Debug This Architecture

Here are the critical issues I've identified in this architecture:

Critical Failure Modes

1. WebSocket Connection Loss & Server Failure

Problem: When an API server crashes or restarts, all WebSocket connections are lost, and clients won't know about changes processed by that server.

Solution:

• Implement WebSocket reconnection with automatic failover to different servers
• Add server-side state persistence for active document sessions
• Use sticky sessions or client-side load balancing awareness

Trade-offs: Increased complexity vs. guaranteed message delivery

2. Clock Synchronization Issues

Problem: Client-generated timestamps for conflict resolution are unreliable due to clock drift, leading to inconsistent conflict resolution.

Solution:

• Use server-generated timestamps when processing changes
• Implement logical clocks (Lamport timestamps) or vector clocks
• Add client timestamp as metadata, but use server time for ordering

Trade-offs: Slightly higher latency vs. correct ordering guarantees

Race Conditions

3. Cross-Server Race Condition

Problem: Server A processes change, broadcasts to its clients, but Server B's 2-second poll hasn't occurred yet, so Server B's clients see stale data.

Solution:

• Replace polling with database LISTEN/NOTIFY (PostgreSQL) or similar pub/sub
• Implement inter-server communication via Redis pub/sub for real-time updates
• Add sequence numbers to track processing order

Trade-offs: More complex infrastructure vs. real-time consistency

4. Read-After-Write Inconsistency

Problem: Client makes change, reads document before change is committed to database, seeing stale data.

Solution:

• Implement read-your-writes consistency by caching recent changes in memory
• Use database read-after-write patterns or force reads from master
• Add client-side optimistic UI updates with rollback capability

Trade-offs: Increased memory usage vs. consistency guarantees

Scaling Bottlenecks

5. Database Bottleneck

Problem: Every change writes to PostgreSQL, and polling creates read pressure. Full HTML snapshots every 30 seconds are expensive.

Solution:

• Store operational transforms (deltas) instead of full snapshots
• Use database connection pooling and batch writes
• Implement change data capture (CDC) instead of polling
• Add document versioning with incremental diffs

Trade-offs: More complex data model vs. database scalability

6. Broadcast Scalability

Problem: Each server only broadcasts to its own connected clients, creating inconsistent views during cross-server collaboration.

Solution:

• Use Redis pub/sub for cross-server message distribution
• Implement consistent hashing for document-to-server mapping
• Add message queuing system (RabbitMQ/Kafka) for reliable delivery

Trade-offs: Additional infrastructure vs. global consistency

7. CDN Cache Invalidation

Problem: 5-minute CDN caching conflicts with real-time collaboration needs.

Solution:

• Separate real-time WebSocket endpoints from cached API endpoints
• Implement cache invalidation for collaborative documents
• Use different caching strategies for static vs. dynamic content

Trade-offs: Reduced cache efficiency vs. real-time accuracy

Additional Critical Issues

8. Authentication Security

Problem: JWT in localStorage is vulnerable to XSS attacks.

Solution:

• Use httpOnly cookies with SameSite flags
• Implement token refresh mechanisms
• Add additional security headers

Trade-offs: Slightly more complex auth flow vs. security

9. Conflict Resolution Limitations

Problem: Last-write-wins can lose data when multiple users edit simultaneously.

Solution:

• Implement operational transformation (OT) or Conflict-free Replicated Data Types (CRDTs)
• Add merge conflict detection and resolution UI
• Use more sophisticated conflict resolution algorithms

Trade-offs: Significant complexity increase vs. data integrity

10. Session Management

Problem: Redis session cache can become inconsistent or lost during failures.

Solution:

• Implement redundant Redis setup with replication
• Add session persistence to database as backup
• Use distributed session management patterns

Trade-offs: Infrastructure complexity vs. session reliability

Recommended Priority Fixes

1. Immediate: Fix clock synchronization and WebSocket reliability
2. Short-term: Implement proper pub/sub instead of polling
3. Medium-term: Improve conflict resolution and data storage strategy
4. Long-term: Enhance security and implement advanced consistency models

The architecture needs fundamental changes to support true real-time collaboration at scale, particularly around consistency models and communication patterns.