Difference between MapReduce and Spark

Both MapReduce and Spark are examples of so-called frameworks because they make it possible to construct flagship products in the field of big data analytics. The Apache Software Foundation is responsible for maintaining these frameworks as open-source projects.

MapReduce, also known as Hadoop MapReduce, is a framework that enables application writing, which in turn enables the processing of vast amounts of data on clusters in a distributed form while maintaining fault tolerance and reliability. The MapReduce model is constructed by separating the term "MapReduce" into its component parts, "Map," which refers to the activity that must come first in the process, and "Reduce," which describes the action that must come last.

Spark, on the other hand, is a framework that is also used for processing a large number of data analytics applications across a cluster of computers. It is also referred to as a "Unified Analytics Engine," which is another common name for this type of software.

What is MapReduce?

Within the Hadoop framework for distributed computing, MapReduce is a programming model that is based on Java. It allows access to large amounts of data stored in the Hadoop File System (HDFS). It is a method of organizing your computation in a way that makes it simple to execute on a number of different machines.

MapReduce enables massive scalability across a Hadoop cluster's potentially hundreds of thousands of servers. Writing distributed, scalable jobs requires very little effort thanks to this feature. It filters work and distributes it to various nodes within the cluster or map, both of which are extremely important functions that it serves.

MapReduce is utilized for conducting data analysis on a massive scale, utilizing a cluster consisting of multiple computers. Typically, a MapReduce framework consists of a three-step process, which is referred to as Map, Shuffle, and Reduce.

What is Apache Spark?

Spark is a Big Data processing framework that is open source, lightning fast, and widely considered to be the successor to the MapReduce framework for handling large amounts of data. Spark is an enhancement to Hadoop's MapReduce programme that is used for processing large amounts of data.

Spark provides a quick and simple method for analysing large amounts of data across an entire cluster of computers, making it an ideal solution for businesses that need to process vast amounts of data. It is a unified analytics engine for big data and machine learning that can support multiple languages. Because of its unified programming model, it is the best option for developers who are working on data-intensive analytical applications.

Difference between MapReduce and Spark

The following table highlights the major differences between MapReduce and Spark −

Basis of comparisonMapReduceSpark
Product's CategoryWe learned in the introduction that MapReduce is primarily a data processing engine since it enables the processing of data and is therefore a data processing engine.Spark, on the other hand, is a framework that powers whole analytical solutions or applications; this characteristic makes it a logical choice for data scientists to use Spark as a data analytics engine.
Framework's Performance and Data ProcessingWhen it comes to MapReduce, the processing pace is slowed down since reading and writing operations are conducted from and to a disc. This causes the speed of the processing to be slower.Along with reducing the number of read/write cycles, Spark also minimises the amount of data that is stored in memory, which enables it to be ten times faster. However, if the data cannot be stored in memory, spark's performance may significantly degrade.
LatencyMapReduce has a greater latency in computing as a consequence of its lower performance in comparison to Spark.As a result of Spark's superior speed, developers can take advantage of its low-latency processing capabilities.
Manageability of frameworkDue to the fact that MapReduce is merely a batch engine, all of the other components need to be managed independently while simultaneously, which makes it tough to maintain.Spark is a complete data analytics engine that has the capacity to conduct batch processing, interactive streaming, and other components of a similar nature all under the same cluster umbrella, making it easier to administer.
Real-time AnalysisSince MapReduce was developed primarily for batch processing, it is not effective when applied to use cases that require real-time analytics.Spark allows for the effective management and processing of data coming from real-time live feeds such as Facebook, Twitter, and other similar platforms.
Interactive ModeMapReduce does not offer the option of having an interactive mode at your disposal.Interactivity in data processing is a feature that is available in Spark.
SecurityBecause MapReduce has access to all of the elements that are included in Hadoop security, it is possible to combine it with the various other Hadoop Security projects in a straightforward manner. ASLs can also be used with MapReduce.Spark's security has an OFF setting by default, which could result in a significant breach of security if left unchecked. When it comes to authentication, the only approach that can be used in Spark is the one with the shared secret password.
Tolerance to FailureIn the event that the MapReduce process were to become corrupted, the process would be able to restart from the point where it had been stopped earlier due to the fact that it uses hard drives rather than RAMs.Because Spark is dependent on the utilisation of RAM, it is less fault-tolerant than MapReduce due to the necessity of starting the processing from scratch in the event that the Spark process becomes corrupted.


To conclude, there are some parallels between MapReduce and Spark, such as the fact that both are utilised for the processing of a massive pool of data; nonetheless, there is no definitive answer regarding which is superior. The answer to which one is better to use relies on the problem statement that we are attempting to resolve, and we must choose the one that is most appropriate for the circumstance.