Debug This Architecture

Let's analyze this architecture systematically:

1. Timestamp-based Conflict Resolution Issues

Problem: Using client clocks creates potential race conditions due to clock drift and network latency variability. If two users edit the same content near simultaneously but clocks aren't synchronized, the actual last edit might not win.

Solution: Implement logical timestamps (Lamport clocks) or true-time servers (like Spanner). Client sends opId, server increments sequence, gives you serverOpId that enforces causality.

Trade-offs: Higher complexity, requires versioning database records

2. WebSocket Connection Management

Problem: Sticky sessions needed for WebSocket clients (same client must connect to same server). Users could lose connection during server scaling events.

Solution: Implement Pub/Sub pattern with Redis. All servers subscribe to changes, allowing any server to broadcast updates to clients.

Trade-offs: Higher infrastructure cost, adds latency for initial broadcast

3. Full Snapshot Save Bottleneck

Problem: Saving full HTML snapshots every 30 seconds under high edit frequency creates database contention.

Solution: Implement delta updates - store diffs between versions, only snapshot on major changes. Store deltas with expiry based on version frequency.

Trade-offs: Need periodic compaction of deltas to snapshots

4. Session Cache Inconsistency

Problem: JWT tokens stored in localStorage are vulnerable to XSS, leading to session hijacking.

Solution: Move JWT to httpOnly cookies with CSRF protection. Revoke tokens server-side using Redis blacklist.

Trade-offs: CORS restrictions, additional CSRF token handling

5. Database Partitioning Issues

Problem: Organization-level partitioning creates hot partitions for large organizations. Write amplification when organizations cross partition boundaries.

Solution: Shard by document ID and maintain cross-shard consistency through distributed transactions. Use eventual consistency with quorum writes.

Trade-offs: Read latency increases, requires transaction coordination overhead

6. CDN Cache Invalidation

Problem: API responses cached for 5 minutes means stale data during active collaboration.

Solution: Implement cache invalidation via WebSocket triggers or CDN PATCH API with adaptive TTLs.

Trade-offs: Increases CDN costs, requires additional tooling

7. PostgreSQL Write Contention

Problem: High-frequency document updates create write bottlenecks in PostgreSQL.

Solution: Implement append-only logs for each document, batch writes, and use replication with read replicas.

Trade-offs: More storage needed, increased complexity for data integrity

8. Document Version Rollback

Problem: Conflicts causing content loss when two clients edit same section.

Solution: Store complete document history with revert functionality, maintain author info. Implement operational transform (OT) or CRDT for true collaborative editing.

Trade-offs: Much higher storage requirements, complex conflict resolution

9. Load Balancer State

Problem: Round-robin LB doesn't consider server load or WebSocket connection count.

Solution: Switch to least-connections algorithm with health checks. Add connection pooling with connection limits per server.

Trade-offs: Higher operational complexity, need to monitor active connections

10. Recovery Scenario Race Conditions

Problem: Server failure causes lost in-memory state and reconnections.

Solution: Implement server recovery protocol with document state reconstruction, client reconnection strategy, and state reconciliation using database version.

Trade-offs: Significant complexity increase, possible data inconsistencies during recovery

The key insight is that real-time collaboration systems face fundamental tradeoffs between consistency, availability, and partition tolerance. The proposed solutions often shift complexity from one area to another, requiring careful analysis of your specific use case and scaling requirements. Would you like me to elaborate on any of these solutions or discuss alternatives?