Uma Abordagem Experimental para Avaliar os Níveis de Consistência do Banco de Dados NoSQL Cassandra
Resumo
A computação distribuída permite a comunicação entre vários computadores, possibilitando, por exemplo, a distribuição de dados entre eles. No entanto, essa tecnologia traz alguns problemas, como, por exemplo, a consistência dos dados. A consistência dos dados replicados entre os diferentes computadores visa garantir que o mesmo dado seja acessado em todas os computadores em execução. Entretanto, garantir a consistência pode afetar o desempenho, visto que tem suas vantagens e desvantagens. Assim, este trabalho avalia os impactos dos níveis de consistência no desempenho do banco de dados NoSQL (Não Somente SQL) Cassandra, onde diferentes cenários e cargas de trabalho são considerados para analisar os trade-offs que surgem a partir da utilização desses níveis. Nós adotamos uma abordagem experimental para avaliar e analisar o tempo de resposta do sistema quando esses diferentes níveis de consistência e carga de trabalho são utilizados. Os resultados obtidos mostram que a carga de usuários concorrentes acentua a disparidade entre os tempos de resposta que cada um dos níveis apresenta, bem como a quantidade de dados envolvidos nas requisições.
Referências
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Abramova, V. and Bernardino, J. (2013). Nosql databases: Mongodb vs cassandra. In Proceedings of the international C* conference on computer science and software engineering, pages 14–22.
Bermbach, D. and Tai, S. (2011). Eventual consistency: How soon is eventual? an evaluation of amazon s3’s consistency behavior. In Proceedings of the 6th Workshop on Middleware for Service Oriented Computing, pages 1–6.
Bermbach, D. and Tai, S. (2014). Benchmarking eventual consistency: Lessons learned from long-term experimental studies. In 2014 IEEE International Conference on Cloud Engineering, pages 47–56. IEEE.
Bhagwan, R., Savage, S., and Voelker, G. M. (2003). Understanding availability. In International Workshop on Peer-to-Peer Systems, pages 256–267. Springer.
Borthakur, D. (2007). The hadoop distributed file system: Architecture and design. Hadoop Project Website, 11(2007):21.
Brewer, E. (2012). Cap twelve years later: How the ”rules” have changed. Computer, 45(2):23–29.
Burckhardt, S. (2014). Principles of eventual consistency.
Cheng, C.-S. (2016). Theory of Factorial Design. Chapman and Hall/CRC.
Chodorow, K. (2013). MongoDB: the definitive guide: powerful and scalable data storage. ”O’Reilly Media, Inc.”.
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Dede, E., Sendir, B., Kuzlu, P., Hartog, J., and Govindaraju, M. (2013). An evaluation of cassandra for hadoop. In 2013 IEEE Sixth International Conference on Cloud Computing, pages 494–501. IEEE.
Diogo, M., Cabral, B., and Bernardino, J. (2019). Consistency models of nosql databases. Future Internet, 11(2):43.
Gomes, C., Borba, E., Tavares, E., and Junior, M. N. d. O. (2019). Performability model for assessing nosql dbms consistency. In 2019 IEEE International Systems Conference (SysCon), pages 1–6. IEEE.
Gorbenko, A., Romanovsky, A., and Tarasyuk, O. (2019). Fault tolerant internet computing: Benchmarking and modelling trade-offs between availability, latency and consistency. Journal of Network and Computer Applications, 146:102412.
Halili, E. H. (2008). Apache JMeter: A practical beginner’s guide to automated testing and performance measurement for your websites. Packt Publishing Ltd.
Hewitt, E. (2010). Cassandra: the definitive guide. ”O’Reilly Media, Inc.”.
Lakshman, A. and Malik, P. (2010). Cassandra: a decentralized structured storage system. ACM SIGOPS Operating Systems Review, 44(2):35–40.
Le Lann, G. (1977). Distributed systems-towards a formal approach. In IFIP congress, volume 7, pages 155–160. Toronto.
Liu, S., Nguyen, S., Ganhotra, J., Rahman, M. R., Gupta, I., and Meseguer, J. (2015). Quantitative analysis of consistency in nosql key-value stores. In International Conference on Quantitative Evaluation of Systems, pages 228–243. Springer.
Membrey, P., Plugge, E., Hawkins, T., and Hawkins, D. (2010). The definitive guide to MongoDB: the noSQL database for cloud and desktop computing. Springer.
Nadiminti, K., De Assunçao, M. D., and Buyya, R. (2006). Distributed systems and recent innovations: Challenges and benefits. InfoNet Magazine, 16(3):1–5.
Nejati Sharif Aldin, H., Deldari, H., Moattar, M. H., and Razavi Ghods, M. (2019). Consistency models in distributed systems: A survey on definitions, disciplines, challenges and applications. arXiv e-prints, pages arXiv–1902.
Özsu, M. T. and Valduriez, P. (1996). Distributed and parallel database systems. ACM Computing Surveys (CSUR), 28(1):125–128.
Schultz, W., Avitabile, T., and Cabral, A. (2019). Tunable consistency in mongodb. Proceedings of the VLDB Endowment, 12(12):2071–2081.
