원문정보
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
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