Difference Between Hadoop and MapReduce
In today’s data-driven world, businesses and organizations handle massive amounts of information every second. Managing and analyzing such large datasets—known as Big Data—requires powerful tools. That’s where Hadoop comes in. Hadoop is an open-source framework that helps store and process huge volumes of data across many computers, all working together. It breaks down big tasks into smaller ones and distributes them efficiently, making data handling faster and more reliable.
A key part of Hadoop’s power comes from MapReduce, a programming model designed to process large datasets in parallel. Inspired by basic concepts like “mapping” and “reducing” data, MapReduce allows developers to write programs that split big jobs into smaller tasks, process them on different computers, and combine the results. This article explores how Hadoop and MapReduce work, how they differ, and how they fit into the big data ecosystem.
Hadoop
Hadoop software is a framework that permits the distributed processing of huge data sets across clusters of computers using simple programming models. In simple terms, Hadoop is a framework for processing ‘Big Data. Hadoop was created by Doug Cutting. It was also created by Mike Cafarella. It is designed to scale from single servers to thousands of machines, each having local computation and storage. Hadoop is an open-source software. The core of Apache Hadoop consists of a storage part, known as the Hadoop Distributed File System (HDFS), and a processing part, which may be a MapReduce the programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the info in parallel.
Mapreduce
MapReduce is a programming model that is used for processing and generating large data sets on clusters of computers. It was introduced by Google. MapReduce is a concept or a method for large-scale parallelization. It is inspired by functional programming map() and reduce() functions. MapReduce program is executed in three stages they are:
- Mapping: The Mapper's job is to process input data. Each node applies the map function to the local data.
- Shuffle: Here nodes are redistributed where data is based on the output keys.(output keys are produced by map function).
- Reduce: Nodes are now processed into each group of output data, per key, in parallel.
Architecture Overview
Hadoop’s architecture is made up of four main parts that work together to process large amounts of data across many computers:
- HDFS - This is where data is stored. It breaks big files into small parts and spreads them across multiple machines to store safely and access quickly.
- MapReduce - This is the brain that processes the data stored in HDFS. It breaks tasks into smaller parts, does the work on different computers, and brings the results back together. It's ideal for batch processing huge datasets.
- YARN - YARN is the manager. It handles system resources and schedules jobs so each task gets the right amount of CPU, memory, etc.
- Hadoop Common - This contains the shared libraries and utilities needed by other parts of Hadoop to function properly.
Where MapReduce fits
MapReduce is one of the processing engines in Hadoop. It sits on top of HDFS (to read/write data) and works with YARN (to get resources and run tasks). While other engines like Spark can also be used, MapReduce is Hadoop’s original processing model.
Below is a table of differences between Hadoop and MapReduce:
| Based on | Hadoop | MapReduce |
|---|---|---|
| Definition | The Apache Hadoop is a software that allows all the distributed processing of large data sets across clusters of computers using simple programming | MapReduce is a programming model which is an implementation for processing and generating big data sets with distributed algorithm on a cluster. |
| Meaning | The name “Hadoop” was the named after Doug cutting's son's toy elephant. He named this project as “Hadoop” as it was easy to pronounce it. | The “MapReduce” name came into existence as per the functionality itself of mapping and reducing in key-value pairs. |
| Framework | Hadoop not only has storage framework which stores the data but creating name node’s and data node’s it also has other frameworks which include MapReduce itself. | MapReduce is a programming framework which uses a key, value mappings to sort/process the data |
| Invention | Hadoop was created by Doug Cutting and Mike Cafarella. | Mapreduce is invented by Google. |
| Features | ||
| Concept | The Apache Hadoop is an eco-system which provides an environment which is reliable, scalable and ready for distributed computing. | MapReduce is a submodule of this project which is a programming model and is used to process huge datasets which sits on HDFS (Hadoop distributed file system). |
| Language | Hadoop is a collection of all modules and hence may include other programming/scripting languages too | MapReduce is basically written in Java programming language |
| Pre-requisites | Hadoop runs on HDFS (Hadoop Distributed File System) | MapReduce can run on HDFS/GFS/NDFS or any other distributed system for example MapR-FS |
Conclusion
The duo of Hadoop and MapReduce makes it easy to store and process massive datasets. While Hadoop offers the whole framework, providing storage via the Hadoop Distributed File System (HDFS) and resource management through the Yet Another Resource Negotiator (YARN), MapReduce acts as the engine that churns the data in a distributed manner and in the most efficient way possible. The duo has been the building blocks of many big data solutions. However, faster engines like Apache Spark now more or less demote MapReduce as an interim phase of the evolution of big data tooling. But it pays to understand Hadoop and MapReduce before looking into the development of modern data processing systems.
