devops

# DevOps Skill

Comprehensive guide for deploying and managing cloud infrastructure across Cloudflare edge platform, Docker containerization, and Google Cloud Platform.

## When to Use This Skill

Use this skill when:
- Deploying serverless applications to Cloudflare Workers
- Containerizing applications with Docker
- Managing Google Cloud infrastructure with gcloud CLI
- Setting up CI/CD pipelines across platforms
- Optimizing cloud infrastructure costs
- Implementing multi-region deployments
- Building edge-first architectures
- Managing container orchestration with Kubernetes
- Configuring cloud storage solutions (R2, Cloud Storage)
- Automating infrastructure with scripts and IaC

## Platform Selection Guide

### When to Use Cloudflare

**Best For:**
- Edge-first applications with global distribution
- Ultra-low latency requirements (<50ms)
- Static sites with serverless functions
- Zero egress cost scenarios (R2 storage)
- WebSocket/real-time applications (Durable Objects)
- AI/ML at the edge (Workers AI)

**Key Products:**
- Workers (serverless functions)
- R2 (object storage, S3-compatible)
- D1 (SQLite database with global replication)
- KV (key-value store)
- Pages (static hosting + functions)
- Durable Objects (stateful compute)
- Browser Rendering (headless browser automation)

**Cost Profile:** Pay-per-request, generous free tier, zero egress fees

### When to Use Docker

**Best For:**
- Local development consistency
- Microservices architectures
- Multi-language stack applications
- Traditional VPS/VM deployments
- Kubernetes orchestration
- CI/CD build environments
- Database containerization (dev/test)

**Key Capabilities:**
- Application isolation and portability
- Multi-stage builds for optimization
- Docker Compose for multi-container apps
- Volume management for data persistence
- Network configuration and service discovery
- Cross-platform compatibility (amd64, arm64)

**Cost Profile:** Infrastructure cost only (compute + storage)

### When to Use Google Cloud

**Best For:**
- Enterprise-scale applications
- Data analytics and ML pipelines (BigQuery, Vertex AI)
- Hybrid/multi-cloud deployments
- Kubernetes at scale (GKE)
- Managed databases (Cloud SQL, Firestore, Spanner)
- Complex IAM and compliance requirements

**Key Services:**
- Compute Engine (VMs)
- GKE (managed Kubernetes)
- Cloud Run (containerized serverless)
- App Engine (PaaS)
- Cloud Storage (object storage)
- Cloud SQL (managed databases)

**Cost Profile:** Varied pricing, sustained use discounts, committed use contracts

## Quick Start

### Cloudflare Workers

```bash
# Install Wrangler CLI
npm install -g wrangler

# Create and deploy Worker
wrangler init my-worker
cd my-worker
wrangler deploy
```

See: `references/cloudflare-workers-basics.md`

### Docker Container

```bash
# Create Dockerfile
cat > Dockerfile <<EOF
FROM node:20-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --production
COPY . .
EXPOSE 3000
CMD ["node", "server.js"]
EOF

# Build and run
docker build -t myapp .
docker run -p 3000:3000 myapp
```

See: `references/docker-basics.md`

### Google Cloud Deployment

```bash
# Install and authenticate
curl https://sdk.cloud.google.com | bash
gcloud init
gcloud auth login

# Deploy to Cloud Run
gcloud run deploy my-service \
  --image gcr.io/project/image \
  --region us-central1
```

See: `references/gcloud-platform.md`

## Reference Navigation

### Cloudflare Platform
- `cloudflare-platform.md` - Edge computing overview, key components
- `cloudflare-workers-basics.md` - Getting started, handler types, basic patterns
- `cloudflare-workers-advanced.md` - Advanced patterns, performance, optimization
- `cloudflare-workers-apis.md` - Runtime APIs, bindings, integrations
- `cloudflare-r2-storage.md` - R2 object storage, S3 compatibility, best practices
- `cloudflare-d1-kv.md` - D1 SQLite database, KV store, use cases
- `browser-rendering.md` - Puppeteer/Playwright automation on Cloudflare

