Debug This Architecture

Potential Failure Modes, Race Conditions, and Scaling Bottlenecks

1. WebSocket Connection Failure

• Issue: If a user's WebSocket connection drops (e.g., network issue), they may miss updates until reconnected. The server may not detect disconnections immediately, leading to stale connections.
• Solution: Implement WebSocket heartbeats (e.g., every 30 seconds) to detect inactive clients. If a client doesn't respond, close the connection. On reconnection, sync the latest document state from Redis or PostgreSQL.
• Trade-off: Adds slight overhead for heartbeat checks but improves reliability.

2. Last-Write-Wins (LWW) Conflict Resolution

• Issue: LWW can lead to unintended data loss if two users edit the same part of the document concurrently (e.g., one user's edit is discarded if the other's timestamp is later, even if the latter was a minor typo).
• Solution: Replace LWW with Operational Transformation (OT) or Conflict-Free Replicated Data Types (CRDTs) for real-time conflict resolution. This preserves all edits but adds complexity.
• Trade-off: OT/CRDTs are more complex to implement but avoid data loss.

3. Timestamp Inconsistency Across Clients

• Issue: If client clocks are significantly out of sync, LWW may incorrectly resolve conflicts (e.g., a "later" timestamp might actually be older).
• Solution: Use server-generated timestamps instead of client timestamps. When a client sends an edit, the server assigns a timestamp before storing it.
• Trade-off: Requires a round trip to the server for every edit, adding latency.

4. WebSocket Broadcast Limited to a Single Server

• Issue: If clients are distributed across multiple servers (due to round-robin load balancing), changes made to one server are not immediately broadcast to clients on other servers (they only poll every 2 seconds).
• Solution: Use Redis Pub/Sub for real-time cross-server communication. When a server processes a change, it publishes it to Redis, and all other servers subscribe and broadcast to their clients.
• Trade-off: Adds Redis dependency but enables real-time cross-server sync.

5. Polling for Cross-Server Changes

• Issue: Servers polling PostgreSQL every 2 seconds for changes is inefficient and can cause database load.
• Solution: Replace polling with Redis Pub/Sub (as above) or PostgreSQL LISTEN/NOTIFY for real-time change notifications.
• Trade-off: LISTEN/NOTIFY is database-specific but more efficient than polling.

6. JWT Token Invalidation

• Issue: If a user logs out or tokens are compromised, stale tokens in localStorage could still grant access until expiry (24 hours).
• Solution: Implement token revocation (e.g., store invalid tokens in Redis with a TTL). On critical actions (e.g., saving edits), require a fresh token or re-authentication.
• Trade-off: Adds complexity but improves security.

7. Full HTML Snapshot Storage

• Issue: Storing full HTML snapshots every 30 seconds can lead to large storage usage and potential data redundancy.
• Solution: Store only diffs (changes) instead of full snapshots. Implement a versioned document storage system (e.g., Git-like history).
• Trade-off: Diffs are more storage-efficient but require a way to reconstruct the full document.

8. CDN Caching API Responses

• Issue: Caching API responses for 5 minutes could return stale data (e.g., unsaved edits) if a client is offline and reconnects.
• Solution: Use CDN cache invalidation (e.g., Vary: Authorization header) or make API responses private (not cached) for authenticated requests.
• Trade-off: Private responses reduce CDN benefits but ensure data freshness.

9. Database Write Bottleneck

• Issue: High-frequency writes (e.g., during collaborative editing) could overwhelm PostgreSQL.
• Solution: Implement write batching or use a more scalable database like DynamoDB for collaborative edits, with eventual consistency.
• Trade-off: DynamoDB is more scalable but requires adapting the data model.

10. Session Cache (Redis) Failure

• Issue: If Redis fails, session data is lost, leading to auth issues or connection drops.
• Solution: Use Redis Sentinel or Cluster for high availability. Fallback to database-encrypted session storage if Redis is down.
• Trade-off: Adds complexity but ensures reliability.

11. Document Partitioning by Organization ID

• Issue: If an organization has extremely high traffic, its partition could become a bottleneck.
• Solution: Implement sharding within organization partitions (e.g., by document ID or user ID).
• Trade-off: Sharding adds complexity but improves scalability.

Summary of Key Fixes

1. Replace LWW with OT/CRDTs for conflict resolution.
2. Use server timestamps or Redis Pub/Sub for real-time sync.
3. Replace polling with database notifications or Redis Pub/Sub.
4. Implement token revocation for JWT security.
5. Store document diffs instead of full snapshots.
6. Make API responses private to avoid CDN staleness.
7. Use Redis Cluster/Sentinel for session cache reliability.
8. Consider sharding for high-traffic organizations.

These changes improve reliability, reduce race conditions, and address scaling bottlenecks, though some add complexity or require additional infrastructure.