Debug This Architecture

Critical Issues in the Architecture

1. WebSocket Connection Partitioning (Race Condition)

Issue: Clients are connected to different API servers based on load balancing, causing inconsistent message delivery.

• Problem: When user A edits a document, the change is broadcast only to clients connected to the same server
• Risk: Client B connected to server X won't receive updates from client A connected to server Y
• Solution: Implement a centralized pub/sub system (Redis pub/sub or message broker like Kafka/NSQ)
• Trade-offs: Adds network latency, requires additional infrastructure complexity, potential single point of failure

2. Document Consistency Race Condition

Issue: Two clients editing the same paragraph simultaneously can cause data loss

• Problem: Last-write-wins strategy with client timestamps can lose concurrent edits
• Risk: If client A and B both edit paragraph 1 at nearly the same time, one edit gets overwritten
• Solution: Implement Operational Transformation (OT) or Conflict-free Replicated Data Types (CRDTs)
• Trade-offs: Complex implementation, potential performance overhead, harder to debug

3. Database Write Bottleneck

Issue: All write operations go through PostgreSQL directly

• Problem: PostgreSQL becomes a bottleneck under high concurrent write loads
• Risk: Write latency increases dramatically, potential database connection pool exhaustion
• Solution: Implement database sharding by document ID or add read replicas for writes
• Trade-offs: Increased complexity, eventual consistency challenges, higher operational overhead

4. Eventual Consistency Lag

Issue: 2-second polling interval creates noticeable delay

• Problem: Users see stale data up to 2 seconds after another user's changes
• Risk: Poor user experience during collaborative editing
• Solution: Use WebSockets for real-time notifications instead of polling, implement Redis pub/sub
• Trade-offs: Higher infrastructure costs, more complex state management

5. Single Point of Failure - Load Balancer

Issue: Round-robin load balancer creates uneven distribution

• Problem: No awareness of connection counts or server health
• Risk: Some servers become overloaded while others sit idle
• Solution: Implement smart load balancing (least connections, health checks, weighted routing)
• Trade-offs: Additional complexity, potential for temporary imbalances during scaling events

6. Memory Cache Invalidation

Issue: Redis cache for sessions isn't clearly invalidated

• Problem: Stale authentication information in cache
• Risk: Users remain authenticated when they should be logged out
• Solution: Implement cache TTLs, proper invalidation triggers, or use JWT-based session store
• Trade-offs: Cache hit rate reduction, increased database reads, more complex invalidation logic

7. CDN Caching Issues

Issue: CDN caching API responses for 5 minutes

• Problem: Long-lived cached responses create stale content
• Risk: Document versions may not update in real-time for some users
• Solution: Implement cache-control headers with no-cache for sensitive data, use cache-busting URLs
• Trade-offs: Reduced CDN effectiveness, increased bandwidth usage, more complex caching strategy

8. Authentication Security Vulnerability

Issue: JWT tokens stored in localStorage

• Problem: XSS attacks can steal tokens from localStorage
• Risk: Session hijacking, unauthorized access to documents
• Solution: Store tokens in HttpOnly cookies, implement CSRF protection, use secure flag
• Trade-offs: CORS configuration complexity, potential issues with cross-origin requests, browser compatibility concerns

9. Document Storage Scalability

Issue: Full HTML snapshots every 30 seconds

• Problem: High I/O pressure on database, large storage requirements
• Risk: Database performance degradation, high storage costs
• Solution: Implement incremental diffs instead of full snapshots, compress data before storage
• Trade-offs: More complex synchronization logic, potential data corruption risk if diff fails

10. Horizontal Scaling Coordination

Issue: No coordination mechanism between API servers

• Problem: Each server operates independently without knowledge of global state
• Risk: Inconsistent views of the same document across servers
• Solution: Add distributed consensus layer or coordination service (etcd, Consul)
• Trade-offs: Increased complexity, potential availability issues, network dependency

11. Client-Side Conflict Resolution

Issue: Client clocks might not be synchronized

• Problem: Timestamps from different clients may be inconsistent
• Risk: Incorrect conflict resolution, data loss
• Solution: Implement server-side timestamp generation, use vector clocks, or logical clocks
• Trade-offs: Additional round-trips, potential latency increase, more complex client-server communication

12. Network Partition Issues

Issue: No handling for network partitions

• Problem: If a server goes down, clients lose connection
• Risk: Data loss, service unavailability
• Solution: Implement automatic failover, connection retry mechanisms, graceful degradation
• Trade-offs: Increased complexity, potential for split-brain scenarios, longer recovery times

Recommended Immediate Fixes:

1. Implement Redis pub/sub for real-time messaging across servers
2. Add proper authentication security (HttpOnly cookies + CSRF)
3. Replace polling with WebSocket notifications for better real-time sync
4. Implement Operational Transformation or CRDTs for conflict resolution
5. Add circuit breaker pattern for external dependencies

The core architectural flaw is the lack of coordination between independent API servers, which fundamentally breaks the collaborative model. Addressing the WebSocket partitioning issue should be the top priority.