2024-11-16 –, LT8
Language: English
In this era of microservices, the '3 Pillars of System Observability', a.k.a. logging, metrics monitoring and traffic tracing are pivotal in giving developers quick feedbacks on the performance and behaviour of their application. With good observability practices, not only developers can understand the bottleneck and stability of their app, they can even be benefited from faster iteration cycle due to reliable feedback loops.
In this talk, the usage of OpenTelemetry package, a well-known open-source Observability stack, in Python will be addressed. OpenTelemetry is a vendor- and tool-agnostic, Observability stack integrating with a broad variety of Observability backends, including open source tools like Jaeger and Prometheus, as well as commercial offerings.
With the introduction of usage of OpenTelemetry in Python, it is hoped that users have more understanding on the '3 Pillars of System Observability', actively and confidently monitor their Python workloads, and define a suitable and meaningful Service Level Objective (SLO) for their program.
This talk will be broken down into following sessions:
- Introduction: 3 Pillars of System Observability (~5 mins)
- OpenTelemetry on Python FastAPI and AWS Lambda, with Visualizations (~15 - 20 mins)
- Why not just Logging? (~2 mins)
- Why not just Metrics (with Prometheus)? (~2 mins)
- Integration with other Cloud Monitoring Platforms (~2-3 mins)
I am a Cloud Engineer with past experiences of production-grade ML and backend developments. In my 4 years of experience in Python, I have been working on AI / recommender system and cloud computing. I like to explore topics in AI, information retrieval and system designs in my leisure time. I also contributes to some Rust and Python Open-Source projects. In the era of Cloud and AI / ML Technologies, I would like to share efficient and cost-effective system designs to others for advancing their developments or businesses.