Earticle

초록

영어

Working with data involves two major factors, storing the data and performing computations by accessing the data. MySQL is the first Database Management Software that provided an effective and efficient method for data storage and computations. However, with the huge amount of data that is getting generated every day from various fields, need for the advanced methods for managing and analyzing the big data is very much obvious. One of such platforms, which were developed exclusively for Big Data Analytics, is Apache Spark. Though MySQL is preferred for small amount of Data and Spark is meant for big data, many of the functionalities are found similar in both and they can be considered for a comparative study. In this work we have executed a set of queries with common functionalities for a dataset on both the frameworks. The obtained results are analyzed by visualizing aids to arrive at appropriate conclusion.

목차

Abstract
 1. Introduction
 2. MySQL cluster Programming model
 3. Apache Spark Programming Model
  3.1 Resilient Distributed Datasets (RDDs)
 4. Common Functionalities
 5. Implementation
 6. Results and Analysis
  6.1 Response Time
  6.2 CPU Utilization
  6.3 Memory Utilization
  6.4 Transfer Rate
 7. Conclusion and Future Work
 Acknowledgments
 References

키워드

저자

• Indira Bidari [ Department of Information Science and Engineering, B V Bhoomaraddi College of Engineering and Technology, Hubballi, Karnataka, India ]
• Sindhooja K [ Applied Materials Pvt. Ltd, Benguluru, India ]
• Satyadhyan Chickerur [ Centre for High Performance Computing, K L E Technological University, Hubballi, Karnataka, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605