Debug This Architecture

I'll analyze this architecture systematically and identify key issues along with potential solutions:

1. Clock Synchronization Issues Problem: Using client timestamps for conflict resolution is unreliable due to unsynchronized clocks. Solution: Replace with Lamport timestamps or vector clocks

• Pro: Guarantees causally consistent ordering
• Con: Increased complexity and metadata overhead
• Con: Vector clocks grow with number of users

2. Split-Brain WebSocket Broadcasting Problem: Changes only broadcast within same server, creating race conditions Solution A: Use Redis Pub/Sub for cross-server broadcasting

• Pro: Real-time cross-server communication
• Con: Additional Redis load
• Con: Single point of failure

Solution B: Move to Kafka for event streaming

• Pro: Better scaling and fault tolerance
• Con: Increased operational complexity
• Con: Higher latency than Redis

3. Polling Bottleneck Problem: Polling PostgreSQL every 2 seconds won't scale with many servers Solution: Implement Change Data Capture (CDC) with PostgreSQL logical replication

• Pro: Real-time updates without polling
• Con: More complex setup
• Con: Requires careful monitoring of replication lag

4. Single Write Master Database Problem: All writes going to one PostgreSQL instance Solution: Implement document-based sharding

• Pro: Distributes write load
• Con: Complexity in cross-shard operations
• Con: Need for distributed transactions

5. Last-Write-Wins Data Loss Problem: LWW can silently drop valid user changes Solution: Implement Operational Transform (OT) or Conflict-free Replicated Data Types (CRDTs)

• Pro: No data loss, better conflict resolution
• Con: Significant complexity increase
• Con: Higher memory usage

6. JWT Token Security Problem: localStorage vulnerable to XSS, 24-hour window is long Solution: Use HttpOnly cookies with shorter expiry + refresh tokens

• Pro: Better security
• Con: Need to handle refresh flow
• Con: More complex implementation

7. CDN Cache Consistency Problem: 5-minute cache could serve stale data Solution: Implement cache invalidation on writes

• Pro: Consistent data
• Con: More complex deployment
• Con: Potentially higher origin load

8. WebSocket Connection Management Problem: No reconnection strategy specified Solution: Implement exponential backoff + state sync

• Pro: Better reliability
• Con: More complex client logic
• Con: Need for state reconciliation

9. Full Document Snapshots Problem: Storing full HTML snapshots is inefficient Solution: Store operations log + periodic compaction

• Pro: More efficient storage
• Con: More complex recovery
• Con: Need for background compaction jobs

10. Load Balancer Session Affinity Problem: Round-robin might break WebSocket connections Solution: Implement sticky sessions

• Pro: Maintains WebSocket connections
• Con: Less even load distribution
• Con: More complex failover

11. Redis Single Point of Failure Problem: Single Redis instance for session cache Solution: Redis cluster with sentinel

• Pro: High availability
• Con: Network complexity
• Con: Need for cluster management

12. Document Concurrency Control Problem: No explicit locking mechanism Solution: Implement optimistic concurrency control with version numbers

• Pro: Better conflict detection
• Con: May require retry logic
• Con: Additional database load

Recommendations for Implementation Priority:

1. Replace client timestamps with Lamport timestamps immediately
2. Implement Redis Pub/Sub for cross-server communication
3. Switch to CDC instead of polling
4. Implement proper WebSocket reconnection handling
5. Add proper concurrency control
6. Improve security with HttpOnly cookies
7. Implement proper cache invalidation
8. Consider CRDT implementation for long-term scalability

This would provide a more robust foundation while keeping immediate implementation complexity manageable. The more complex solutions (like sharding) can be implemented as needed based on actual usage patterns and growth.