spec/Google Cloud project at 1f3647d3ec395c1696f14aa19f6331be7b534ee8 · devcontainers/spec · GitHub

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I'll help you build and deploy an application to Google Kubernetes Engine (GKE). Here's a complete step-by-step guide:

## Prerequisites

1. **Google Cloud Account** with billing enabled
2. **Google Cloud SDK** installed
3. **Docker** installed locally
4. **kubectl** installed

## Step 1: Set Up Your Project

```bash
# Set your Google Cloud project
gcloud config set project YOUR_PROJECT_ID

# Enable required APIs
gcloud services enable container.googleapis.com
gcloud services enable containerregistry.googleapis.com
```

## Step 2: Create a Simple Application

Create a basic Node.js application:

**package.json:**
```json
{
  "name": "gke-app",
  "version": "1.0.0",
  "description": "Sample app for GKE deployment",
  "main": "server.js",
  "scripts": {
    "start": "node server.js"
  },
  "dependencies": {
    "express": "^4.18.2"
  }
}
```

**server.js:**
```javascript
const express = require('express');
const app = express();
const PORT = process.env.PORT || 8080;

app.get('/', (req, res) => {
  res.send(`
    <html>
      <body>
        <h1>Hello from GKE!</h1>
        <p>Server time: ${new Date().toISOString()}</p>
        <p>Host: ${process.env.HOSTNAME || 'unknown'}</p>
      </body>
    </html>
  `);
});

app.get('/health', (req, res) => {
  res.json({ status: 'healthy', timestamp: new Date().toISOString() });
});

app.listen(PORT, () => {
  console.log(`Server running on port ${PORT}`);
});
```

## Step 3: Create Dockerfile

**Dockerfile:**
```dockerfile
# Use the official Node.js runtime as base image
FROM node:18-alpine

# Set working directory in container
WORKDIR /app

# Copy package files
COPY package*.json ./

# Install dependencies
RUN npm install --production

# Copy application code
COPY . .

# Expose port
EXPOSE 8080

# Start the application
CMD ["npm", "start"]
```

**.dockerignore:**
```
node_modules
npm-debug.log
.git
.gitignore
README.md
Dockerfile
.dockerignore
```

## Step 4: Build and Test Docker Image Locally

```bash
# Build the Docker image
docker build -t my-node-app .

# Test locally
docker run -p 8080:8080 my-node-app

# Visit http://localhost:8080 to verify it works
```

## Step 5: Push to Google Container Registry (GCR)

```bash
# Configure Docker to use gcloud as credential helper
gcloud auth configure-docker

# Tag the image for GCR
docker tag my-node-app gcr.io/YOUR_PROJECT_ID/my-node-app:v1.0

# Push to GCR
docker push gcr.io/YOUR_PROJECT_ID/my-node-app:v1.0
```

## Step 6: Create a GKE Cluster

```bash
# Create a cluster
gcloud container clusters create my-gke-cluster \
  --num-nodes=2 \
  --zone=us-central1-a \
  --machine-type=e2-small

# Get cluster credentials
gcloud container clusters get-credentials my-gke-cluster \
  --zone=us-central1-a
```

## Step 7: Create Kubernetes Deployment Files

**deployment.yaml:**
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-node-app
  labels:
    app: my-node-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-node-app
  template:
    metadata:
      labels:
        app: my-node-app
    spec:
      containers:
      - name: my-node-app
        image: gcr.io/YOUR_PROJECT_ID/my-node-app:v1.0
        ports:
        - containerPort: 8080
        env:
        - name: NODE_ENV
          value: "production"
        resources:
          requests:
            memory: "128Mi"
            cpu: "100m"
          limits:
            memory: "256Mi"
            cpu: "200m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 10
```

**service.yaml:**
```yaml
apiVersion: v1
kind: Service
metadata:
  name: my-node-app-service
  labels:
    app: my-node-app
spec:
  type: LoadBalancer
  selector:
    app: my-node-app
  ports:
  - port: 80
    targetPort: 8080
    protocol: TCP
```

## Step 8: Deploy to GKE

```bash
# Apply the deployment
kubectl apply -f deployment.yaml

# Apply the service
kubectl apply -f service.yaml

# Check deployment status
kubectl get deployments

# Check pods
kubectl get pods

# Check service (get external IP)
kubectl get services
```

## Step 9: Verify Deployment

```bash
# Get the external IP
kubectl get service my-node-app-service

# Test the application (replace EXTERNAL_IP with actual IP)
curl http://EXTERNAL_IP

# View pod logs
kubectl logs -l app=my-node-app --tail=50
```

## Step 10: Scaling and Updates

```bash
# Scale the deployment
kubectl scale deployment my-node-app --replicas=5

# Update the application (after building new image)
docker build -t gcr.io/YOUR_PROJECT_ID/my-node-app:v2.0 .
docker push gcr.io/YOUR_PROJECT_ID/my-node-app:v2.0
kubectl set image deployment/my-node-app my-node-app=gcr.io/YOUR_PROJECT_ID/my-node-app:v2.0

# Rollback if needed
kubectl rollout undo deployment/my-node-app
```

## Step 11: Clean Up

```bash
# Delete the deployment and service
kubectl delete -f deployment.yaml
kubectl delete -f service.yaml

# Delete the cluster
gcloud container clusters delete my-gke-cluster --zone=us-central1-a

# Delete the container image
gcloud container images delete gcr.io/YOUR_PROJECT_ID/my-node-app:v1.0 --force-delete-tags
```

## Automation Script

Create a **deploy.sh** script for automation:

```bash
#!/bin/bash
PROJECT_ID="YOUR_PROJECT_ID"
IMAGE_NAME="my-node-app"
VERSION="v1.0"
CLUSTER_NAME="my-gke-cluster"
ZONE="us-central1-a"

echo "Building Docker image..."
docker build -t $IMAGE_NAME .

echo "Tagging image for GCR..."
docker tag $IMAGE_NAME gcr.io/$PROJECT_ID/$IMAGE_NAME:$VERSION

echo "Pushing to GCR..."
docker push gcr.io/$PROJECT_ID/$IMAGE_NAME:$VERSION

echo "Deploying to GKE..."
gcloud container clusters get-credentials $CLUSTER_NAME --zone $ZONE
kubectl apply -f deployment.yaml
kubectl apply -f service.yaml

echo "Deployment complete!"
kubectl get services
```

Make it executable:
```bash
chmod +x deploy.sh
```

This complete workflow shows you how to containerize an application, push it to Google Container Registry, and deploy it to a GKE cluster with proper configuration for production use.