MapReduce : Simplified Data Processing on Large Cluster

International Journal of Research and Engineering

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Field Value
 
Title MapReduce : Simplified Data Processing on Large Cluster
 
Creator Dayalan, Muthu
 
Description MapReduce is a data processing approach, where a single machine acts as a master, assigning map/reduce tasks to all the other machines attached in the cluster. Technically, it could be considered as a programming model, which is applied in generating, implementation and generating large data sets. The key concept behind MapReduce is that the programmer is required to state the current problem in two basic functions, map and reduce. The scalability is handles within the system, rather than being handled by the concerned programmer. By applying various restrictions on the applied programming style, MapReduce performs several moderated functions such fault tolerance, locality optimization, load balancing as well as massive parallelization. Intermediate k/v pairs are generated by the Map, and then fed o the reduce workers by the use of the incorporated file system. The data received by the reduce workers is then merged using the same key, to produce multiple output file to the concerned user (Dean & Ghemawat, 2008). Additionally, the programmer is only required to master and write the codes regarding the easy to understand functionality.
 
Publisher IJRE Publisher
 
Date 2018-06-04
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://digital.ijre.org/index.php/int_j_res_eng/article/view/339
10.21276/ijre.2018.5.5.4
 
Source International Journal of Research and Engineering; Vol 5 No 5 (2018): May 2018 Edition; 399-403
2348-7860
2348-7852
 
Language eng
 
Relation https://digital.ijre.org/index.php/int_j_res_eng/article/view/339/306
 
Rights Copyright (c) 2018 Muthu Dayalan
http://creativecommons.org/licenses/by/4.0
 

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