is the most efficient?

The Reduce side join, on the other hand, may link both big data sets. The Map side join is faster than the Reducer side join since it does not have to wait for all mappers to finish. As a result, the decreased side join is slower.

Where does MapReduce come into play?

MapReduce is well suited to iterative computations with huge amounts of data that require parallel processing. Rather than a method, it depicts a data flow. It’s also well-suited to large-scale graph analysis; in fact, MapReduce was created to find the PageRank of web articles in mind.

Is MapReduce still in use nowadays?

Why Is MapReduce Still The Most Popular Method For Large-Scale Machine Learning? In 2014, Google discontinued MapReduce as their primary large data processing paradigm. Meanwhile, Apache Mahout’s work had progressed to more powerful and less disk-centric methods that integrated the complete map and reduction capabilities.

MapReduce Interview Questions with sample answers

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