Parallel Processing of cluster by Map Reduce

Abstract

MapReduce is a parallel programming model and an associated implementation introduced by This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given.The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.

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“…The third phase of the flowchart involves splitting each digest, abstracted resources and events into multiple units (Chattopadhyay et al, 2011;Vaidya, 2012). Splitting here means partitioning each document into small clusters which are a logical grouping by decomposing it into metadata units (Subramaniyaswamy et al, 2015;Kruijf and Sankaralingam, 2007;Singh and Singh, 2015).…”