Rumo a um Catálogo de Anti-padrões de Observabilidade: Uma Revisão da Literatura
Resumo
Os sistemas de software modernos são cada vez mais construídos sobre arquiteturas de microsserviços para alcançar maior escalabilidade, modularidade e suporte à entrega contínua. Embora esse estilo arquitetural simplifique o desenvolvimento, ele também introduz considerável complexidade operacional, tornando as falhas do sistema mais frequentes e difíceis de diagnosticar. Para lidar com esses desafios, a observabilidade tornou-se uma prática fundamental, permitindo a introspecção do sistema por meio de logs, métricas e traces. No entanto, apesar de seu papel crítico, a observabilidade muitas vezes é implementada de forma inadequada devido à ausência de diretrizes padronizadas, levando a monitoramento ineficaz, fadiga de alertas e redução da eficiência na resposta a incidentes. Embora pesquisas existentes abordem ferramentas, conceitos e desafios da observabilidade, nenhum trabalho anterior focou especificamente na identificação e análise de anti-padrões de observabilidade. Como consequência, decisões de projeto e implementação são frequentemente tomadas sem uma compreensão clara dos erros mais comuns, resultando em soluções ruins. Este artigo apresenta os resultados de uma revisão sistemática da literatura combinada com uma revisão da literatura cinzenta, culminando em um catálogo de 37 anti-padrões de observabilidade. Cada anti-padrão é descrito de forma sucinta, destacando como ele tipicamente se manifesta e oferecendo estratégias de refatoração acionáveis para mitigar seus efeitos. Nossos resultados mostram que o catálogo serve como um recurso valioso para ajudar equipes a reconhecer e abordar problemas recorrentes de observabilidade em sistemas baseados em microsserviços.
Palavras-chave:
Monitoramento, Observabilidade, Microsserviços, Catálogo, Antipadrões
Referências
AWS. 2025. Anti-patterns for continuous monitoring. (2025). [link] Accessed: March 10, 2025.
AWS. 2025. AWS Observability Best Practices. (2025). [link] Accessed: March 10, 2025.
Budhaditya Bhattacharya. 2024. Bad API observability: 5 anti-patterns in 5 minutes. (2024). [link] Accessed: March 10, 2025.
C2 Wiki. 2024. Anti-Pattern Template. [link] Accessed: March 9, 2025.
Tomas Cerny, Amr S Abdelfattah, Abdullah Al Maruf, Andrea Janes, and Davide Taibi. 2023. Catalog and detection techniques of microservice anti-patterns and bad smells: A tertiary study. Journal of Systems and Software 206 (2023), 111829.
Boyuan Chen and Zhen Ming Jiang. 2021. A survey of software log instrumentation. ACM Computing Surveys (CSUR) 54, 4 (2021), 1–34.
S. Chevre. 2024. 7 API Observability Anti-Patterns to Avoid. (2024). [link] Accessed: March 10, 2025.
Shuiguang Deng, Hailiang Zhao, Binbin Huang, Cheng Zhang, Feiyi Chen, Yinuo Deng, Jianwei Yin, Schahram Dustdar, and Albert Y Zomaya. 2023. Cloud-Native Computing: A Survey from the Perspective of Services. arXiv preprint arXiv:2306.14402 (2023).
Paolo Di Francesco, Ivano Malavolta, and Patricia Lago. 2017. Research on architecting microservices: Trends, focus, and potential for industrial adoption. In 2017 IEEE International conference on software architecture (ICSA). IEEE, 21–30.
António Pedro dos Santos Carvalho. 2022. Observabilidade e telemetria em arquiteturas de micro-serviços. Master’s thesis. Universidade do Porto (Portugal).
Nicola Dragoni, Saverio Giallorenzo, Alberto Lluch Lafuente, Manuel Mazzara, Fabrizio Montesi, Ruslan Mustafin, and Larisa Safina. 2017. Microservices: yesterday, today, and tomorrow. Present and ulterior software engineering (2017), 195–216.
Paulo Duarte, Rainara Carvalho, and Windson Viana. 2024. A Catalog of Interoperability Solutions for Ambient Assisted Living. In Anais do XVIII Simpósio Brasileiro de Componentes, Arquiteturas e Reutilização de Software (Curitiba/PR). SBC, Porto Alegre, RS, Brasil, 61–70. DOI: 10.5753/sbcars.2024.3859
Francisco Gomes, Paulo Rego, and Fernando Trinta. 2024. Rumo a uma Taxonomia de Observabilidade para Aplicações Baseadas em Microsserviços. In Anais do XXXVIII Simpósio Brasileiro de Engenharia de Software (Curitiba/PR). SBC, Porto Alegre, RS, Brasil, 234–245. DOI: 10.5753/sbes.2024.3386
Mia E Gortney, Patrick E Harris, Tomas Cerny, Abdullah Al Maruf, Miroslav Bures, Davide Taibi, and Pavel Tisnovsky. 2022. Visualizing microservice architecture in the dynamic perspective: A systematic mapping study. IEEE Access 10 (2022), 119999–120012.
Shenghui Gu, Guoping Rong, He Zhang, and Haifeng Shen. 2022. Logging practices in software engineering: A systematic mapping study. IEEE transactions on software engineering 49, 2 (2022), 902–923.
Shilin He, Pinjia He, Zhuangbin Chen, Tianyi Yang, Yuxin Su, and Michael R Lyu. 2021. A survey on automated log analysis for reliability engineering. ACM computing surveys (CSUR) 54, 6 (2021), 1–37.
Rudolf E Kalman. 1960. On the general theory of control systems. In Proceedings First International Conference on Automatic Control, Moscow, USSR. 481–492.
Suman Karumuri, Franco Solleza, Stan Zdonik, and Nesime Tatbul. 2021. Towards observability data management at scale. ACM SIGMOD Record 49, 4 (2021), 18–23.
Joanna Kosińska, Bartosz Baliś,Marek Konieczny, Maciej Malawski, and Sławomir Zielinśki. 2023. Towards the Observability of Cloud-native applications: The Overview of the State-of-the-Art. IEEE Access (2023).
Joanna Kosińska and Krzysztof Zieliński. 2020. Autonomic management framework for cloud-native applications. Journal of Grid Computing 18 (2020), 779–796.
Nane Kratzke. 2022. Cloud-native observability: the many-faceted benefits of structured and unified logging—a multi-case study. Future Internet 14, 10 (2022), 274.
T. LaRock. 2023. Everything You Know About Observability is Wrong. (2023). [link] Accessed: March 10, 2025.
Joshua Levin and Theophilus A Benson. 2020. ViperProbe: Rethinking microservice observability with eBPF. In 2020 IEEE 9th International Conference on Cloud Networking (CloudNet). IEEE, 1–8.
Bowen Li, Xin Peng, Qilin Xiang, HanzhangWang, Tao Xie, Jun Sun, and Xuanzhe Liu. 2022. Enjoy your observability: an industrial survey of microservice tracing and analysis. Empirical Software Engineering 27 (2022), 1–28.
Nicolas Marie-Magdelaine. 2021. Observability and resources managements in cloud-native environnements. Ph.D. Dissertation. Bordeaux.
Sameer Mhaisekar. 2024. 10 tips to BAD Observability. (2024). [link] Accessed: March 10, 2025.
D. Mittal. 2022. Mistakes to avoid in Observability. (2022). [link] Accessed: March 10, 2025.
Moeen Ali Naqvi, Sehrish Malik, Merve Astekin, and Leon Moonen. 2022. On Evaluating Self-Adaptive and Self-Healing Systems using Chaos Engineering. In 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). IEEE, 1–10.
Chujie Ni. 2023. Adopting Observability-Driven Development for Cloud-Native Applications : Designing End-to-end Observability Pipeline using Open-source Software. Master’s thesis. KTH, School of Electrical Engineering and Computer Science (EECS).
Sina Niedermaier, Falko Koetter, Andreas Freymann, and Stefan Wagner. 2019. On observability and monitoring of distributed systems–an industry interview study. In Service-Oriented Computing: 17th International Conference, ICSOC 2019, Toulouse, France, October 28–31, 2019, Proceedings 17. Springer, 36–52.
J. O’Donnell. 2024. Top 10 Mistakes People Make When Building Observability Dashboards. (2024). [link] Accessed: March 10, 2025.
