MapReduce Algorithm

In this tutorial, we will focus on the MapReduce Algorithm and its working, Word Count Problem, the Implementation of the wordcount problem in PySpark, MapReduce components, applications, and limitations.

Map-Reduce is a programming model or framework for processing large distributed data. It processes data that resides on hundreds of machines.
It is a simple, elegant, and easy-to-understand programming model. It is based on the parallel computation of the job in a distributed environment. It processes a large amount of data in a reasonable time. Here, distribution means parallel computing of the same task on each CPU with a different dataset. MapReduce program can be written in JAVA, Python, and C++.

MapReduce Algorithm

MapReduce has two basic operations: The first operation is applied to each of the input records, and the second operation aggregates the output results. Map-Reduce must define two functions:

• Map function: It reads, splits, transforms, and filters input data.
• Reduce function: It shuffles, sorts, aggregates, and reduces the results.

How Does MapReduce Algorithm Work?

MapReduce has two steps: map and reduce. map phase load, parse, transform, and filter the data. The map tasks…