opentelemetry-python VS opentelemetry-python-contrib

Python

As an example, Exemplars are part of the metrics spec [1]. The official python library says metrics status is 'stable' [2]. But there's an approximately 2-year old issue with no work on it, titled 'Metrics: Add support for exemplars', where the latest update is that no work has begun [3]. Nothing at a top-level of the opentelemetry-python project indicates that the project does not implement everything in the metrics spec, so if you wanted to use that capability, you are apt to discover it relatively late.

[1] https://opentelemetry.io/docs/specs/otel/metrics/data-model/...

[2] https://github.com/open-telemetry/opentelemetry-python

[3] https://github.com/open-telemetry/opentelemetry-python/issue...

Unfortunately, I have not seen any modern libraries that can match the features offered by the archived library. However, there are similar libraries that could be used, such as https://github.com/open-telemetry/opentelemetry-python which provides health checks and metrics. Additionally, you could use Prometheus libraries such as https://github.com/prometheus/client\_python to get similar metrics. However, both of these libraries might require some modification to get the desired features

We need to force a specific version of protobuf because of Launcher compatibility issues with newer versions. This was already fixed in opentelemetry-python.

I spent a huge amount of time - maybe 6 hours - digging into the problem. I was flipping back and forth between honeycomb's docs, the open telemetry docs, and the open telemetry python repo digging through issues.

You can check out the current releases of opentelemetry-python.

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.

More specifically, auto-instrumentation uses shims or bytecode instrumentation agents to intercept your code at runtime or at compile-time to add tracing and metrics instrumentation to the libraries and frameworks you depend on. The beauty of auto-instrumentation is that it requires a minimum amount of effort. Sit back, relax, and enjoy the show. A number of popular Python libraries are auto-instrumented, including Flask and Django. You can find the full list here.

And sure, there's gaps and those are awful when you get to them. But writing minimal tracing integration is pretty easy. This is the full source of the psycopg2 instrumentation. https://github.com/open-telemetry/opentelemetry-python-contrib/blob/main/instrumentation/opentelemetry-instrumentation-psycopg2/src/opentelemetry/instrumentation/psycopg2/__init__.py