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In my last blog post, I showed y’all how to instrument Python code with OpenTelemetry (OTel), à la auto-instrumentation. You may also recall from that post that I recommended using the Python auto-instrumentation binary even for non-auto-instrumented libraries, because it abstracts all that pesky OTel config stuff so nicely. When you use it, along with any applicable Python auto-instrumentation libraries (installed courtesy of opentelemetry-bootstrap), it takes care of context propagation across related services for you.
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Today we learned how to manually configure OpenTelemetry for Python to connect to Lightstep (this also works for any Observability back-end that ingests the OTLP format). We also learned how to link related services together through manual context propagation.
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A basic understanding of Python and Python virtual environments
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opentelemetry-examples
Example code and resources for working with OpenTelemetry, provided by Lightstep
The example in this tutorial can be found in the lightstep/opentelemetry-examples repo. We will be working with three main files:
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Note: Although the OpenTelemetry documentation does provide some insight into how to do manual context propagation in Python, the documentation needs a little work. I’m actually part of the OpenTelemetry Comms SIG, so I am using this as motivation to improve the docs around this topic…stay tuned for updates to the OTel docs too! 😎
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If you peek over at the terminal running server.py, you will likely notice a super-ugly stack trace. DON’T PANIC! The /ping service makes calls to Redis and MongoDB, and since neither of these services is running, you end up getting some nasty error messages like this:
