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A pytest plugin for instrumenting test runs via OpenTelemetry

Project description


Instruments your pytest runs, exporting the spans and timing via OpenTelemetry.

Why instrument my test suite?

As projects grow larger, perhaps with many contributors, test suite runtime can be a significant limiting factor to how fast you and your team can deliver changes. By measuring your test suite's runtime in detail, and keeping a history of this runtime in a visualization tool like Jaeger, you can spot test bottlenecks that might be slowing your entire suite down.

Additionally, pytest makes an excellent driver for integration tests that operate on fully deployed systems, like your testing/staging environment. By using pytest-opentelemetry and configuring the appropriate propagators, you can connect traces from your integration test suite to your running system to analyze failures more quickly.

Even if you only enable pytest-opentelemetry locally for occasional debugging, it can help you understand exactly what is slowing your test suite down. Did you forget to mock that requests call? Didn't realize the test suite was creating 10,000 example accounts? Should that database setup fixture be marked scope=module? These are the kinds of questions pytest-opentelemetry can help you answer.

pytest-opentelemetry works even better when testing applications and libraries that are themselves instrumented with OpenTelemetry. This will give you deeper visibility into the layers of your stack, like database queries and network requests.

Installation and usage

pip install pytest-opentelemetry

Installing a library that exposes a specific pytest-related entry point is automatically loaded as a pytest plugin. Simply installing the plugin should be enough to register it for pytest.

Using the --export-traces flag enables trace exporting (otherwise, the created spans will only be tracked in memory):

pytest --export-traces

By default, this exports traces to http://localhost:4317, which will work well if you're running a local OpenTelemetry Collector exposing the OTLP gRPC interface. You can use any of the OpenTelemetry environment variables to adjust the tracing export or behavior:

export OTEL_EXPORTER_OTLP_ENDPOINT=http://another.collector:4317
pytest --export-traces

Only the OTLP over gRPC exporter is currently supported.

pytest-opentelemetry will use the name of the project's directory as the OpenTelemetry, but it will also respect the standard OTEL_SERVICE_NAME and OTEL_RESOURCE_ATTRIBUTES environment variables. If you would like to permanently specify those for your project, consider using the very helpful pytest-env package to set these for all test runs, for example, in your pyproject.toml:

env = [

If you are using the delightful pytest-xdist package to spread your tests out over multiple processes or hosts, pytest-opentelemetry will automatically unite them all under one trace. If this pytest run is part of a larger trace, you can provide a --trace-parent argument to nest this run under that parent:

pytest ... --trace-parent 00-1234567890abcdef1234567890abcdef-fedcba0987654321-01

Visualizing test traces

One quick way to visualize test traces would be to use an OpenTelemetry Collector feeding traces to Jaeger. This can be configured with a minimal Docker Compose file like:

version: "3.8"
    image: jaegertracing/all-in-one:1.33
    - 16686:16686    # frontend
    - 14250:14250    # model.proto
    image: otel/opentelemetry-collector-contrib:0.49.0
    - jaeger
    - 4317:4317      # OTLP (gRPC)
    - ./otelcol-config.yaml:/etc/otelcol-contrib/config.yaml:ro

With this otelcol-config.yaml:



    endpoint: jaeger:14250
      insecure: true

      receivers: [otlp]
      processors: [batch]
      exporters: [jaeger]


Two references I keep returning to is the pytest guide on writing plugins, and the pytest API reference:

These are extremely helpful in understanding the lifecycle of a pytest run.

To get setup for development, you will likely want to use a "virtual environment", using great tools like virtualenv or pyenv.

Once you have a virtual environment, install this package for editing, along with its development dependencies, with this command:

pip install -e '.[dev]'

When sending pull requests, don't forget to bump the version in setup.cfg.

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