Simon, S. (2000). Brewer’s cap theorem. CS341 Distributed Information Systems, University of Basel (HS2012).
Wang, H., Li, J., Zhang, H., and Zhou, Y. (2014). Benchmarking replication and consistency strategies in cloud serving databases: Hbase and cassandra. In Workshop on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, pages 71–82. Springer.
Abramova, V. and Bernardino, J. (2013). Nosql databases: Mongodb vs cassandra. In Proceedings of the international C* conference on computer science and software engineering, pages 14–22.
Bermbach, D. and Tai, S. (2011). Eventual consistency: How soon is eventual? an evaluation of amazon s3’s consistency behavior. In Proceedings of the 6th Workshop on Middleware for Service Oriented Computing, pages 1–6.
Bermbach, D. and Tai, S. (2014). Benchmarking eventual consistency: Lessons learned from long-term experimental studies. In 2014 IEEE International Conference on Cloud Engineering, pages 47–56. IEEE.
Bhagwan, R., Savage, S., and Voelker, G. M. (2003). Understanding availability. In International Workshop on Peer-to-Peer Systems, pages 256–267. Springer.
Borthakur, D. (2007). The hadoop distributed file system: Architecture and design. Hadoop Project Website, 11(2007):21.
Brewer, E. (2012). Cap twelve years later: How the ”rules” have changed. Computer, 45(2):23–29.
Burckhardt, S. (2014). Principles of eventual consistency.
Cheng, C.-S. (2016). Theory of Factorial Design. Chapman and Hall/CRC.
Chodorow, K. (2013). MongoDB: the definitive guide: powerful and scalable data storage. ”O’Reilly Media, Inc.”.
DB-Engines (2021). DB-Engines Ranking. https://db-engines.com/en/ranking. [Online; accessed 17-jan-2021].
Dede, E., Sendir, B., Kuzlu, P., Hartog, J., and Govindaraju, M. (2013). An evaluation of cassandra for hadoop. In 2013 IEEE Sixth International Conference on Cloud Computing, pages 494–501. IEEE.
Diogo, M., Cabral, B., and Bernardino, J. (2019). Consistency models of nosql databases. Future Internet, 11(2):43.
Gomes, C., Borba, E., Tavares, E., and Junior, M. N. d. O. (2019). Performability model for assessing nosql dbms consistency. In 2019 IEEE International Systems Conference (SysCon), pages 1–6. IEEE.
Gorbenko, A., Romanovsky, A., and Tarasyuk, O. (2019). Fault tolerant internet computing: Benchmarking and modelling trade-offs between availability, latency and consistency. Journal of Network and Computer Applications, 146:102412.
Halili, E. H. (2008). Apache JMeter: A practical beginner’s guide to automated testing and performance measurement for your websites. Packt Publishing Ltd.
Hewitt, E. (2010). Cassandra: the definitive guide. ”O’Reilly Media, Inc.”.
Lakshman, A. and Malik, P. (2010). Cassandra: a decentralized structured storage system. ACM SIGOPS Operating Systems Review, 44(2):35–40.
Le Lann, G. (1977). Distributed systems-towards a formal approach. In IFIP congress, volume 7, pages 155–160. Toronto.
Liu, S., Nguyen, S., Ganhotra, J., Rahman, M. R., Gupta, I., and Meseguer, J. (2015). Quantitative analysis of consistency in nosql key-value stores. In International Conference on Quantitative Evaluation of Systems, pages 228–243. Springer.
Membrey, P., Plugge, E., Hawkins, T., and Hawkins, D. (2010). The definitive guide to MongoDB: the noSQL database for cloud and desktop computing. Springer.
Nadiminti, K., De Assunçao, M. D., and Buyya, R. (2006). Distributed systems and recent innovations: Challenges and benefits. InfoNet Magazine, 16(3):1–5.
Nejati Sharif Aldin, H., Deldari, H., Moattar, M. H., and Razavi Ghods, M. (2019). Consistency models in distributed systems: A survey on definitions, disciplines, challenges and applications. arXiv e-prints, pages arXiv–1902.
Özsu, M. T. and Valduriez, P. (1996). Distributed and parallel database systems. ACM Computing Surveys (CSUR), 28(1):125–128.
Schultz, W., Avitabile, T., and Cabral, A. (2019). Tunable consistency in mongodb. Proceedings of the VLDB Endowment, 12(12):2071–2081.
Simon, S. (2000). Brewer’s cap theorem. CS341 Distributed Information Systems, University of Basel (HS2012).
Wang, H., Li, J., Zhang, H., and Zhou, Y. (2014). Benchmarking replication and consistency strategies in cloud serving databases: Hbase and cassandra. In Workshop on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, pages 71–82. Springer.
Publicado
26/10/2021
Como Citar
FERREIRA, Saulo; ANDRADE, Ermeson; MENDONÇA, Júlio.
Uma Abordagem Experimental para Avaliar os Níveis de Consistência do Banco de Dados NoSQL Cassandra. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 22. , 2021, Belo Horizonte.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 156-167.
DOI: https://doi.org/10.5753/wscad.2021.18520.
