Debug This Architecture

In the architecture you've described for a real-time collaborative document editor, various potential failure modes, race conditions, and scaling bottlenecks can arise. Below, I outline these issues along with proposed solutions and their associated trade-offs.

1. Failure Modes

a. WebSocket Connection Failures

Issue: WebSockets can disconnect due to network issues or server crashes, leading to loss of real-time updates for users.

Solution: Implement a reconnection strategy that attempts to re-establish the WebSocket connection automatically. Additionally, use a queue on the client-side to store unsent changes during disconnection and send them once reconnected.

Trade-offs: This increases complexity on the client-side and may introduce a delay in sending updates, but it improves user experience by minimizing disruption.

b. Database Failures

Issue: PostgreSQL might be unavailable or have performance issues, leading to failed writes or reads.

Solution: Implement a retry mechanism with exponential backoff for database operations. Additionally, consider using a message queue system (like RabbitMQ) to queue changes when the database is down, allowing for eventual consistency.

Trade-offs: This adds complexity and potential latency in syncing changes, but it enhances system reliability.

c. Server Crashes

Issue: If an API server crashes, all connected clients will lose their connection.

Solution: Use a sticky session approach with the load balancer to ensure users reconnect to the same server, and implement a health check mechanism to quickly reroute traffic to healthy servers.

Trade-offs: Sticky sessions can lead to uneven load distribution, but they help in maintaining connection state.

2. Race Conditions

a. Concurrent Edits

Issue: Two users editing the same document section simultaneously could lead to data inconsistency.

Solution: Instead of a simple last-write-wins strategy, implement operational transformation (OT) or conflict-free replicated data types (CRDTs) for better handling of concurrent edits.

Trade-offs: OT and CRDTs are more complex to implement but provide a better user experience by allowing real-time collaboration without losing changes.

b. Polling Conflicts

Issue: Servers polling for changes every 2 seconds might read stale data or miss changes made by users connected to other servers.

Solution: Instead of polling, consider using a pub/sub mechanism where changes are published to a message broker (like Redis Pub/Sub) and subscribed to by all servers.

Trade-offs: This requires additional infrastructure and complexity, but it reduces the chances of stale reads and improves real-time sync.

3. Scaling Bottlenecks

a. Database Bottlenecks

Issue: As the number of users grows, the database may become a bottleneck, especially for write operations.

Solution: Implement database sharding based on document ownership or organization ID to reduce load on individual database instances. Use read replicas to balance read operations.

Trade-offs: Sharding increases complexity in database management and may require changes to application logic, but it significantly improves scalability.

b. WebSocket Connection Limits

Issue: Each API server has a limit on the number of concurrent WebSocket connections it can handle.

Solution: Introduce a WebSocket gateway layer that can handle connections and distribute messages to the appropriate API servers. Use load balancing for WebSocket connections.

Trade-offs: This introduces an extra layer in the architecture, which can add latency but allows for better management of connections and scaling.

c. Cache Staleness

Issue: Using Redis for session caching may lead to stale data if not handled properly.

Solution: Implement a cache invalidation strategy, such as time-based expiration or invalidating the cache on certain write operations.

Trade-offs: While this adds complexity to the caching layer, it ensures data consistency and reduces the risk of serving outdated data.

Conclusion

By addressing these potential failure modes, race conditions, and scaling bottlenecks, the architecture can be made more robust and scalable. The trade-offs involved in each solution should be carefully considered based on the specific performance needs and user experience expectations of the collaborative document editor.