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Mapreduce in Big Data: Overview, Functionality & Importance

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4th Jun, 2023
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Mapreduce in Big Data: Overview, Functionality & Importance

What is Big Data?

Big Data is the comprehensive collection of vast amounts of data that cannot be processed with the help of traditional computing methods. Big data analysis refers to utilising methods like user behaviour analytics, predictive analytics, or various other advanced analytics that effectively deal with big data. Big data analysis is used to extract information from large datasets systematically. 

With the advancement of technology, our digitally driven lives are primarily dependent on large data sets in various fields. Data is everywhere, from digital devices like mobile phones to computer systems and is a vital resource for large organisations and businesses. They rely on large sets of unprocessed data, which fall under the big data umbrella.

Therefore, the collection, study, analysis, and information extraction are integral for the growth of businesses and other purposes in various sectors. Data scientists’ job is to process this data and present it to the company for forecasting and business planning.

What is MapReduce?

MapReduce is a programming model that plays an integral part in processing big data and large datasets with the help of a parallel, distributed algorithm on a cluster. MapReduce programs can be written in many programming languages like C++, Java, Ruby, Python, etc. The biggest advantage of MapReduce is that it makes data processing easy to scale over numerous computer nodes. 

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MapReduce and HDFS are primarily used for the effective management of big data. Hadoop is referred to as the basic fundamentals of this coupled Mapreduce and HDFS system known as the HDFS-MapReduce system. Therefore, it is needless to say that MapReduce is an integral component of the Apache Hadoop ecosystem. The framework of Mapreduce contributes to the  enhancement of data processing on a massive level. Apache Hadoop consists of other elements that include Hadoop Distributed File System (HDFS), Apache Pig and Yarn.

MapReduce helps enhance data processing with the help of dispersed and parallel algorithms of the Hadoop ecosystem. The application of this programming model in e-commerce and social platforms helps analyse the huge data collected from online users.

MapReduce’s Significance in Big data

The ability of MapReduce to manage the enormous volumes of data created by contemporary applications is crucial in the context of big data. Processing and analysing such big data sets would be time- and resource-intensive without MapReduce. Big data may be processed effectively with MapReduce, allowing for the extraction of insightful knowledge and useful intelligence from these enormous data volumes.

In addition, the MapReduce big data is intended to be scalable and fault-tolerant. Hardware failures are frequent in a distributed computing system. The MapReduce architecture is built to handle these errors and carry on uninterrupted data processing

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Big Data MapReduce Functionalities

The Map Reduce algorithm in big data breaks down large data sets into smaller, easier-to-manage chunks, which are then processed concurrently. The algorithm’s two essential parts are the Reduce and Map functions. A collection of key-value pairs results from the Map function, which manipulates the input data in some ways. These key-value pairs are then provided to the Reduce function, which uses them as input and executes different operations to create the desired output. The key-value team set that the Reduce function produces condenses the data into a smaller set.

These Map and Reduce functions may be distributedly processed across a cluster of computational nodes using the MapReduce framework in big data. MapReduce can handle enormous data sets that are too big to be run on a single machine because of the dispersal of computing.

How does MapReduce work?

The MapReduce algorithm consists of two integral tasks, namely Map and Reduce. The Map task takes a dataset and proceeds to convert it into another dataset, where individual elements are broken into tuples or key-value pairs. The Reduce task takes the output from the Map as an input and combines those data tuples or key-value pairs into smaller tuple sets. The Reduce task is always performed after the map job. 

Below are the various phases of MapReduce:-

  • Input Phase: In the input phase, a Record Reader helps translate each record in the input file and send the parsed data in the form of key-value pairs to the mapper.
  • Map: The map function is user-defined. It helps process a series of key-value pairs and generate zero or multiple key-value pairs.
  • Intermediate Keys: The key-value pairs generated by the mapper are known as intermediate keys.
  • Combiner: This kind of local Reducer helps group similar data generated from the map phase into identifiable sets. It is an optional part of the MapReduce algorithm.
  • Shuffle and Sort: The Reducer task starts with this step where it downloads the grouped key-value pairs into the machine, where the Reducer is already running. The key-value pairs are segregated by key into a more extensive data list. The data list then groups the equivalent keys together to iterate their values with ease in the Reducer task.
  • Reducer: The Reducer takes the key-value paired data grouped as input and then runs a Reducer function on every one of them. Here, data can be filtered, aggregated, and combined in many ways. It also needs a wide range of processing. Once the process is over, it gives zero or multiple key-value pairs to the final step.
  • Output Phase:  In this phase, there is an output formatter that translates the final key-value pairs from the Reducer function and writes them onto a file using a record writer.

