Instruments your pytest runs, exporting the spans and timing via OpenTelemetry.
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.
pip install pytest-opentelemetryInstalling 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-tracesBy 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-tracesOnly the OTLP over gRPC exporter is currently supported.
pytest-opentelemetry will use the name of the project's directory as the OpenTelemetry
service.name, 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:
[tool.pytest.ini_options]
env = [
"OTEL_RESOURCE_ATTRIBUTES=service.name=my-project",
]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-fedcba0987654321-01One 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"
services:
jaeger:
image: jaegertracing/all-in-one:1.33
ports:
- 16686:16686 # frontend
- 14250:14250 # model.proto
collector:
image: otel/opentelemetry-collector-contrib:0.49.0
depends_on:
- jaeger
ports:
- 4317:4317 # OTLP (gRPC)
volumes:
- ./otelcol-config.yaml:/etc/otelcol-contrib/config.yaml:roWith this otelcol-config.yaml:
receivers:
otlp:
protocols:
grpc:
processors:
batch:
exporters:
jaeger:
endpoint: jaeger:14250
tls:
insecure: true
service:
pipelines:
traces:
receivers: [otlp]
processors: [batch]
exporters: [jaeger]Two references I keep returning to is the pytest guide on writing plugins, and the pytest API reference:
- https://docs.pytest.org/en/6.2.x/writing_plugins.html
- https://docs.pytest.org/en/6.2.x/reference.html#hooks
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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