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Instrumenting Python applications with EDOT SDKs on Kubernetes

Elastic Stack Serverless Observability

Learn how to instrument Python applications on Kubernetes using the OpenTelemetry Operator, the Elastic Distribution of OpenTelemetry (EDOT) Collector, and the EDOT Python SDK.

The following environments and configurations are supported:

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 to Kibana Observability.

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

  1. Ensure you have successfully installed the OpenTelemetry Operator, and confirm that the following Instrumentation object exists in the system:

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

    If your Instrumentation 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 under spec.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 the auto-instrumentation resources are injected in the Pod:

    Run a kubectl describe of one of your application pods and check:

    • There should be an init container named opentelemetry-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, including PYTHONPATH:

      ...
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 an EmptyDir volume named opentelemetry-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 variable OTEL_EXPORTER_OTLP_ENDPOINT points to a valid endpoint and there's network communication between the Pod and the endpoint.

  5. Confirm data is flowing to Kibana:

    • Open Observability → Applications → Service inventory, and determine if:

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

    • For application container logs, open Kibana 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 the operator installation.

Refer to troubleshoot auto-instrumentation for further analysis.