### Docker Containerization
- `docker-basics.md` - Core concepts, Dockerfile, images, containers
- `docker-compose.md` - Multi-container apps, networking, volumes

### Google Cloud Platform
- `gcloud-platform.md` - GCP overview, gcloud CLI, authentication
- `gcloud-services.md` - Compute Engine, GKE, Cloud Run, App Engine

### Python Utilities
- `scripts/cloudflare-deploy.py` - Automate Cloudflare Worker deployments
- `scripts/docker-optimize.py` - Analyze and optimize Dockerfiles

## Common Workflows

### Edge + Container Hybrid

```yaml
# Cloudflare Workers (API Gateway)
# -> Docker containers on Cloud Run (Backend Services)
# -> R2 (Object Storage)

# Benefits:
# - Edge caching and routing
# - Containerized business logic
# - Global distribution
```

### Multi-Stage Docker Build

```dockerfile
# Build stage
FROM node:20-alpine AS build
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

# Production stage
FROM node:20-alpine
WORKDIR /app
COPY --from=build /app/dist ./dist
COPY --from=build /app/node_modules ./node_modules
USER node
CMD ["node", "dist/server.js"]
```

### CI/CD Pipeline Pattern

```yaml
# 1. Build: Docker multi-stage build
# 2. Test: Run tests in container
# 3. Push: Push to registry (GCR, Docker Hub)
# 4. Deploy: Deploy to Cloudflare Workers / Cloud Run
# 5. Verify: Health checks and smoke tests
```

## Best Practices

### Security
- Run containers as non-root user
- Use service account impersonation (GCP)
- Store secrets in environment variables, not code
- Scan images for vulnerabilities (Docker Scout)
- Use API tokens with minimal permissions

### Performance
- Multi-stage Docker builds to reduce image size
- Edge caching with Cloudflare KV
- Use R2 for zero egress cost storage
- Implement health checks for containers
- Set appropriate timeouts and resource limits

### Cost Optimization
- Use Cloudflare R2 instead of S3 for large egress
- Implement caching strategies (edge + KV)
- Right-size container resources
- Use sustained use discounts (GCP)
- Monitor usage with cloud provider dashboards

### Development
- Use Docker Compose for local development
- Wrangler dev for local Worker testing
- Named gcloud configurations for multi-environment
- Version control infrastructure code
- Implement automated testing in CI/CD

## Decision Matrix

| Need | Choose |
|------|--------|
| Sub-50ms latency globally | Cloudflare Workers |
| Large file storage (zero egress) | Cloudflare R2 |
| SQL database (global reads) | Cloudflare D1 |
| Containerized workloads | Docker + Cloud Run/GKE |
| Enterprise Kubernetes | GKE |
| Managed relational DB | Cloud SQL |
| Static site + API | Cloudflare Pages |
| WebSocket/real-time | Cloudflare Durable Objects |
| ML/AI pipelines | GCP Vertex AI |
| Browser automation | Cloudflare Browser Rendering |

## Resources

- **Cloudflare Docs:** https://developers.cloudflare.com
- **Docker Docs:** https://docs.docker.com
- **GCP Docs:** https://cloud.google.com/docs
- **Wrangler CLI:** https://developers.cloudflare.com/workers/wrangler/
- **gcloud CLI:** https://cloud.google.com/sdk/gcloud

## Implementation Checklist

### Cloudflare Workers
- [ ] Install Wrangler CLI
- [ ] Create Worker project
- [ ] Configure wrangler.toml (bindings, routes)
- [ ] Test locally with `wrangler dev`
- [ ] Deploy with `wrangler deploy`

### Docker
- [ ] Write Dockerfile with multi-stage builds
- [ ] Create .dockerignore file
- [ ] Test build locally
- [ ] Push to registry
- [ ] Deploy to target platform

### Google Cloud
- [ ] Install gcloud CLI
- [ ] Authenticate with service account
- [ ] Create project and enable APIs
- [ ] Configure IAM permissions
- [ ] Deploy and monitor resources