Evaluating the Performance of DBMSs in Insertion and Query of Time Series Data

  • Marcelo Costa de Lima UFSM
  • Daniel Lichtnow UFSM

Abstract


The aim of this work is to evaluate the performance of Database Management Systems (DBMS) in the insertion and retrieval of time series data, aiming to identify when Time Series Databases should be used instead of Relational Databases. Experiments were conducted using PostgreSQL, InfluxDB, and TimeScaleDB, varying the volume of data and assessing the execution time. The initial results and the comparison with other studies indicated the need to evaluate the requirements of each use case to determine the type of DBMS to be used for this data.

References

Apache (2023) Apache Jmeter Acessado em 09 jun 2023. Disponível em: [link]. Acesso em : 16/02/2023

Bamford, T., et al (2023) Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections. In Proceedings of the Fourth ACM International Conference on AI in Finance (pp. 498-506).

DB-Engines. DB-Engines Ranking (2023) Acessado em 09 fev 2024. Disponível em: [link]. Acesso: 16/02/2023

Dunning, T. et al. (2014) Time Series Databases: New Ways to Store and Access Data. O’ReillyMedia, Incorporated.

Freedman, M. (2017) Time-series data: Why(and how) to use a relational database instead of NoSQL. Disponível em: [link] Acesso: 16/02/2023

Grzesik, P., & Mrozek, D. (2020) Comparative analysis of time series databases in the context of edge computing for low power sensor networks. In Computational Science–ICCS 2020: 20th International Conference, Amsterdam, Springer

Hao, Y. et al. (2021) Ts-benchmark: A benchmark for time series databases. In: IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. p. 588-599.

Jensen, et al C. (2017) Time series management systems: A survey. IEEE Transactions on Knowledge and Data Engineering, 29(11), 2581-2600.

Mostafa, J., et al (2022) SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things. In Proc. of the 34th International Conference on Scientific and Statistical Database Management (pp. 1-11).

Musa, E. et al. (2019) Comparison of relational and time-series databases for real-time massive datasets. MIPRO Computers in Technical Systems, p. 1065–1070.

Rasch, E. L. (2018) Uma aplicação para carga de dados de monitoramento da geometria de linhas férreas. Trabalho de Conclusão de Curso, UFSM, Santa Maria

Shah, B.; Jat, P.; Sashidhar, K. (2022) Performance study of time series databases. arXiv preprint arXiv:2208.13982.

Wang, et al. (2023) Apache IoTDB: A Time Series Database for IoT Applications. Proceedings of the ACM on Management of Data 1.2: 1-27.
Published
2024-04-10
LIMA, Marcelo Costa de; LICHTNOW, Daniel. Evaluating the Performance of DBMSs in Insertion and Query of Time Series Data. In: REGIONAL DATABASE SCHOOL (ERBD), 19. , 2024, Farroupilha/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 170-173. ISSN 2595-413X. DOI: https://doi.org/10.5753/erbd.2024.238695.