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Difference Between MapReduce and Apache Spark
  • Last Updated : 25 Jul, 2020

MapReduce is a framework the use of which we can write functions to process massive quantities of data, in parallel, on giant clusters of commodity hardware in a dependable manner. It is also a processing method and an application model for dispensed computing primarily based on java. The MapReduce algorithm incorporates two necessary tasks, particularly Map and Reduce. The map takes a set of records and converts it into every other set of data, where individual factors are broken down into tuples that are present in key-value pairs. Also, it helps in minimizing task, which takes the output from a map as an enter and combines those statistics tuples into a smaller set of tuples. As the sequence of the title MapReduce implies, the decrease assignment is continually carried out after the map job.

Difference-Between-MapReduce-and-Apache-Spark

Apache Spark is a data processing framework that can rapidly operate processing duties on very massive information sets, and can additionally distribute information processing duties throughout a couple of computers, either on its very own or in tandem with different allotted computing tools. These two features are key to the worlds of massive information and machine learning, which require the marshaling of large computing energy to crunch via massive information stores. Spark additionally takes some of the programming burdens of these duties off the shoulders of developers with an easy use API that abstracts away a whole lot of the grunt work of distributed computing and large information processing.

Difference Between MapReduce and Spark

S.No.

MapReduce

Spark

1. It is a framework that is open-source which is used for writing data into the Hadoop Distributed File System. It is an open-source framework used for faster data processing.
2. It is having a very slow speed as compared to Apache Spark. It is much faster than MapReduce.
3. It is unable to handle real-time processing. It can deal with real-time processing.
4. It is difficult to program as you required code for every process. It is easy to program.
5. It supports more security projects. Its security is not as good as MapReduce and continuously working on its security issues.
6. For performing the task, It is unable to cache in memory. It can cache the memory data for processing its task.
7. Its scalability is good as you can add up to n different nodes. It is having low scalability as compared to MapReduce. 
8. It actually needs other queries to perform the task. It has Spark SQL as its very own query language.
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