Avaliando a Performance de SGBDs na Inserção e Consulta de Dados de Séries Temporais
Resumo
O objetivo deste trabalho é avaliar a performance de SGBDs na inserção e consulta de dados de séries temporais procurando identificar quando Bancos de Dados de Séries Temporais devem ser usados no lugar de Bancos de Dados Relacionais. Foram feitos experimentos com PostgreSQL, InfluxDB e TimeScaleDB variando o volume de dados e avaliando o tempo de execução. Os resultados iniciais e a comparação com outros trabalhos indicaram a necessidade de avaliar os requisitos de cada cenário de uso para definir o tipo de SGBD a ser utilizado para estes dados.
Referências
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.
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.
Publicado
10/04/2024
Como Citar
LIMA, Marcelo Costa de; LICHTNOW, Daniel.
Avaliando a Performance de SGBDs na Inserção e Consulta de Dados de Séries Temporais. In: ESCOLA REGIONAL DE BANCO DE DADOS (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.
