A Comparison of Big Data Application Programming Approaches: A Travel Companion Case Study
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Type of Work10 pages
conference papers and proceedings
Citation of Original PublicationP. Guo, J. Wang and Z. Chen, "A comparison of big data application programming approaches: A travel companion case study," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 2869-2878. DOI: 10.1109/BigData.2017.8258255
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© 2017 IEEE
big data application programming
With advances of big data technologies, there are many possible ways to program for each big data application. A challenge is to know the differences of the program approaches and decide which programming approach is the best for a particular big data application. In this paper, we use vehicle travel companion as a case study to explore four different programming approaches, including Spark RDD (with GroupBy or Join), Spark SQL with Hive and Hive on Hadoop, and tune the programmed big data applications. Our experiments show that the execution time of one programming approach could be more than 100-fold longer than that of another for the same application logic, which verifies that programming approach decision is important. We also explain the reasons for the differences. The findings could be applied to the selection of programming approach for other big data applications.