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oai:riuma.uma.es:10630/249572026-02-03T11:35:22Zcom_10630_2254col_10630_37953

On the performance of SQL scalable systems on Kubernetes: a comparative study Cardas Ezeiza, Cristian Aldana Martín, José Francisco Burgueño Romero, Antonio Manuel Nebro-Urbaneja, Antonio Jesús Mateos, Jose M. Sánchez-Martínez, Juan José SQL (Lenguaje de programación) The popularization of Hadoop as the the-facto standard platform for data analytics in the context of Big Data applications has led to the upsurge of SQL-on-Hadoop systems, which provide scalable query execution engines allowing the use of SQL queries on data stored in HDFS. In this context, Kubernetes appears as the leading choice to simplify the deployment and scaling of containerized applications; however, there is a lack of studies about the performance of SQL-on-Hadoop systems deployed on Kubernetes, and this is the gap we intend to fill in this paper. We present an experimental study involving four representative SQL scalable platforms: Apache Drill, Apache Hive, Apache Spark SQL and Trino. Concretely, we analyze the performance of these systems when they are deployed on a Hadoop cluster with Kubernetes by using the TPC-H benchmark. The results of our study can help practitioners and users about what they can expect in terms of performance if they plan to use the advantages of Kubernetes to deploy applications using the analyzed SQL scalable platforms. 2022-09-12T12:13:13Z 2022-09-12T12:13:13Z 2022-09-09 journal article Cardas, C., Aldana-Martín, J.F., Burgueño-Romero, A.M. et al. On the performance of SQL scalable systems on Kubernetes: a comparative study. Cluster Comput (2022). https://doi.org/10.1007/s10586-022-03718-9 https://hdl.handle.net/10630/24957 10.1007/s10586-022-03718-9 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional