Autoinstrumentation Adventures: enhancing Python apps with OpenTelemetry

Abstract

Hey there, fellow Python enthusiasts! Are you ready to dive into the exciting world of application observability without getting your hands too dirty with complex instrumentation? If that sounds like a journey you’d be interested in, then you’re in for a treat!

Observability is that magical window into the inner workings of our applications, allowing us to understand what’s happening under the hood, troubleshoot issues, and ensure everything is running smoothly. However, achieving this level of insight can sometimes feel like a daunting task. That’s where OpenTelemetry comes into play, simplifying the entire process and making it accessible to everyone, not just the observability wizards.

In our session, we’ll start with the basics: what OpenTelemetry is and the problems it aims to solve (and those it doesn’t). We’ll demystify the concept of instrumentation—the process of embedding observability into your applications—and show you how OpenTelemetry makes this not only possible but painless.

The heart of our talk will be focused on autoinstrumentation, a magical feature of OpenTelemetry that automates the task of adding observability to your Python projects. Imagine being able to get detailed insights into your application’s performance and behavior without having to manually instrument every nook and cranny. Sounds like a dream, right?

And because we believe in learning by doing, we’ll walk you through a small but mighty demo. You’ll see firsthand how effortlessly you can implement OpenTelemetry in your own Python applications, turning the daunting into the doable.

Recording

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