This doc covers the steps on how to deploy an OpenTelemetry collector on Kubernetes. We will then use an OTEL instrumented (Go) application provided by OpenTelemetry to send traces to the Collector. From there, we will bring the trace data to a Jaeger collector. Finally, the traces will be visualised using the Jaeger UI.

This image shows the flow between the application, OpenTelemetry collector and Jaeger.

This OpenTelemetry repository provides a complete demo on how you can deploy OpenTelemetry on Kubernetes, we can use this as a starting point.


To start off, we need a Kubernetes cluster you can use any of your existing Kubernetes clusters that has got the apx 2vCPUs, 4GB RAM, and 100GB Storage.

Local Kubernetes Cluster Setup

Skip this in case you have the existing cluster.

In case, you don't have the ready Kubernetes but you have a good local machine with at least 4GB RAM left, you can use a local instance of Kind. The application will access this Kubernetes cluster through a NodePort (on port 30080). So make sure this port is free.

conn, err := grpc.DialContext(ctx, "localhost:30080", grpc.WithTransportCredentials(insecure.NewCredentials()), grpc.WithBlock())

To use NodePort with Kind, we need to first enable it.

Extra port mappings can be used to port forward to the kind nodes. This is a cross-platform option to get traffic into your kind cluster.

vim kind-config.yaml

kind: ClusterapiVersion: role: control-plane  # port forward 30080 on the host to 30080 on this node  extraPortMappings:  - containerPort: 30080    hostPort: 30080- role: worker

Create the cluster with: kind create cluster --config kind-config.yaml

Creating cluster "kind" ... โœ“ Ensuring node image (kindest/node:v1.24.0) ๐Ÿ–ผ โœ“ Preparing nodes ๐Ÿ“ฆ ๐Ÿ“ฆ โœ“ Writing configuration ๐Ÿ“œ โœ“ Starting control-plane ๐Ÿ•น๏ธ โœ“ Installing CNI ๐Ÿ”Œ โœ“ Installing StorageClass ๐Ÿ’พ โœ“ Joining worker nodes ๐ŸšœSet kubectl context to "kind-kind"You can now use your cluster with:kubectl cluster-info --context kind-kindThanks for using kind! ๐Ÿ˜Š

Once our Kubernetes cluster is up, we can start deploying Jaeger.

What is Jaeger?

Jaeger is an open-source distributed tracing system for tracing transactions between distributed services. Itโ€™s used for monitoring and troubleshooting complex microservices environments. By doing this, we can view traces and analyse the applicationโ€™s behaviour.

Why do we need it?

Using a tracing system (like Jaeger) is especially important in microservices environments since they are considered a lot more difficult to debug than a single monolithic application.

Problems that Jaeger addresses?

  • Distributed tracing monitoring

  • Performance and latency optimisation

  • Root cause analysis

  • Service dependency analysis

Deploy Jaeger

To deploy Jaeger on the Kubernetes cluster, we can make use of the Jaeger operator.

Operators are pieces of software that ease the operational complexity of running another piece of software.

Deploy Jaeger Operator

You first install the Jaeger Operator on Kubernetes. This operator will then watch for new Jaeger custom resources (CR).

There are different ways of installing the Jaeger Operator on Kubernetes:

  • using Helm

  • using Deployment files

Before you start, pay attention to the Prerequisite section.

Since version 1.31 the Jaeger Operator uses webhooks to validate Jaeger custom resources (CRs). This requires an installed version of the cert-manager.

Installing Cert-Manager

cert-manager is a powerful and extensible X.509 certificate controller for Kubernetes and OpenShift workloads. It will obtain certificates from a variety of Issuers, both popular public Issuers as well as private Issuers, and ensure the certificates are valid and up-to-date, and will attempt to renew certificates at a configured time before expiry.

Installation of cert-manager of is very simple, just run:

kubectl apply -f

By default, cert-manager will be installed into the cert-manager namespace.

You can verify the installation by following the instructions here

With cert-manager installed, letโ€™s continue with the deployment of Jaeger

Installing Jaeger Operator Using Helm

Jump over to Artifact Hub and search for jaeger-operator

Add the Jaeger Tracing Helm repository:

helm repo add jaegertracing

To install the chart with the release name my-release (in the default namespace)

helm install my-release jaegertracing/jaeger-operator

You can also install a specific version of the helm chart:

helm install my-release jaegertracing/jaeger-operator --version 2.25.0

Verify that itโ€™s installed on Kubernetes:

helm list -A

You can also deploy the Jaeger operator using deployment files.

kubectl create -f

At this point, there should be a jaeger-operator deployment available.

kubectl get deployment my-jaeger-operator

NAME                 READY   UP-TO-DATE   AVAILABLE   AGEmy-jaeger-operator   1/1     1            1           2m58s

The operator is now ready to create Jaeger instances.

Deploy Jaeger All-in-One

The operator that we just installed doesnโ€™t do anything itself, it just means that we can create jaeger resources/instances that we want the jaeger operator to manage.

