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Kartik Dudeja
Kartik Dudeja

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OpenTelemetry in Action on Kubernetes: Part 3 - Deploying the Application on Kubernetes

#opentelemetry #kubernetes #observability

Deploying Our Instrumented ML App to Kubernetes

Welcome to Part 3! If you’ve followed along so far, by the end of Part 2 you had:

  • A FastAPI-based machine learning app
  • Instrumented with OpenTelemetry for full-stack observability
  • Dockerized and ready to ship

Now, it's time to bring in the big orchestration guns — Kubernetes.

OTel-k8s

Understanding Kubernetes Deployment & Service

Before we throw YAML at a cluster, let’s understand what these two crucial building blocks do:

Deployment

A Deployment in Kubernetes manages a set of replicas (identical Pods running our app). It provides:

  • Declarative updates: You describe what you want, K8s makes it so.
  • Rolling updates: Smooth upgrades without downtime.
  • Self-healing: If a Pod dies, K8s spins up a new one.

Think of it as a smart manager for your app's pods.

Service

A Service exposes your app inside the cluster (or externally, if needed). It:

  • Provides a stable DNS name.
  • Load balances traffic between pods.

  • In our case, exposes:

    • Port 80 → App port 8000 (FastAPI HTTP)
    • Port 4317 → OTLP gRPC (Telemetry)

Kubernetes Manifest Breakdown

Let’s break down the configuration:

Deployment: house-price-service 

apiVersion: apps/v1
kind: Deployment
metadata:
  name: house-price-service
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We declare a Deployment that manages our app.

spec:
  replicas: 2
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We want 2 replicas of our app running — high availability for the win.

  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 25%
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Kubernetes will update pods gracefully. It allows some extra pods during rollout and ensures some stay alive.

      containers:
        - name: app
          image: house-price-predictor:v2
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We use the Docker image built in Part 2, deployed as a container.

          ports:
            - containerPort: 8000   # App port
            - containerPort: 4317   # OTLP telemetry port
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Complete Deployment Manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: house-price-service
  labels:
    app: house-price-service

spec:
  replicas: 2

  revisionHistoryLimit: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%           # Allow 25% more pods than desired during update
      maxUnavailable: 25%     # Allow 25% of desired pods to be unavailable during update

  selector:
    matchLabels:
      app: house-price-service
  template:
    metadata:
      labels:
        app: house-price-service

    spec:
      containers:
        - name: app
          image: house-price-predictor:v2
          imagePullPolicy: IfNotPresent
          resources:
            requests:
              cpu: "10m"
              memory: "128Mi"
            limits:
              cpu: "20m"
              memory: "256Mi"
          ports:
            - containerPort: 8000   # Application Port
            - containerPort: 4317   # OTLP gRPC Port
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Service: house-price-service 

apiVersion: v1
kind: Service
metadata:
  name: house-price-service
  labels:
    app: house-price-service
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This ClusterIP Service lets other K8s workloads communicate with our app.

  ports:
    - port: 80
      targetPort: 8000
    - port: 4317
      targetPort: 4317
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The Service maps:

  • Port 80 → App HTTP server
  • Port 4317 → For OTLP spans, metrics, logs

Complete Service Manifest File:

apiVersion: v1
kind: Service
metadata:
  name: house-price-service
  labels:
    app: house-price-service  
spec:
  selector:
    app: house-price-service
  ports:
    - name: http
      protocol: TCP
      port: 80
      targetPort: 8000
    - name: otlp-grpc
      protocol: TCP
      port: 4317
      targetPort: 4317
  type: ClusterIP
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Add both in the one file: house-price-app.yaml

Deploying with kubectl

Before deploying the app, let's create a Kubernetes namespace. This helps group related resources together.

kubectl create namespace mlapp
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Run the following to deploy your app:

kubectl -n mlapp apply -f house-price-app.yaml
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To check the deployment status:

kubectl -n mlapp get deployments
kubectl -n mlapp get pods
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mlapp-k8s-get-deploy-pods

To see pod logs (structured JSON + OpenTelemetry info):

kubectl -n mlapp logs -f -l app=house-price-service
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To view the exposed service:

kubectl -n mlapp get svc -l app=house-price-service
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Testing the App in Kubernetes

Get the Endpoint IP from the K8s service:

API_ENDPOINT_IP=$(kubectl -n mlapp get svc -l app=house-price-service -o json | jq -r '.items[].spec.clusterIP')
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Test it locally using curl or Postman:

curl -X POST "http://${API_ENDPOINT_IP}:80/predict/" \
  -H "Content-Type: application/json" \
  -d '{"features": [1200]}'
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You should get a prediction response like:

{"predicted_price": 170000.0}
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And voilà — telemetry data is flowing.

What’s Next: Meet the OpenTelemetry Collector

In Part 4, we’ll introduce the OpenTelemetry Collector Agent:

  • Deploy it as a DaemonSet alongside your app
  • Configure it to collect traces, metrics, and logs
  • Route the data to a gateway, and onward to backends like Prometheus, Jaeger, and Loki

TL;DR: It’s where the real observability magic begins.

{
    "author"   :  "Kartik Dudeja",
    "email"    :  "kartikdudeja21@gmail.com",
    "linkedin" :  "https://linkedin.com/in/kartik-dudeja",
    "github"   :  "https://github.com/Kartikdudeja"
}
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