devops

name	devops
description	Deploy and manage cloud infrastructure on Cloudflare (Workers, R2, D1, KV, Pages, Durable Objects, Browser Rendering), Docker containers, and Google Cloud Platform (Compute Engine, GKE, Cloud Run, App Engine, Cloud Storage). Use when deploying serverless functions to the edge, configuring edge computing solutions, managing Docker containers and images, setting up CI/CD pipelines, optimizing cloud infrastructure costs, implementing global caching strategies, working with cloud databases, or building cloud-native applications.
license	MIT
version	1.0.0

DevOps Skill

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

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

# 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

# Create Dockerfile cat > Dockerfile <

Build and run

docker build -t myapp . docker run -p 3000:3000 myapp

See: references/docker-basics.md

Google Cloud Deployment

# 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

# 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

# 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

# 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

Never commit secrets, API keys, or credentials to version control Always run containers as non-root user in production Scan container images for vulnerabilities before deployment Implement health checks for all containerized services Use multi-stage Docker builds to minimize image size Set resource limits (CPU, memory) for all containers

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