On the performance of SQL scalable systems on Kubernetes: a comparative study
| Autor: | Cristian Cardas, José F. Aldana-Martín, Antonio M. Burgueño-Romero, Antonio J. Nebro, Jose M. Mateos, Juan J. Sánchez |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | RIUMA. Repositorio Institucional de la Universidad de Málaga instname |
| ISSN: | 2020-1125 |
| DOI: | 10.1007/s10586-022-03718-9 |
| Popis: | 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. 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”. |
| Databáze: | OpenAIRE |
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