The simplest possible way to create a Jaeger instance is by deploying the All-in-one strategy, which installs the all-in-one image, and includes the agents, collector, query and the Jaeger UI in a single pod using in-memory storage.

Create a yaml file like the following. The name of the Jaeger instance will be simplest

vim simplest.yaml

apiVersion: Jaegermetadata:  name: simplest

kubectl apply -f simplest.yaml

After a little while, a new in-memory all-in-one instance of Jaeger will be available, suitable for quick demos and development purposes.

When the Jaeger instance is up and running, we can check the pods and services.

kubectl get pods

NAME                     READY STATUS    RESTARTS   AGEsimplest-656d7cf5c8-lff7b 1/1  Running   0          3m55s

kubectl get services

To get the pod name, query for the pods belonging to the simplest Jaeger instance:

Query the logs from the pod:

kubectl logs -l

{"level":"info","ts":1660155049.86027,"caller":"channelz/logging.go:50","msg":"[core]Channel Connectivity change to READY","system":"grpc","grpc_log":true}{"level":"info","ts":1660155049.8612773,"caller":"grpc/builder.go:120","msg":"Agent collector connection state change","dialTarget":":14250","status":"READY"}{"level":"info","ts":1660155049.8617437,"caller":"app/server.go:241","msg":"Starting HTTP server","port":16686,"addr":":16686"}{"level":"info","ts":1660155049.8621716,"caller":"app/server.go:260","msg":"Starting GRPC server","port":16685,"addr":":16685"}

Letโ€™s open the Jaeger UI

Use port-forwarding to access the Jaeger UI

kubectl port-forward svc/simplest-query 16686:16686

Forwarding from -> 16686Forwarding from [::1]:16686 -> 16686

Jaeger UI

Deploy Open Telemetry Collector

To deploy the OpenTelemetry collector, we will use this otel-collector.yaml file as a starting point. The yaml file consists of a ConfigMap, Service and a Deployment.

vim otel-collector.yaml

Make sure to change the name of the jaeger collector (exporter) to match the one we deployed above. In our case, that would be:

exporters:      jaeger:        endpoint: "simplest-collector.default.svc.cluster.local:14250"

Also, pay attention to receivers. This part creates the receiver on the Collector side and opens up the port 4317 for receiving traces, which enables the application to send data to the OpenTelemetry Collector.

...  otel-collector-config: |    receivers:      otlp:        protocols:          grpc:            endpoint: ""...

Apply the file with: kubectl apply -f otel-collector.yaml

configmap/otel-collector-conf createdservice/otel-collector createddeployment.apps/otel-collector created

Verify that the OpenTelemetry Collector is up and running.

kubectl get deployment

kubectl logs deployment/otel-collector

"Everything is ready. Begin running and processing data."

Run Application

Time to send some trace data to our OpenTelemetry collector.

Remember, that the application access the Kubernetes cluster through a NodePort on port 30080. The Kubernetes service will bind the 4317 port used to access the OTLP receiver to port 30080 on the Kubernetes node.

By doing so, it makes it possible for us to access the Collector by using the static address <node-ip>:30080. In case you are running a local cluster, this will be localhost:30080. Source

This repository contains an (SDK) instrumented application written in Go, that simulates an application.

go run main.go

2022/08/10 20:31:37 Waiting for connection...2022/08/10 20:31:37 Doing really hard work (1 / 10)2022/08/10 20:31:38 Doing really hard work (2 / 10)2022/08/10 20:31:39 Doing really hard work (3 / 10)2022/08/10 20:31:40 Doing really hard work (4 / 10)2022/08/10 20:31:41 Doing really hard work (5 / 10)2022/08/10 20:31:42 Doing really hard work (6 / 10)2022/08/10 20:31:43 Doing really hard work (7 / 10)2022/08/10 20:31:44 Doing really hard work (8 / 10)2022/08/10 20:31:45 Doing really hard work (9 / 10)2022/08/10 20:31:46 Doing really hard work (10 / 10)2022/08/10 20:31:47 Done!

Viewing the data

Letโ€™s check out the telemetry data generated by our sample application

Again, we can use port-forwarding to access Jaeger UI.

kubectl port-forward svc/simplest-query 16686:16686

Open the web-browser and go to

Under Service select test-service to view the generated traces.

The service name is specified in the main.go file.

res, err := resource.New(ctx,  resource.WithAttributes(   // the service name used to display traces in backends   semconv.ServiceNameKey.String("test-service"),  ),

The application will access this Kubernetes cluster through a NodePort (on port 30080). The URL is specified here:

conn, err := grpc.DialContext(ctx, "localhost:30080", grpc.WithTransportCredentials(insecure.NewCredentials()), grpc.WithBlock()) if err != nil {  return nil, fmt.Errorf("failed to create gRPC connection to collector: %w", err) }


This document has covered how we deploy an OpenTelemetry collector on Kubernetes. Then we sent trace data to this collector using an Otel SDK instrumented application written in Go. From there, the traces were sent to a Jaeger collector and visualised in Jaeger UI.

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