Setup

1. Install the Datadog Python tracer in your Dockerfile.

   Dockerfile

   Copy

   RUN pip install --target /dd_tracer/python/ ddtrace

   For more information, see Tracing Python applications.

2. Install serverless-init.

   Datadog publishes new releases of the serverless-init container image to Google’s gcr.io, AWS’s ECR, and on Docker Hub:

   hub.docker.com	gcr.io	public.ecr.aws
   datadog/serverless-init	gcr.io/datadoghq/serverless-init	public.ecr.aws/datadog/serverless-init

   Images are tagged based on semantic versioning, with each new version receiving three relevant tags:

   • 1, 1-alpine: use these to track the latest minor releases, without breaking changes
   • 1.x.x, 1.x.x-alpine: use these to pin to a precise version of the library
   • latest, latest-alpine: use these to follow the latest version release, which may include breaking changes

   Add the following instructions and arguments to your Dockerfile.

   COPY --from=datadog/serverless-init:1 /datadog-init /app/datadog-init
   ENTRYPOINT ["/app/datadog-init"]
   CMD ["/dd_tracer/python/bin/ddtrace-run", "python", "path/to/your/python/app.py"]
   

   Alternative configuration

   Datadog expects serverless-init to be the top-level application, with the rest of your app’s command line passed in for serverless-init to execute.

   If you already have an entrypoint defined inside your Dockerfile, you can instead modify the CMD argument.

   CMD ["/app/datadog-init", "/dd_tracer/python/bin/ddtrace-run", "python", "path/to/your/python/app.py"]
   

   If you require your entrypoint to be instrumented as well, you can instead swap your entrypoint and CMD arguments.

   ENTRYPOINT ["/app/datadog-init"]
   CMD ["/your_entrypoint.sh", "/dd_tracer/python/bin/ddtrace-run", "python", "path/to/your/python/app.py"]
   

   As long as your command to run is passed as an argument to datadog-init, you will receive full instrumentation.

   
   

3. Set up logs.

   To enable logging, set the environment variable DD_LOGS_ENABLED=true. This allows serverless-init to read logs from stdout and stderr.

   Datadog also recommends the following environment variables:

   • ENV PYTHONUNBUFFERED=1: Ensure Python outputs appear immediately in container logs instead of being buffered.
   • ENV DD_SOURCE=python: Enable advanced Datadog log parsing.

   If you want multiline logs to be preserved in a single log message, Datadog recommends writing your logs in JSON format. For example, you can use a third-party logging library such as structlog:

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   import structlog
   
   def tracer_injection(logger, log_method, event_dict):
       event_dict.update(tracer.get_log_correlation_context())
       return event_dict
   
   structlog.configure(
       processors=[
           tracer_injection,
           structlog.processors.EventRenamer("msg"),
           structlog.processors.JSONRenderer()
       ],
       logger_factory=structlog.WriteLoggerFactory(file=sys.stdout),
   )
   
   logger = structlog.get_logger()
   
   logger.info("Hello world!")

   For more information, see Correlating Python Logs and Traces.

4. Configure your application.

   After the container is built and pushed to your registry, set the required environment variables for the Datadog Agent:

   • DD_API_KEY: Your Datadog API key, used to send data to your Datadog account. For privacy and safety, configure this API key as a Google Cloud Secret.
   • DD_SITE: Your Datadog site. For example, datadoghq.com.

   For more environment variables, see the Environment variables section on this page.

   The following command deploys the service and allows any external connection to reach it. In this example, your service listening is set to port 8080. Ensure that this port number matches the exposed port inside of your Dockerfile.

   gcloud run deploy <APP_NAME>
     --image=gcr.io/<YOUR_PROJECT>/<APP_NAME> \
     --port=8080 \
     --update-env-vars=DD_API_KEY=$DD_API_KEY \
     --update-env-vars=DD_SITE=$DD_SITE \
   

5. Finally, add a service label in Google Cloud. In your Cloud Run service’s info panel, add a label with the following key and value:

   Key	Value
   service	The name of your service. Matches the value provided as the DD_SERVICE environment variable.

   See Configure labels for services in the Cloud Run documentation for instructions.

Environment variables

Troubleshooting

This integration depends on your runtime having a full SSL implementation. If you are using a slim image, you may need to add the following command to your Dockerfile to include certificates:

RUN apt-get update && apt-get install -y ca-certificates

To have your Cloud Run services appear in the software catalog, you must set the DD_SERVICE, DD_VERSION, and DD_ENV environment variables.

Further reading

お役に立つドキュメント、リンクや記事:

Tracing Python ApplicationsDOCUMENTATION Correlating Python Logs and TracesDOCUMENTATION