Understanding the MapReduce Application

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MapReduce Applications

Since time immemorial data has always played a key role in business decision making. Organizations have for the longest time made use of varying business analytics to gain insights on the past performance of their businesses to be able to make provision for future performance. Technologies have advanced over time, which has fortunately made this process more efficient and easier. MapReduce is one the new technologies that more and more businesses have embraced to enhance data management.

What are MapReduce applications?

MapReduce is generally a software framework which makes it possible for developers to write programs capable of processing massive amounts of parallel unstructured data across a distributed processor clusters or even standalone computers. This application was conceived and developed at Google specifically for use in indexing web pages after which they replaced their initial indexing algorithms as well as heuristics in 2004.This software framework is typically divided into two main parts as outlined below:

•Map is a function which usually parcels work to the different nodes within each distributed cluster.
•Reduce is another function responsible for collating the work and resolving the results into one value.

MapReduce applications are known for their numerous advantages. Perhaps the biggest advantage originating from the fact that it is fault tolerant since every node within the cluster is usually expected to report back regularly with the finished work besides status updates. If, for instance, a node remains silent for a period considered longer than the expected interval, the master node generates a note and then re-assigns the work to other available nodes.

How MapReduce works

To understand how MapReduce applications work, a simple way to consider input conceptually as a list of records which are then split among separate computers by Map in the cluster. The outcome of Map computation is actually a list of key pairs. The next step entails the Reduce taking each set of keys with the same key pairs and combining them into a single key. Contrary to what many people think, this software is not designed for replacing relational databases. It is, however, intended for providing a lightweight means of programming things in a manner that they can run faster by making them run parallel on many different machines.

MapReduce is essential as it makes possible for ordinary developers to make use of the routines of MapReduce library to generate parallel programs without worrying about programming for task monitoring, intracluster communication or even failure handling. This makes it one of the most advantageous business analytics for activities such as log file analysis, data mining, financial analysis and other scientific stimulations.