What is MapReduce?

MapReduce is a parallel, distributed programming model in the Hadoop framework that can be used to access the extensive data stored in the Hadoop Distributed File System (HDFS). The Hadoop is capable of running the MapReduce program written in various languages such as Java, Ruby, and Python. One of the beneficial factors that MapReduce aids is that MapReduce programs are inherently parallel, making the very large scale easier for data analysis.

When the MapReduce programs run in parallel, it speeds up the process. The process of running MapReduce programs is explained below.

• Dividing the input into fixed-size chunks: Initially, it divides the work into equal-sized pieces. When the file size varies, dividing the work into equal-sized pieces isn’t the straightforward method to follow, because some processes will finish much earlier than others while some may take a very long run to complete their work. So one of the better approaches is that one that requires more work is said to split the input into fixed-size chunks and assign each chunk to a process.
• Combining the results: Combining results from independent processes is a crucial task in MapReduce programming because it may often need additional processing such as aggregating and finalizing the results.

Key components of MapReduce

There are two key components in the MapReduce. The MapReduce consists of two primary phases such as the map phase and the reduces phase. Each phase contains the key-value pairs as its input and output and it also has the map function and reducer function within it.

• Mapper: Mapper is the first phase of the MapReduce. The Mapper is responsible for processing each input record and the key-value pairs are generated by the InputSplit and RecordReader. Where these key-value pairs can be completely different from the input pair. The MapReduce output holds the collection of all these key-value pairs.
• Reducer: The reducer phase is the second phase of the MapReduce. It is responsible for processing the output of the mapper. Once it completes processing the output of the mapper, the reducer now generates a new set of output that can be stored in HDFS as the final output data.

In the Hadoop framework, MapReduce is the programming model. MapReduce utilizes the map and reduce strategy for the analysis of data. In today’s fast-paced world, there is a huge number of data available, and processing this extensive data is one of the critical tasks to do so. However, the MapReduce programming model can be the solution for processing extensive data while maintaining both speed and efficiency. Understanding this programming model, its components, and execution workflow in the Hadoop framework will be helpful to gain valuable insights.