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02-operator-introduction.md

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OpenTelemetry Operator introduction

This tutorial step focuses on the OpenTelemetry operator introduction.

What is Kubernetes operator

Kubernetes operator can:

  • Create CustomResouceDefinitions (CRD) in the cluster
  • Hide deployment complexity of the application
  • Support application upgrades (handles breaking changes, schema migrations)
  • Auto scale the application

What is OpenTelemetry operator

OpenTelemetry Kubernetes operator can:

  • Deploy and manage OpenTelemetry collector
  • Instrument workloads with OpenTelemetry auto-instrumentation/agents (see app instrumentation tutorial step). Supports Java, .Net, Node.JS and Python.
  • Read Prometheus podmonitor.monitoring.coreos.com and servicemonitor.monitoring.coreos.com and distribute scrape targets across deployed OpenTelemetry collectors (see metrics tutorial step)

It manages two CustomResourceDefinitions (CRDs):

  • opentelemetrycollectors.opentelemetry.io, short name otelcol
  • instrumentations.opentelemetry.io, short name otelinst

Deploy the operator

The operator installation consists of the operator Deployment, Service, ClusterRole, ClusterRoleBinding, CustomResourceDefinitions etc.

The operator can be deployed via:

The default operator installation uses cert-manager to provision certificates for the validating and mutating admission webhooks.

Deploy the operator to the local Kubernetes cluster

Deploy OpenTelemetry operator to the cluster:

kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.11.0/cert-manager.yaml
sleep 50 # wait until cert-manager is up and ready
kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releases/download/v0.74.0/opentelemetry-operator.yaml

Verify operator installation:

kubectl get pods -w -n opentelemetry-operator-system

OpenTelemetry collector CRD

Example OpenTelemetryCollector CR:

apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: otel
spec:
  image: ghcr.io/open-telemetry/opentelemetry-collector-releases/opentelemetry-collector-contrib:0.74.0
  mode: deployment # statefulset, daemonset, sidecar
  autoscaler:
    targetCPUUtilization: 90
    minReplicas: 1
    maxReplicas: 5
  ingress:
    hostname: ...
  config: | # contains OpenTelemetry collector configuration
    receivers:
      otlp:
        protocols:
          grpc:
          http:
    processors:
      batch:

    exporters:
      logging:

    service:
      pipelines:
        traces:
          receivers: [otlp]
          processors: [batch]
          exporters: [logging]

The sidecar can be injected to a pod by applying sidecar.opentelemetry.io/inject: "true" annotation to a pod spec.

Deploy collector

See the collector CR, and deploy it:

kubectl apply -f https://raw.githubusercontent.com/pavolloffay/kubecon-eu-2023-opentelemetry-kubernetes-tutorial/main/backend/02-collector.yaml

Verify the collector deployment:

kubectl get pods -n observability-backend -l app.kubernetes.io/component=opentelemetry-collector -w

The collector is by default configured to export data to the observability backed that was deployed in the prerequisites section, however, the configuration can be changed to export data to any other supported observability system.

The data to the collector can be pushed via otel-collector service in the observability-backend namespace. The full endpoint is otel-collector.observability-backend.svc.cluster.local:4317. The collector is by default configured to receive OpenTelemetry protocol (OTLP).

Change collector configuration

In this step we will change the collector config by adding the Jaeger receiver.

Let's get the currently created services and see what ports are exposed:

kubectl get svc otel-collector -n observability-backend -o yaml

Edit the CR:

kubectl edit opentelemetrycollectors.opentelemetry.io otel -n observability-backend

Let's add Jaeger receiver:

    receivers:
      jaeger:
        protocols:
          grpc:
          thrift_binary:
          thrift_compact:
          thrift_http:
        
    service:
      pipelines:
        traces:
          receivers: [otlp, jaeger]
          processors: [memory_limiter, batch]

The collector pod should be re-deployed and the otel-collector service should expose more ports:

kubectl get svc otel-collector -n observability-backend -o yaml
NAME                        TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)                                                             AGE
otel-collector              ClusterIP   10.217.4.201   <none>        14250/TCP,6832/UDP,6831/UDP,14268/TCP,4317/TCP,4318/TCP,55681/TCP   5m15s
otel-collector-headless     ClusterIP   None           <none>        14250/TCP,6832/UDP,6831/UDP,14268/TCP,4317/TCP,4318/TCP,55681/TCP   5m15s
otel-collector-monitoring   ClusterIP   10.217.4.207   <none>        8888/TCP

Instrumentation CRD

The operator use pod mutating webhook to inject auto-instrumentation libraries into starting pods. The webhook adds an init container that copies auto-instrumentation libraries into a volume that is mounted as well to the application container and it configures runtime (e.g. in JVM via JAVA_TOOL_OPTIONS) to load the libraries.

Example OpenTelemetry Instrumentation CR:

apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: instrumentation
spec:
  exporter:
    endpoint: http://otel-collector:4317
  propagators:
    - tracecontext
    - baggage
    - b3
  sampler:
    type: parentbased_traceidratio
    argument: "1"
  resource:
    addK8sUIDAttributes: true
    attributes: # Add user defined attributes
      env: production
  python:
    env:
      - name: OTEL_EXPORTER_OTLP_ENDPOINT
        value: http://otel-collector:4318
  dotnet:
    env:
      - name: OTEL_EXPORTER_OTLP_ENDPOINT
        value: http://otel-collector:4318

Then use annotation on a pod spec to enable the injection e.g. instrumentation.opentelemetry.io/inject-java: "true"

We will create the Instrumentation resource in the next tutorial step.

Next steps