A. Patel. 2024. How to Resolve Bad Observability Data Quality. (2024). [link] Accessed: March 10, 2025.
A. Patel. 2024. How to Solve 3 Data Flow Issues in Observability. (2024). [link] Accessed: March 10, 2025.
William Pourmajidi, Lei Zhang, John Steinbacher, Tony Erwin, and Andriy Miranskyy. 2023. A Reference Architecture for Observability and Compliance of Cloud Native Applications. arXiv preprint arXiv:2302.11617 (2023).
Mario Scrocca, Riccardo Tommasini, Alessandro Margara, Emanuele Della Valle, and Sherif Sakr. 2020. The Kaiju project: enabling event-driven observability. In Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems. 85–96.
Anas Shatnawi, Bachar Rima, Zakarea Alshara, Gabriel Darbord, Abdelhak-Djamel Seriai, and Christophe Bortolaso. 2023. Telemetry of Legacy Web Applications: An Industrial Case Study. (Nov. 2023). DOI: 10.36227/techrxiv.24449092 working paper or preprint.
Andy Singleton. 2016. The economics of microservices. IEEE Cloud Computing 3, 5 (2016), 16–20.
Davide Taibi, Ben Kehoe, and Danilo Poccia. 2022. Serverless: from bad practices to good solutions. In 2022 IEEE International Conference on Service-Oriented System Engineering (SOSE). IEEE, 85–92.
Johannes Thönes. 2015. Microservices. IEEE software 32, 1 (2015), 116–116.
Rafik Tighilt, Manel Abdellatif, Naouel Moha, Hafedh Mili, Ghizlane El Boussaidi, Jean Privat, and Yann-Gaël Guéhéneuc. 2020. On the Study of Microservices Antipatterns: a Catalog Proposal (EuroPLoP ’20). Association for Computing Machinery, NewYork, NY, USA, Article 34, 13 pages. DOI: 10.1145/3424771.3424812
S. Townshend. 2023. Bad Observability. (2023). [link] Accessed: March 10, 2025.
Muhammad Usman, Simone Ferlin, Anna Brunstrom, and Javid Taheri. 2022. A survey on observability of distributed edge & container-based microservices. IEEE Access 10 (2022), 86904–86919.
A. Villela. 2022. Observability Mythbusters: Observability Anti-Patterns. (2022). [link] Accessed: March 10, 2025.
A. Villela. 2024. OpenTelemetry Collector Anti-Patterns. (2024). [link] Accessed: March 10, 2025.
Anton Widerberg and Erik Johansson. 2021. Observability of Cloud Native Systems: : An industrial case study of system comprehension with Prometheus & knowledge transfer. 111 pages.
Indika Wimalasuriya. 2024. AWS: Your Ally Against Observability Anti-Patterns | Conf42 Observability 2024. (2024). [link] Accessed: March 10, 2025.
Tianyi Yang, Jiacheng Shen, Yuxin Su, Xiaoxue Ren, Yongqiang Yang, and Michael R Lyu. 2022. Characterizing and mitigating anti-patterns of alerts in industrial cloud systems. In 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 393–401.
AWS. 2025. AWS Observability Best Practices. (2025). [link] Accessed: March 10, 2025.
Budhaditya Bhattacharya. 2024. Bad API observability: 5 anti-patterns in 5 minutes. (2024). [link] Accessed: March 10, 2025.
C2 Wiki. 2024. Anti-Pattern Template. [link] Accessed: March 9, 2025.
Tomas Cerny, Amr S Abdelfattah, Abdullah Al Maruf, Andrea Janes, and Davide Taibi. 2023. Catalog and detection techniques of microservice anti-patterns and bad smells: A tertiary study. Journal of Systems and Software 206 (2023), 111829.
Boyuan Chen and Zhen Ming Jiang. 2021. A survey of software log instrumentation. ACM Computing Surveys (CSUR) 54, 4 (2021), 1–34.
S. Chevre. 2024. 7 API Observability Anti-Patterns to Avoid. (2024). [link] Accessed: March 10, 2025.
Shuiguang Deng, Hailiang Zhao, Binbin Huang, Cheng Zhang, Feiyi Chen, Yinuo Deng, Jianwei Yin, Schahram Dustdar, and Albert Y Zomaya. 2023. Cloud-Native Computing: A Survey from the Perspective of Services. arXiv preprint arXiv:2306.14402 (2023).
