Performance Evaluation of Apache Spark Vs MPI: A Practical Case Study on Twitter Sentiment Analysis
Deepa S Kumar and M Abdul Rahman
DOI : 10.3844/jcssp.2017.781.794
Journal of Computer Science
Volume 13, Issue 12
The advent of various processing frameworks which happens under big data technologies is due to tremendous dataset size and its complexity. The speed of execution was much higher with High Performance computing frameworks rather than big data processing frameworks. As majority of the jobs under big data are mostly data intensive rather than computation intensive, the High Performance Computing paradigms were not been used in big data processing. This paper reviews two distributed and parallel computing frameworks: Apache Spark and MPI. Sentiment analysis on twitter data is chosen as a test case application for benchmarking and implemented on Scala programming for spark processing and in C++ for MPI. Experiments were conducted on Google cloud virtual machines for three data set sizes, 100 GB, 500 GB and 1 TB to compare the execution times. Results shown that MPI outperforms Apache Spark in parallel and distributed cluster computing environments and hence the higher performance of MPI can be exploited in big data applications for improving speedups.
© 2017 Deepa S Kumar and M Abdul Rahman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.