An Enormous Inspection of MapReduce Technology

Authors(1) :-J. Rajesh Khanna

Since, the last three or four years, the field of “big data” has appeared as the new frontier in the wide spectrum of IT-enabled innovations and favorable time allowed by the information revolution. Today, there is a raise necessity to analyses very huge datasets, that have been coined big data, and in need of uniqueness storage and processing infrastructures. MapReduce is a programming model the goal of processing big data in a parallel and distributed manner. In MapReduce, the client describes a map function that processes a key/value pair to procreate a set of intermediate value pairs & key, and a reduce function that merges all intermediate values be associated with the same intermediate key. In this paper, we aimed to demonstrate a close-up view about MapReduce. The MapReduce is a famous framework for data-intensive distributed computing of batch jobs. This is over-simplify fault tolerance, many implementations of MapReduce materialize the overall output of every map and reduce task before it can be consumed. Finally, we also discuss the comparison between RDBMS and MapReduce, and famous scheduling algorithms in this field.

Authors and Affiliations

J. Rajesh Khanna
Assistant Professor, Department of CSE, BVRIT, Telangana, India

Big Data, MapReduce, Scheduling, Processing Layer, Indexing, Data Layout.

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 491-499
Manuscript Number : CSEIT1726154
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

J. Rajesh Khanna, "An Enormous Inspection of MapReduce Technology", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.491-499, November-December-2017.
Journal URL : http://ijsrcseit.com/CSEIT1726154

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