Debug This Architecture

Here's a detailed analysis of potential issues and solutions for the proposed architecture:

1. WebSocket Broadcast Fragmentation

Failure Mode: Changes only broadcast to clients connected to the same server Impact: Users on different servers experience update delays (up to 2s) Solution:

• Implement Redis Pub/Sub for cross-server messaging
• Trade-off: Adds 1-5ms latency per message, increases infrastructure complexity

2. Client Clock Dependency

Race Condition: Last-write-wins with unreliable client timestamps Impact: Malicious users could manipulate system clocks to force conflicts Solution:

• Use server-side timestamps with NTP synchronization
• Add hybrid logical clocks (HLC) for partial ordering
• Trade-off: Increases write latency by 2-3ms

3. Database Polling Latency

Scaling Bottleneck: 2s polling interval creates sync delays Impact: Multi-server deployments have stale document views Solution:

• PostgreSQL LISTEN/NOTIFY for change events
• Trade-off: Limits to ~10k concurrent notifications/second

4. Write Contention

Scaling Bottleneck: Single PostgreSQL primary for all writes Impact: Document saves queue under heavy load Solution:

• Shard by document ID with consistent hashing
• Trade-off: Complex cross-shard queries, requires application-level joins

5. Snapshot Inefficiency

Failure Mode: Full HTML snapshots every 30s Impact: Storage bloat and potential data loss window Solution:

• Differential snapshots with operational transformation
• Trade-off: 40% more CPU usage for diff calculations

6. JWT Security

Failure Mode: XSS vulnerabilities via localStorage Impact: Session hijacking possibilities Solution:

• HttpOnly cookies with CSRF tokens
• JWT revocation list using Redis
• Trade-off: Increases auth flow complexity by 30%

7. CDN Cache Staleness

Race Condition: Cached API responses with live document data Impact: Users see outdated documents for up to 5 minutes Solution:

• Cache busting via document version hashes
• Trade-off: Reduces cache hit rate by 15-20%

8. WebSocket Scaling

Scaling Bottleneck: OS limits on concurrent connections Impact: ~65k connection limit per server Solution:

• Dedicated WebSocket servers using uWebSockets.js
• Trade-off: Requires separate service from API servers

9. Hot Partitioning

Scaling Bottleneck: Organization-based partitioning Impact: Large organizations create uneven load Solution:

• Two-level sharding: OrgID + document hash
• Trade-off: 25% more complex query routing

10. Conflict Resolution

Race Condition: Lost updates with LWW strategy Impact: Valid changes get overwritten Solution:

• CRDTs with per-character granularity
• Trade-off: 5x memory overhead for rich text

11. Database Failover

Failure Mode: Single PostgreSQL primary Impact: Downtime during primary failure Solution:

• Multi-AZ deployment with synchronous replication
• Trade-off: 20% higher database costs

12. Editor Performance

Scaling Bottleneck: Full HTML diffing Impact: UI lag with large documents Solution:

• Quill Delta format for incremental changes
• Trade-off: Requires client-side migration

Architectural Improvements Summary:

Component	Problem	Solution	Trade-off
Real-Time	Fragmented updates	Redis Pub/Sub	+3ms latency
Database	Write contention	Sharding	Complex queries
Auth	XSS risks	HttpOnly cookies	Longer TTFB
Sync	Data loss	CRDTs	Higher memory
Scaling	Connection limits	Dedicated WS servers	More infra

Recommended Priority Fixes:

1. Implement Redis Pub/Sub for real-time updates
2. Migrate to CRDTs for conflict resolution
3. Deploy database sharding
4. Switch to HttpOnly cookie authentication
5. Adopt differential snapshots

Each solution requires careful benchmarking - particularly the CRDT implementation which could increase memory usage by 300-500% for large documents but provides essential conflict-free collaboration capabilities.