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MapReduce Based Analysis of Sample Applications Using Hadoop

  • Mohd Rehan GhaziEmail author
  • N. S. RaghavaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 899)

Abstract

The rate of increase of structured, semi-structured and unstructured data is very high. To discover hidden information from different types of data is a big challenge. The two techniques, word frequency count and string matching, are applied on a single node and multi node cluster with an input data set. The results are analyzed and compared by varying MapReduce configuration of both. In this paper we have tested that for a MapReduce job how changing the number of mappers and reducers can significantly affect performance. Further, it is analyzed how Hadoop invokes number of mappers/reducers depending upon the input size and Hadoop Distributed File System (HDFS) block size. The outcome of research analysis for heterogeneous cluster configurations indicates the prospective of the framework, as well as of mappers and reducers that affect its performance.

Keywords

Big data Cloud computing Hadoop HDFS MapReduce 

Notes

Acknowledgement

This work was made possible by the financial support of Department of Science & Technology (DST), Ministry of Science and Technology, Government of India, in terms of Research Fellowship.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringDelhi Technological UniversityDelhiIndia

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