7.1 KiB
7.1 KiB
Cloudflare Platform Overview
Cloudflare Developer Platform: comprehensive edge computing ecosystem for full-stack applications on global network across 300+ cities.
Core Concepts
Edge Computing Model
Global Network:
- Code runs on servers in 300+ cities globally
- Requests execute from nearest location
- Ultra-low latency (<50ms typical)
- Automatic failover and redundancy
V8 Isolates:
- Lightweight execution environments (faster than containers)
- Millisecond cold starts
- Zero infrastructure management
- Automatic scaling
- Pay-per-request pricing
Key Components
Workers - Serverless functions on edge
- HTTP/scheduled/queue/email handlers
- JavaScript/TypeScript/Python/Rust support
- Max 50ms CPU (free), 30s (paid)
- 128MB memory limit
D1 - SQLite database with global read replication
- Standard SQLite syntax
- Single-writer consistency
- Global read replication
- 25GB database size limit
- Batch operations for transactions
KV - Distributed key-value store
- Sub-millisecond reads (edge-cached)
- Eventual consistency (~60s globally)
- 25MB value size limit
- Automatic TTL expiration
- Best for: cache, sessions, feature flags
R2 - Object storage (S3-compatible)
- Zero egress fees (huge cost advantage)
- Unlimited storage
- 5TB object size limit
- S3-compatible API
- Multipart upload support
Durable Objects - Stateful compute with WebSockets
- Single-instance coordination (strong consistency)
- Persistent storage (1GB limit paid)
- WebSocket support
- Automatic hibernation
Queues - Message queue system
- At-least-once delivery
- Automatic retries (exponential backoff)
- Dead-letter queue support
- Batch processing
Pages - Static site hosting + serverless functions
- Git integration (auto-deploy)
- Directory-based routing
- Framework support (Next.js, Remix, Astro, SvelteKit)
- Built-in preview deployments
Workers AI - Run AI models on edge
- LLMs (Llama 3, Mistral, Gemma, Qwen)
- Image generation (Stable Diffusion, DALL-E)
- Embeddings (BGE, GTE)
- Speech recognition (Whisper)
- No GPU management required
Browser Rendering - Headless browser automation
- Puppeteer/Playwright support
- Screenshots, PDFs, web scraping
- Session reuse for cost optimization
- MCP server support for AI agents
Architecture Patterns
Full-Stack Application
┌─────────────────────────────────────────┐
│ Cloudflare Pages (Frontend) │
│ Next.js / Remix / Astro │
└──────────────────┬──────────────────────┘
│
┌──────────────────▼──────────────────────┐
│ Workers (API Layer) │
│ - Routing │
│ - Authentication │
│ - Business logic │
└─┬──────┬──────┬──────┬──────┬───────────┘
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
┌────┐ ┌────┐ ┌────┐ ┌────┐ ┌────────────┐
│ D1 │ │ KV │ │ R2 │ │ DO │ │ Workers AI │
└────┘ └────┘ └────┘ └────┘ └────────────┘
Polyglot Storage Pattern
export default {
async fetch(request: Request, env: Env) {
// KV: Fast cache
const cached = await env.KV.get(key);
if (cached) return new Response(cached);
// D1: Structured data
const user = await env.DB.prepare(
"SELECT * FROM users WHERE id = ?"
).bind(userId).first();
// R2: Media files
const avatar = await env.R2_BUCKET.get(`avatars/${user.id}.jpg`);
// Durable Objects: Real-time
const chat = env.CHAT_ROOM.get(env.CHAT_ROOM.idFromName(roomId));
// Queue: Async processing
await env.EMAIL_QUEUE.send({ to: user.email, template: 'welcome' });
return new Response(JSON.stringify({ user }));
}
};
Wrangler CLI Essentials
Installation
npm install -g wrangler
wrangler login
wrangler init my-worker
Core Commands
# Development
wrangler dev # Local dev server
wrangler dev --remote # Dev on real edge
# Deployment
wrangler deploy # Deploy to production
wrangler deploy --dry-run # Preview changes
# Logs
wrangler tail # Real-time logs
wrangler tail --format pretty # Formatted logs
# Versions
wrangler deployments list # List deployments
wrangler rollback [version] # Rollback
# Secrets
wrangler secret put SECRET_NAME
wrangler secret list
Resource Management
# D1
wrangler d1 create my-db
wrangler d1 execute my-db --file=schema.sql
# KV
wrangler kv:namespace create MY_KV
wrangler kv:key put --binding=MY_KV "key" "value"
# R2
wrangler r2 bucket create my-bucket
wrangler r2 object put my-bucket/file.txt --file=./file.txt
Configuration (wrangler.toml)
name = "my-worker"
main = "src/index.ts"
compatibility_date = "2024-01-01"
# Environment variables
[vars]
ENVIRONMENT = "production"
# D1 Database
[[d1_databases]]
binding = "DB"
database_name = "my-database"
database_id = "YOUR_DATABASE_ID"
# KV Namespace
[[kv_namespaces]]
binding = "KV"
id = "YOUR_NAMESPACE_ID"
# R2 Bucket
[[r2_buckets]]
binding = "R2_BUCKET"
bucket_name = "my-bucket"
# Durable Objects
[[durable_objects.bindings]]
name = "COUNTER"
class_name = "Counter"
script_name = "my-worker"
# Queues
[[queues.producers]]
binding = "MY_QUEUE"
queue = "my-queue"
# Workers AI
[ai]
binding = "AI"
# Cron triggers
[triggers]
crons = ["0 0 * * *"]
Best Practices
Performance
- Keep Workers lightweight (<1MB bundled)
- Use bindings over fetch (faster than HTTP)
- Leverage KV and Cache API for frequently accessed data
- Use D1 batch for multiple queries
- Stream large responses
Security
- Use
wrangler secretfor API keys - Separate production/staging/development environments
- Validate user input
- Implement rate limiting (KV or Durable Objects)
- Configure proper CORS headers
Cost Optimization
- R2 for large files (zero egress fees vs S3)
- KV for caching (reduce D1/R2 requests)
- Request deduplication with caching
- Efficient D1 queries (proper indexing)
- Monitor usage via Cloudflare Analytics
Decision Matrix
| Need | Choose |
|---|---|
| Sub-millisecond reads | KV |
| SQL queries | D1 |
| Large files (>25MB) | R2 |
| Real-time WebSockets | Durable Objects |
| Async background jobs | Queues |
| ACID transactions | D1 |
| Strong consistency | Durable Objects |
| Zero egress costs | R2 |
| AI inference | Workers AI |
| Static site hosting | Pages |