MapReduce occurs in three stages:-

Stage 1 :  The map stage

Stage 2 : The shuffle stage

Stage 3 :  The reduce stage.

Examples to help understand the stages better. Here is an example of a Wordcount problem solved by Mapreduce through the stages:-

Take the below input data into consideration:-

  • Anna Karen Lola
  • Clara Clara Lola
  • Anna Clara Karen
  1. The above data has been segregated into three input splits.
  • Anna Karen Lola
  • Clara Clara Lola
  • Anna Clara Karen
  1. In the next stage, this data is fed into the next phase, that is referred to as the mapping phase.

Considering the first line (Anna Karen Lola), we get three key-value pairs – Anna, 1; Karen, 1; Lola, 1.

You will find the result in the mapping phase below:-

  • Anna,1
  • Clara,1
  • Anna,1
  1. The data mentioned above is then fed into the next phase. This phase is called the sorting and shuffling phase. The data in this phase is grouped into unique keys and is further sorted. You will find the result of the sorting and shuffling phase:
  • Lola,(1,1)
  • Karen(1,1)
  • Anna(1,1)
  • Clara(1,1,1)
  1. The data above is then fed into the next phase, that is referred to as the reduce phase.

All the key values are aggregated here, and the number of 1s is counted.

Below is the result in reduce phase:

  • Lola,2
  • Karen,2
  • Anna,2
  • Clara,3

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Tools & Techniques Used in Conjunction with MapReduce

MapReduce is often used in conjunction with techniques like CRUD procedures, indexes, aggregation pipelines, splitting, and replication. These techniques help effectively manage and process big data.

CRUD Procedures

Create, Read, Update, and Delete (CRUD) operations related to the fundamental activities that can be carried out on a database. Any database management system’s ability to operate with data requires these procedures.

Aggregation Pipeline

A MongoDB data processing architecture called the Aggregation Pipeline enables collection-level data manipulation and transformation. It helps combine data from several sources and run complex analytics on the data.


Data structures called indexes are employed in databases to enhance data retrieval. They expedite queries by allowing users to locate data quickly without browsing through every table row. The performance of a database can be greatly enhanced by adding indexes to the tables.

Splitting and Replication

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Databases may be scaled horizontally using techniques like replication and sharding. To guarantee high availability and fault tolerance, replication entails making several copies of the database and distributing them across many servers. A database is sharded into smaller, more manageable chunks to increase performance and scalability and spread across various servers.

Why Choose MapReduce?

As a programming model for writing applications, MapReduce is one of the best tools for processing big data parallelly on multiple nodes. Other advantages of using MapReduce are as follows:-

  • Security
  • Scalability 
  • Flexibility
  • Budget-friendly
  • Authentication
  • Simplified programming model
  • Fast and effective
  • Availability
  • Parallel processing
  • Resilience


Big Data is a very important part of our lives since giant corporations on which the economy is thriving relies on said Big Data. Today, it is one of the most profitable career choices one can opt for. 

If you are looking to enroll in a reliable course on the Advanced Certificate Programme in Big Data, then look no further. upGrad has the best course you will come across. You will learn top professional skills like Data Processing with PySpark, Data Warehousing, MapReduce, Big Data Processing on the Cloud, Real-time Processing, and the like.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.

Frequently Asked Questions (FAQs)

1What is a partitioner, and how is it used?

A partitioner is a phase that controls the partition of immediate Mapreduce output keys using hash functions. The partitioning determines the reducer, key-value pairs are sent to.

2What are the main configurations specified in MapReduce?

MapReduce requires the input and output location of the job in Hadoop distributed file systems and their formats. MapReduce programmers also need to provide the parameters of the classes containing the map and reduce functions. MapReduce also requires the .JAR file to be configured for reducer, driver and mapper classes.

3What is chain mapper and identity mapper in MapReduce?

A chain mapper can be defined as simple mapper classes that are being implemented with the help of chain operations across specific mapper classes within a single map task. The identity mapper can be defined as Hadoop's mapper class by default. The identity mapper is executed when other mapper classes are not defined.