Complete project code for Three Tier MapReduce



There are various benchmarks that are used for texting MapReduce Libraries. Some of the common names are Matrix Multiplication (MM, multiplies two large square matrices); Sparse Integer Occurrence (SIO, counts the number of times each integer appears in a large dataset); Word Occurrence (WO, counts the number of times each word occurs in a text corpus); Linear Regression (LR, computes a linear model of a set of data), and KMeans Clustering (KMC, partitions a set of data points into clusters) (Stuart & Owens, Multi-GPU MapReduce on GPU Clusters). All these benchmarks touch different aspects of the library.

We decided to go with word occurrence because of its following characteristics:

• Non-Uniform Records: MR deals with data record by record. A record could be a line, a paragraph or a row. A text document can have such records of fixed as well as variable lengths. Also, some keys might exist in a part and not in the other. Working with such example would make the system capable to deal with all kind of records.

• Many Key/Value Pair: Text documents can have an enormous number of different keys, and their repetition would give us dynamic size values.

Scalable: As we are dealing with a cluster of nodes, scalability is one of the most important aspects that we need to keep a keen eye on. The output set for WO is much smaller, leading to a different configuration of the pipeline and drastically different scaling.

Complete MapReduce project in power generation

MATLAB code to Imports the output file from the MapReduce application



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