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In this tutorial we are going to use Odigos for generating distributed traces for a microservices-based application written in Go, Java, Python, .NET and Node.js. We are going to deploy the application in a Kubernetes cluster and use Jaeger as the backend for storing and visualizing the traces.
This guide is designed for:
  • IT professionals with experience in system administration or DevOps
  • Software engineers familiar with containerization and Kubernetes concepts
  • Anyone comfortable with command-line interfaces and basic cloud infrastructure
If you’re new to these concepts or prefer a more detailed, step-by-step approach, we recommend starting with our Beginner’s Guide to Odigos Installation.

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Preparing the environment

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Creating a Kubernetes cluster

This first step is to create a Kubernetes cluster. We recommend using kind or minikube for trying Odigos out in a local environment.
Mac users: please avoid using Docker Desktop built-in Kubernetes cluster as it does not support bind propagation.
Create a new local Kubernetes cluster, by running the following command:
  • Kind
  • Minikube
kind create cluster

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Deploying the target application

We are going to install a demo application that consists of 6 microservices written in Java, Go, Python, Node.js, .NET and PHP.
Architecture of demo application
Deploy the application using the following command: 
kubectl apply -f https://raw.githubusercontent.com/odigos-io/simple-demo/main/kubernetes/deployment.yaml

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Deploying Jaeger

Jaeger is a popular open source distributed tracing backend. We are going to use it for storing and visualizing the traces generated by Odigos. Install Jaeger using the following command: 
kubectl apply -f https://raw.githubusercontent.com/odigos-io/simple-demo/main/kubernetes/jaeger.yaml

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Waiting for the applications to start

Before moving to the next step, make sure that the application pods and Jaeger pods are running, this may take a few minutes. 
kubectl wait --for=condition=available --timeout=300s deployment --all --all-namespaces
Deployments Ready
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