• For general knowledge about the EDOT Python SDK, refer to theEDOT Java Intro page.
• For Python auto-instrumentation specifics, refer toOpenTelemetry Operator Python auto-instrumentation.
• To manually instrument your Python application code (by customizing spans and metrics), refer toEDOT Python manual instrumentation.
• For general information about instrumenting applications on Kubernetes, refer toinstrumenting applications on Kubernetes.

The following environments and configurations are supported:

• EDOT Python container image supportsglibc andmusl based auto-instrumentation for Python 3.12.
• musl based containers instrumentation requires anextra annotation and operator v0.113.0+.
• To turn on logs auto-instrumentation, refer toauto-instrument python logs.
• To turn on specific instrumentation libraries, refer toexcluding auto-instrumentation.
• For a full list of configuration options, refer toPython specific configuration.
• For Python specific limitations when using the OpenTelemetry operator, refer toPython-specific topics.

Following this example, you can learn how to:

• Turn on auto-instrumentation of a Python application using one of the following supported methods:
  • Adding an annotation to the deployment Pods.
  • Adding an annotation to the namespace.
• Verify that auto-instrumentation libraries are injected and configured correctly.
• Confirm data is flowing toKibana Observability.

For this example, we assume the application you're instrumenting is a deployment namedpython-app running in thepython-ns namespace.

1. Ensure you have successfullyinstalled the OpenTelemetry Operator, and confirm that the followingInstrumentation object exists in the system:

   $ kubectl get instrumentation -n opentelemetry-operator-systemNAME                      AGE    ENDPOINTelastic-instrumentation   107s   http://opentelemetry-kube-stack-daemon-collector.opentelemetry-operator-system.svc.cluster.local:4318

   Note

   If yourInstrumentation object has a different name or is created in a different namespace, you will have to adapt the annotation value in the next step.

2. Turn on auto-instrumentation of the Python application using one of the following methods:

   • Edit your application workload definition and include the annotation underspec.template.metadata.annotations:

     spec:# ...template:    metadata:    labels:        app: python-app    annotations:        instrumentation.opentelemetry.io/inject-python: opentelemetry-operator-system/elastic-instrumentation# ...

   • Alternatively, add the annotation at namespace level to apply auto-instrumentation in all Pods of the namespace:

     kubectl annotate namespace python-ns instrumentation.opentelemetry.io/inject-python=opentelemetry-operator-system/elastic-instrumentation

3. Restart the application:

   After the annotation has been set, restart the application to create new Pods and inject the instrumentation libraries:

   bash kubectl rollout restart deployment python-app -n python-ns

4. Verify theauto-instrumentation resources are injected in the Pod:

   Run akubectl describe of one of your application pods and check:

   • There should be an init container namedopentelemetry-auto-instrumentation-python in the Pod:

     $ kubectl describe pod python-app-8d84c47b8-8h5z2 -n python-ns......Init Containers:opentelemetry-auto-instrumentation-python:    Container ID:  containerd://fdc86b3191e34ef5ec872853b14a950d0af1e36b0bc207f3d59bd50dd3caafe9    Image:         docker.elastic.co/observability/elastic-otel-python:0.3.0    Image ID:      docker.elastic.co/observability/elastic-otel-python@sha256:de7b5cce7514a10081a00820a05097931190567ec6e18a384ff7c148bad0695e    Port:          <none>    Host Port:     <none>    Command:    cp    -r    /autoinstrumentation/.    /otel-auto-instrumentation-python    State:          Terminated    Reason:       Completed...

   • The main container has new environment variables, includingPYTHONPATH:

     ...Containers:python-app:...    Environment:...    PYTHONPATH:                          /otel-auto-instrumentation-python/opentelemetry/instrumentation/auto_instrumentation:/otel-auto-instrumentation-python    OTEL_EXPORTER_OTLP_PROTOCOL:         http/protobuf    OTEL_TRACES_EXPORTER:                otlp    OTEL_METRICS_EXPORTER:               otlp    OTEL_SERVICE_NAME:                   python-app    OTEL_EXPORTER_OTLP_ENDPOINT:         http://opentelemetry-kube-stack-daemon-collector.opentelemetry-operator-system.svc.cluster.local:4318...

   • The Pod has anEmptyDir volume namedopentelemetry-auto-instrumentation-python mounted in both the main and the init containers in path/otel-auto-instrumentation-python:

     Init Containers:opentelemetry-auto-instrumentation-python:...    Mounts:    /otel-auto-instrumentation-python from opentelemetry-auto-instrumentation-python (rw)Containers:python-app:...    Mounts:    /otel-auto-instrumentation-python from opentelemetry-auto-instrumentation-python (rw)...Volumes:...opentelemetry-auto-instrumentation-python:    Type:        EmptyDir (a temporary directory that shares a pod's lifetime)

   Make sure the environment variableOTEL_EXPORTER_OTLP_ENDPOINT points to a valid endpoint and there's network communication between the Pod and the endpoint.

5. Confirm data is flowing toKibana:

   • OpenObservability →Applications →Service inventory, and determine if:

     • The application appears in the list of services.
     • The application shows transactions and metrics.
     • Ifpython logs instrumentation is enabled, the application logs should appear in the Logs tab.

   • For application container logs, openKibana Discover and filter for your Pods' logs. In the provided example, we could filter for them with either of the following:

     • k8s.deployment.name: "python-app" (adapt the query filter to your use case)
     • k8s.pod.name: python-app* (adapt the query filter to your use case)

   Note that the container logs are not provided by the instrumentation library, but by the DaemonSet collector deployed as part of theoperator installation.

Refer totroubleshoot auto-instrumentation for further analysis.