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RailsConf 2019 - Scalable Observability for Rails Applications by John Feminella

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Do you measure and monitor your Rails applications by alerting on metrics like CPU, memory usage, and request latency? Do you get large numbers of false positives, and wish you had fewer useless messages cluttering up your inbox? What if there were a better way to monitor your Rails applications that resulted in far more signal — and much less noise?

In this talk, we'll share an approach that's effective at measuring and monitoring distributed applications and systems. Through the application of a few simple core principles and a little bit of mathematical elbow grease, firms we've tried this with have seen significant results. By the end, you'll have the tools to ensure your applications will be healthier, more observable, and a lot less work to monitor.

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In his talk at RailsConf 2019, John Feminella presents 'Scalable Observability for Rails Applications,' focusing on how to effectively monitor distributed applications. He emphasizes the importance of understanding observability, distinguishing it from traditional monitoring and metrics. Here are the key points discussed throughout the talk:

- **Understanding Observability**: Feminella explains that observability is about knowing the state of a system from its outputs rather than just raw metrics like CPU or memory usage.

- **Common Challenges**: He highlights common pitfalls in application monitoring, such as measuring irrelevant metrics that do not contribute to understanding application health or accumulating countless alert notifications (false positives).

- **Service Level Objectives (SLOs)**: He introduces the concept of SLOs, which are promises applications make regarding performance (e.g., processing requests in a timely manner). There are three components: Service Level Indicators (SLIs), SLOs, and Service Level Agreements (SLAs).

- **Tools for Observability**: Feminella outlines three tools that should be part of any observability strategy:
  - Metrics: Help quantify aspects of system performance or health.
  - Logging: Provides a stream of events for debugging and understanding system issues.
  - Tracing: Captures the order of events to provide context about system operations.

- **The Four Golden Signals**: He discusses four critical metrics to measure: traffic, latency, saturation, and errors, stressing that understanding these can significantly enhance application monitoring.

- **Customized Metrics**: He argues against a one-size-fits-all approach to metrics, encouraging teams to establish what is specific to each application based on its unique demands and context.

- **Practical Examples**: Drawing from experience with Fortune 100 companies, Feminella shares anecdotal evidence showing that using a more thorough observability strategy can reduce false positives and improve response relevance to real issues.

- **Key Takeaways**: Feminella concludes that observability requires a comprehensive toolkit, a focus on meaningful metrics relative to application promises, and iteration based on real-world experiences to achieve effective monitoring results. His advice is to start with understanding what each application does and to measure what matters based on the demands on those applications.

This talk serves as a guide for developers to build healthier and more observable Rails applications, leading to fewer unnecessary alerts and better overall system health.

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