GoKawiilImplementing OpenTelemetry in Rust Applications
Updated Mar 10, 2026 15 min read
Over the past several years, Rust has become the go-to language for reliable, high-throughput systems. It also influencing other ecosystems, with teams increasingly adopting Rust for the "heavy-duty" data processing layers.
Case in point: Python developers have been enjoying order-of-magnitude faster performance after switching to developer tools like Astral’s uv for project and dependency management, or ruff for their linting and formatting needs.
These tools have helped cut down on CI/CD bills, and have enabled large productivity gains for engineering teams around the globe.
Why do Rust Applications Need Observability?
If you know Rust, you might wonder why you need observability in the first place?
While the language itself offers incredible correctness guarantees, a Rust application can still encounter slowdowns and failures as it interacts with other system components, due to human error in business logic or higher-level runtime bugs.
As such, it becomes vital to have insights into how your applications process data, what events occur during these processes, and how they affect its health and performance.
In this write-up, we will explore how to use OpenTelemetry (OTel) to instrument Rust applications through a practical demo, delving into key implementation details as we progress. We will capture all three telemetry signals (traces, metrics, and logs) and visualize them using an OpenTelemetry-native backend.
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