Paolo Di Francesco, Ivano Malavolta, and Patricia Lago. 2017. Research on architecting microservices: Trends, focus, and potential for industrial adoption. In 2017 IEEE International conference on software architecture (ICSA). IEEE, 21–30.
António Pedro dos Santos Carvalho. 2022. Observabilidade e telemetria em arquiteturas de micro-serviços. Master’s thesis. Universidade do Porto (Portugal).
Nicola Dragoni, Saverio Giallorenzo, Alberto Lluch Lafuente, Manuel Mazzara, Fabrizio Montesi, Ruslan Mustafin, and Larisa Safina. 2017. Microservices: yesterday, today, and tomorrow. Present and ulterior software engineering (2017), 195–216.
Paulo Duarte, Rainara Carvalho, and Windson Viana. 2024. A Catalog of Interoperability Solutions for Ambient Assisted Living. In Anais do XVIII Simpósio Brasileiro de Componentes, Arquiteturas e Reutilização de Software (Curitiba/PR). SBC, Porto Alegre, RS, Brasil, 61–70. DOI: 10.5753/sbcars.2024.3859
Francisco Gomes, Paulo Rego, and Fernando Trinta. 2024. Rumo a uma Taxonomia de Observabilidade para Aplicações Baseadas em Microsserviços. In Anais do XXXVIII Simpósio Brasileiro de Engenharia de Software (Curitiba/PR). SBC, Porto Alegre, RS, Brasil, 234–245. DOI: 10.5753/sbes.2024.3386
Mia E Gortney, Patrick E Harris, Tomas Cerny, Abdullah Al Maruf, Miroslav Bures, Davide Taibi, and Pavel Tisnovsky. 2022. Visualizing microservice architecture in the dynamic perspective: A systematic mapping study. IEEE Access 10 (2022), 119999–120012.
Shenghui Gu, Guoping Rong, He Zhang, and Haifeng Shen. 2022. Logging practices in software engineering: A systematic mapping study. IEEE transactions on software engineering 49, 2 (2022), 902–923.
Shilin He, Pinjia He, Zhuangbin Chen, Tianyi Yang, Yuxin Su, and Michael R Lyu. 2021. A survey on automated log analysis for reliability engineering. ACM computing surveys (CSUR) 54, 6 (2021), 1–37.
Rudolf E Kalman. 1960. On the general theory of control systems. In Proceedings First International Conference on Automatic Control, Moscow, USSR. 481–492.
Suman Karumuri, Franco Solleza, Stan Zdonik, and Nesime Tatbul. 2021. Towards observability data management at scale. ACM SIGMOD Record 49, 4 (2021), 18–23.
Joanna Kosińska, Bartosz Baliś,Marek Konieczny, Maciej Malawski, and Sławomir Zielinśki. 2023. Towards the Observability of Cloud-native applications: The Overview of the State-of-the-Art. IEEE Access (2023).
Joanna Kosińska and Krzysztof Zieliński. 2020. Autonomic management framework for cloud-native applications. Journal of Grid Computing 18 (2020), 779–796.
Nane Kratzke. 2022. Cloud-native observability: the many-faceted benefits of structured and unified logging—a multi-case study. Future Internet 14, 10 (2022), 274.
T. LaRock. 2023. Everything You Know About Observability is Wrong. (2023). [link] Accessed: March 10, 2025.
Joshua Levin and Theophilus A Benson. 2020. ViperProbe: Rethinking microservice observability with eBPF. In 2020 IEEE 9th International Conference on Cloud Networking (CloudNet). IEEE, 1–8.
Bowen Li, Xin Peng, Qilin Xiang, HanzhangWang, Tao Xie, Jun Sun, and Xuanzhe Liu. 2022. Enjoy your observability: an industrial survey of microservice tracing and analysis. Empirical Software Engineering 27 (2022), 1–28.
Nicolas Marie-Magdelaine. 2021. Observability and resources managements in cloud-native environnements. Ph.D. Dissertation. Bordeaux.
Sameer Mhaisekar. 2024. 10 tips to BAD Observability. (2024). [link] Accessed: March 10, 2025.
D. Mittal. 2022. Mistakes to avoid in Observability. (2022). [link] Accessed: March 10, 2025.
Moeen Ali Naqvi, Sehrish Malik, Merve Astekin, and Leon Moonen. 2022. On Evaluating Self-Adaptive and Self-Healing Systems using Chaos Engineering. In 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). IEEE, 1–10.
