Using OpenTelemetry to send traces to Jaeger V2

How to push trace events to Jaeger V2 distributed tracing platform using OpenTelemetry protocol.

Dapr supports writing traces using the OpenTelemetry (OTLP) protocol, and Jaeger V2 natively supports OTLP, allowing Dapr to send traces directly to a Jaeger V2 instance. This approach is recommended for production to leverage Jaeger V2’s capabilities for distributed tracing.

Configure Jaeger V2 in self-hosted mode

Local setup

The simplest way to start Jaeger is to run the pre-built, all-in-one Jaeger image published to DockerHub and expose the OTLP port:

docker run --rm --name jaeger \
  -p 16686:16686 \
  -p 4317:4317 \
  -p 4318:4318 \
  -p 5778:5778 \
  -p 9411:9411 \
  cr.jaegertracing.io/jaegertracing/jaeger:2.11.0

Next, create the following config.yaml file locally:

Note: Because you are using the Open Telemetry protocol to talk to Jaeger, you need to fill out the otel section of the tracing configuration and set the endpointAddress to the address of the Jaeger container.

apiVersion: dapr.io/v1alpha1
kind: Configuration
metadata:
  name: tracing
  namespace: default
spec:
  tracing:
    samplingRate: "1"
    stdout: true
    otel:
      endpointAddress: "localhost:4317"
      isSecure: false
      protocol: grpc

To launch the application referring to the new YAML configuration file, use the --config option. For example:

dapr run --app-id myapp --app-port 3000 node app.js --config config.yaml

View traces

To view traces in your browser, go to http://localhost:16686 to see the Jaeger UI.

Configure Jaeger V2 on Kubernetes

The following steps show you how to configure Dapr to send distributed tracing data directly to a Jaeger V2 instance deployed using the OpenTelemetry Operator with in-memory storage.

Prerequisites

Set up Jaeger V2 with the OpenTelemetry Operator

Jaeger V2 can be deployed using the OpenTelemetry Operator for simplified management and native OTLP support. The following example configures Jaeger V2 with in-memory storage.

Note on Storage Backends: This example uses in-memory storage (memstore) for simplicity, suitable for development or testing environments as it stores up to 100,000 traces in memory. For production environments, consider configuring a persistent storage backend like Cassandra or Elasticsearch to ensure trace data durability.

Installation

  1. Install cert-manager to manage certificates:

    kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.19.1/cert-manager.yaml -n cert-manager

    Verify that all resources in the cert-manager namespace are ready.

  2. Install the OpenTelemetry Operator:

    kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releases/latest/download/opentelemetry-operator.yaml

    Confirm that all resources in the opentelemetry-operator-system namespace are ready.

  3. Deploy a Jaeger V2 instance with in-memory storage: Apply the following configuration to create a Jaeger V2 instance:

    apiVersion: opentelemetry.io/v1beta1
kind: OpenTelemetryCollector
metadata:
  name: jaeger-inmemory-instance
  namespace: observability
spec:
  image: jaegertracing/jaeger:latest
  ports:
  - name: jaeger
    port: 16686
  config:
    service:
      extensions: [jaeger_storage, jaeger_query]
      pipelines:
        traces:
          receivers: [otlp]
          exporters: [jaeger_storage_exporter]
    extensions:
      jaeger_query:
        storage:
          traces: memstore
      jaeger_storage:
        backends:
          memstore:
            memory:
              max_traces: 100000
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
          http:
            endpoint: 0.0.0.0:4318
    exporters:
      jaeger_storage_exporter:
        trace_storage: memstore

    Apply it with:

    kubectl apply -f jaeger-inmemory.yaml -n observability

Set up Dapr to send traces to Jaeger V2

Create a Dapr configuration file to enable tracing and export the sidecar traces directly to the Jaeger V2 instance.

  1. Create a configuration file (e.g., tracing.yaml) with the following content, updating the namespace and otel.endpointAddress to match your Jaeger V2 instance:

    apiVersion: dapr.io/v1alpha1
kind: Configuration
metadata:
  name: tracing
  namespace: order-system
spec:
  tracing:
    samplingRate: "1"
    otel:
      endpointAddress: "jaeger-inmemory-instance-collector.observability.svc.cluster.local:4317"
      isSecure: false
      protocol: grpc

  2. Apply the configuration:

    kubectl apply -f tracing.yaml -n order-system

Deploy your app with tracing enabled

Apply the tracing Dapr configuration by adding a dapr.io/config annotation to the application deployment that you want to enable distributed tracing for, as shown in the following example:

apiVersion: apps/v1
kind: Deployment
metadata:
  ...
spec:
  ...
  template:
    metadata:
      ...
      annotations:
        dapr.io/enabled: "true"
        dapr.io/app-id: "MyApp"
        dapr.io/app-port: "8080"
        dapr.io/config: "tracing"

You can register multiple tracing exporters at the same time, and the tracing logs are forwarded to all registered exporters.

That’s it! There’s no need to include the OpenTelemetry SDK or instrument your application code. Dapr automatically handles the distributed tracing for you.

View traces

To view Dapr sidecar traces, port-forward the Jaeger V2 service and open the UI:

kubectl port-forward svc/jaeger-inmemory-instance-collector 16686 -n observability

In your browser, go to http://localhost:16686 to see the Jaeger V2 UI.

jaeger

References

Last modified October 22, 2025: feat Jaeger v2 (#4921) (8c5ab7d)