Durable Cache Sync Pattern

A production-ready pattern for high-traffic applications needing low-latency reads with eventual consistency. Perfect for:

• Session management
• User profile caching
• Real-time collaboration features
• High-velocity write buffers

Why This Pattern?

Traditional Database Challenges	With Cache Sync Pattern
📉 90%+ of queries hit primary DB	📈 90%+ cache hit rate
💸 Expensive direct DB writes	🐢 Batched async writes save costs
🌍 Global latency inconsistencies	🚀 Edge caching <5ms responses
🔄 Complex cache invalidation	🔄 Auto-sync with writeback queue

Architecture

Key Components:

• Durable Object Cache: Per-key cache with in-memory access
• Write Buffer: Batched updates every 60s
• Auto Refresh: Stale-while-revalidate pattern
• Pass-through Worker: Simple edge routing

Napkin Math Cost Comparison (1M Requests/Month)

Resource	Traditional DB	Cache Sync Pattern
Database Reads	$500-$2000	$15-$50
Database Writes	$300-$1500	$5-$20
Cache Layer	$0	$10-$30
Total	$800-$3500	$30-$100

Getting Started

1. Clone and install:

git clone https://github.com/acoyfellow/cache-sync.git
cd cache-sync
bun install

2. Configure environment (wrangler.json):

{
  "name": "cache-sync-pattern",
  "main": "src/worker.ts",
  "compatibility_date": "2024-03-01",
  "durable_objects": {
    "bindings": [
      {
        "name": "userprofile",
        "class_name": "UserProfileCache"
      }
    ]
  },
  "vars": {
    "MAIN_DB_API": "https://your-db-api.com"
  }
}

3. Local development:

bun run dev
# Interact with App.svelte at http://localhost:5173

4. Deploy to Cloudflare:

bunx wrangler deploy

How It Works

1. Read Path:

Client → Worker → Dedicated User DO Instance (Isolated per session)
                ↳ (Cache Miss) → Create New DO Instance → Return Stub

2. Write Path:

Client → Dedicated User DO Instance → In-Memory State + Storage
                               ↳ Alarm → Batch Sync to Main DB

3. Client Sync:

Offline → Local Storage → Auto Retry

Features:

• Network detection
• Local persistence
• Auto-retry on reconnect
• Accessible status

This implements a robust offline-first pattern where:

• All user interactions remain available offline
• Changes queue locally until connectivity resumes
• UI clearly indicates sync status
• Data automatically reconciles when back online

Implementation Phases:

1. Shadow Mode: Run cache layer parallel to main API, compare results

2. Read-Only Canary: Route 5% of read traffic through cache

3. Write Queueing: Enable write buffering for canary users

4. Full Cutover: Gradually increase traffic over 1-2 weeks

This maintains security while allowing gradual rollout. The key is keeping the cache layer as a transparent proxy that enforces existing auth rules.

Future Concerns

• Data Compression - Store more data in Durable Object state
• TTL Expiration - Automatically expire stale data
• Rate Limiting - Prevent API abuse
• Auth Integration - Add request validation
• Metrics Dashboard - Track cache performance

License

MIT License - Free for commercial and personal use.