RT Journal Article
T1 On the performance of SQL scalable systems on Kubernetes: a comparative study
A1 Cardas Ezeiza, Cristian
A1 Aldana Martín, José Francisco
A1 Burgueño Romero, Antonio Manuel
A1 Nebro-Urbaneja, Antonio Jesús
A1 Mateos, Jose M.
A1 Sánchez-Martínez, Juan José
K1 SQL (Lenguaje de programación)
AB The popularization of Hadoop as the the-facto standard platform for data analytics in the context of Big Data applicationshas led to the upsurge of SQL-on-Hadoop systems, which provide scalable query execution engines allowing the use ofSQL queries on data stored in HDFS. In this context, Kubernetes appears as the leading choice to simplify the deploymentand scaling of containerized applications; however, there is a lack of studies about the performance of SQL-on-Hadoopsystems deployed on Kubernetes, and this is the gap we intend to fill in this paper. We present an experimental studyinvolving 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 byusing the TPC-H benchmark. The results of our study can help practitioners and users about what they can expect in termsof performance if they plan to use the advantages of Kubernetes to deploy applications using the analyzed SQL scalableplatforms.
YR 2022
FD 2022-09-09
LK https://hdl.handle.net/10630/24957
UL https://hdl.handle.net/10630/24957
LA eng
NO 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
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funding for open access charge: Universidad de Málaga / CBUA. This work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020-112540RB-C41 (AEI/FEDER, UE), Andalusian PAIDI program with grant P18-RT-2799, and by project ”Evolución y desarrollo de la plataforma DOP de Big Data” (702C2000044) under Andalusian “Programa de Apoyo a la I+D+i Empresarial”.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026