Abstract
In this paper we present the initial results of our work to execute BigBench on Spark. First, we evaluated the scalability behavior of the existing MapReduce implementation of BigBench. Next, we executed the group of 14 pure HiveQL queries on Spark SQL and compared the results with the respective Hive ones. Our experiments show that: (1) for both Hive and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.
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Acknowledgment
This work has benefited from valuable discussions in the SPEC Research Group’s Big Data Working Group. We would like to thank Tilmann Rabl (University of Toronto), John Poelman (IBM), Bhaskar Gowda (Intel), Yi Yao (Intel), Marten Rosselli, Karsten Tolle, Roberto V. Zicari and Raik Niemann of the Frankfurt Big Data Lab for their valuable feedback. We would like to thank the Fields Institute for supporting our visit to the Sixth Workshop on Big Data Benchmarking at the University of Toronto.
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Ivanov, T., Beer, MG. (2016). Performance Evaluation of Spark SQL Using BigBench. In: Rabl, T., Nambiar, R., Baru, C., Bhandarkar, M., Poess, M., Pyne, S. (eds) Big Data Benchmarking. WBDB WBDB 2015 2015. Lecture Notes in Computer Science(), vol 10044. Springer, Cham. https://doi.org/10.1007/978-3-319-49748-8_6
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DOI: https://doi.org/10.1007/978-3-319-49748-8_6
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