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Table of contents
What is MapReduce?
Map Function
Reduce Function
Optimizing MapReduce Tasks
When to use MapReduce with Big Data?
What is one of the significant advantage of using MapReduce?
Last Updated: Mar 27, 2024

Map Reduce Function And Its Optimization

Author Akash Nagpal
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While big data has grabbed the news in recent months, huge computing problems have existed since the dawn of computers. People discovered difficulties that were too enormous for the system to manage each time a newer, faster, higher-capacity computer system was released. A solution to big data problems relies on the distribution of computing and data-intensive applications. New techniques are required to ensure the most dependable distribution at a huge scale. 

One of these innovative ways is MapReduce. MapReduce is a programming framework that allows programmers to create software that processes large amounts of unstructured data in parallel across a distributed group of processors.

What is MapReduce?

MapReduce was designed to be a general-purpose programming model. Parallel execution, fault tolerance, load balancing, and data manipulation were significant needs in some early systems. The developers gave the project the name MapReduce because it combines two features from existing functional computer languages: map and reduces.

Map Function

For years, the map function has been a component of many functional programming languages, with an artificial intelligence language, LISP, the first to popularize it. The map has been revitalized as a primary technology for processing lists of data pieces by good software developers who recognize the importance of reuse (keys and values).


  • Operators do not change the data structure; instead, they construct new data structures as their output. More significantly, the original data has not been tampered with. As a solution, we may use the map function with confidence, knowing that it will not destroy your valuable data.
  • Another benefit of functional programming is that it eliminates the need to regulate data movement and flow explicitly. This is advantageous since it relieves the programmer of the responsibility of explicitly regulating data output and placement.
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Reduce Function

Reduce has been a part of functional programming languages for a long time, just as the map function. The reduce function takes the result of a map function and "reduces" it in any way the programmer wants. The reduction function needs the first step of assigning a value to an accumulator, which stores an initial value. 

The reduce function examines each member of the list and performs the action you require throughout the list after saving a beginning value in the accumulator. The reduction function returns a value at the end of the list based on the operation you wish to execute on the output list. Now go back to the map function example to see what the reduce function can achieve.

Optimizing MapReduce Tasks

Several optimization strategies can be applied to improve the application code to increase the reliability and speed of MapReduce tasks. Hardware/network topology, synchronization, and file system are the three categories in which they are classified.

  • Hardware/network topology: The best hardware and networks, regardless of application, will almost certainly result in the fastest run speeds for the program. MapReduce has the advantage of being able to execute on low-cost commodity hardware clusters using conventional networks. In the data centre, commodity hardware is frequently placed in racks. When compared to transporting data and code from rack to rack, the closeness of the hardware within the rack provides a performance benefit. You may set up your MapReduce engine to be aware of and make use of this closeness during implementation.
  • Synchronization: Because it's wasteful to keep all of your mapping results in one node, the synchronisation mechanisms copy the mapping results to the reducing nodes as soon as they're finished, allowing processing to start right away. The same reducer receives all values from the same key, guaranteeing superior performance and efficiency. Because the reduction outputs are sent directly to the file system, they must be carefully planned and calibrated.
  • File System: A distributed file system is used to support the MapReduce implementation. The capacity difference is the most significant distinction between local and distributed file systems. To accommodate the massive volumes of data generated by big data, file systems must be distributed among several workstations or network nodes. MapReduce implementations use a master-slave distribution model, with the master node storing all metadata, access privileges, file and block mapping and location, and so on. The slaves are the nodes that store the real data. All requests are sent to the master, who subsequently forwards them to the relevant slave node.


When to use MapReduce with Big Data?

MapReduce is a key component of the Apache Hadoop open-source ecosystem, and it's widely used in the Hadoop Distributed File System for searching and choosing data (HDFS).

What is one of the significant advantage of using MapReduce?

MapReduce's main benefit is that it's simple to expand data processing over several computer nodes. The data processing primitives of the MapReduce paradigm are known as mappers and reducers. It's not always easy to break down a data processing application into mappers and reducers. Scaling an application to operate over hundreds, thousands, or even tens of thousands of servers in a cluster is only a configuration modification once we build it in MapReduce style.


In this article, we have extensively discussed File Systems in Map Reduce and its optimization.

We hope this blog has helped you enhance your knowledge regarding the Map-Reduce function. Some official documentation on big data that can help you improve your understanding is Big Data and Database Vs Data Warehouse.

If you would like to learn more, check out our articles on Columnar Databasecloud platform comparison, and 10 AWS best books

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