Chujie Ni. 2023. Adopting Observability-Driven Development for Cloud-Native Applications : Designing End-to-end Observability Pipeline using Open-source Software. Master’s thesis. KTH, School of Electrical Engineering and Computer Science (EECS).
Sina Niedermaier, Falko Koetter, Andreas Freymann, and Stefan Wagner. 2019. On observability and monitoring of distributed systems–an industry interview study. In Service-Oriented Computing: 17th International Conference, ICSOC 2019, Toulouse, France, October 28–31, 2019, Proceedings 17. Springer, 36–52.
J. O’Donnell. 2024. Top 10 Mistakes People Make When Building Observability Dashboards. (2024). [link] Accessed: March 10, 2025.
A. Patel. 2024. How to Resolve Bad Observability Data Quality. (2024). [link] Accessed: March 10, 2025.
A. Patel. 2024. How to Solve 3 Data Flow Issues in Observability. (2024). [link] Accessed: March 10, 2025.
William Pourmajidi, Lei Zhang, John Steinbacher, Tony Erwin, and Andriy Miranskyy. 2023. A Reference Architecture for Observability and Compliance of Cloud Native Applications. arXiv preprint arXiv:2302.11617 (2023).
Mario Scrocca, Riccardo Tommasini, Alessandro Margara, Emanuele Della Valle, and Sherif Sakr. 2020. The Kaiju project: enabling event-driven observability. In Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems. 85–96.
Anas Shatnawi, Bachar Rima, Zakarea Alshara, Gabriel Darbord, Abdelhak-Djamel Seriai, and Christophe Bortolaso. 2023. Telemetry of Legacy Web Applications: An Industrial Case Study. (Nov. 2023). DOI: 10.36227/techrxiv.24449092 working paper or preprint.
Andy Singleton. 2016. The economics of microservices. IEEE Cloud Computing 3, 5 (2016), 16–20.
Davide Taibi, Ben Kehoe, and Danilo Poccia. 2022. Serverless: from bad practices to good solutions. In 2022 IEEE International Conference on Service-Oriented System Engineering (SOSE). IEEE, 85–92.
Johannes Thönes. 2015. Microservices. IEEE software 32, 1 (2015), 116–116.
Rafik Tighilt, Manel Abdellatif, Naouel Moha, Hafedh Mili, Ghizlane El Boussaidi, Jean Privat, and Yann-Gaël Guéhéneuc. 2020. On the Study of Microservices Antipatterns: a Catalog Proposal (EuroPLoP ’20). Association for Computing Machinery, NewYork, NY, USA, Article 34, 13 pages. DOI: 10.1145/3424771.3424812
S. Townshend. 2023. Bad Observability. (2023). [link] Accessed: March 10, 2025.
Muhammad Usman, Simone Ferlin, Anna Brunstrom, and Javid Taheri. 2022. A survey on observability of distributed edge & container-based microservices. IEEE Access 10 (2022), 86904–86919.
A. Villela. 2022. Observability Mythbusters: Observability Anti-Patterns. (2022). [link] Accessed: March 10, 2025.
A. Villela. 2024. OpenTelemetry Collector Anti-Patterns. (2024). [link] Accessed: March 10, 2025.
Anton Widerberg and Erik Johansson. 2021. Observability of Cloud Native Systems: : An industrial case study of system comprehension with Prometheus & knowledge transfer. 111 pages.
Indika Wimalasuriya. 2024. AWS: Your Ally Against Observability Anti-Patterns | Conf42 Observability 2024. (2024). [link] Accessed: March 10, 2025.
Tianyi Yang, Jiacheng Shen, Yuxin Su, Xiaoxue Ren, Yongqiang Yang, and Michael R Lyu. 2022. Characterizing and mitigating anti-patterns of alerts in industrial cloud systems. In 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 393–401.
Publicado
22/09/2025
Como Citar
GOMES, Francisco A. A.; REGO, Paulo A. L.; TRINTA, Fernando A. M..
Rumo a um Catálogo de Anti-padrões de Observabilidade: Uma Revisão da Literatura. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE (SBES), 39. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 104-114.
ISSN 2833-0633.
DOI: https://doi.org/10.5753/sbes.2025.9779.
