Debug This Architecture

The proposed architecture for the real-time collaborative document editor has several potential failure modes, race conditions, and scaling bottlenecks. Here are the identified issues and proposed solutions with their trade-offs:

1. Single Point of Failure: Load Balancer

   • Issue: The load balancer is a single point of failure in the system. If the load balancer fails, the entire system becomes unavailable.
   • Solution: Implement a highly available load balancing solution, such as using a redundant pair of load balancers or a distributed load balancing service (e.g., AWS Elastic Load Balancing, Google Cloud Load Balancing).
   • Trade-offs: Increased complexity and cost of the infrastructure, potential performance impact due to the additional layer of load balancing.

2. Scaling Bottleneck: WebSocket Connections

   • Issue: Each API server maintains its own WebSocket connections to clients, which can become a scaling bottleneck as the number of concurrent users increases.
   • Solution: Introduce a dedicated WebSocket server layer, separate from the API servers, that can handle WebSocket connections and distribute the load across multiple instances.
   • Trade-offs: Additional infrastructure complexity, potential latency increase due to the additional hop between the API servers and the WebSocket servers.

3. Race Condition: Document Updates

   • Issue: When two users edit the same paragraph simultaneously, the last-write-wins strategy with timestamps from client clocks may not be reliable, as client clocks can be unsynchronized.
   • Solution: Implement a more robust conflict resolution strategy, such as using a central document state management system (e.g., using operational transformations or Conflict-free Replicated Data Types) to handle concurrent updates and ensure data consistency.
   • Trade-offs: Increased complexity in the backend and client-side logic, potential performance impact due to the additional processing required for conflict resolution.

4. Scaling Bottleneck: Database Writes

   • Issue: The frequent writes to the PostgreSQL database, especially during periods of high user activity, can become a scaling bottleneck.
   • Solution: Introduce a message queue (e.g., RabbitMQ, Apache Kafka) between the API servers and the PostgreSQL database. The API servers can publish document updates to the message queue, and a separate worker process can consume the messages and write them to the database.
   • Trade-offs: Additional infrastructure complexity, potential latency increase due to the additional message queue layer.

5. Scalability Limit: Document Partitioning

   • Issue: While the proposed document partitioning by organization ID can help with scaling, it may not be sufficient for very large organizations or high-traffic scenarios.
   • Solution: Introduce a more sophisticated document partitioning strategy, such as combining organization ID with document ID or user ID, to further distribute the load and improve scalability.
   • Trade-offs: Increased complexity in the document management and retrieval logic, potential impact on query performance and data consistency.

6. Security Concern: JWT Token Storage

   • Issue: Storing JWT tokens in the client's localStorage may not be the most secure approach, as it can be vulnerable to cross-site scripting (XSS) attacks.
   • Solution: Store the JWT tokens on the server-side, using a session-based authentication mechanism, and use a secure cookie to maintain the session. Alternatively, consider using a dedicated authentication service (e.g., Auth0, Firebase Authentication) to handle user authentication.
   • Trade-offs: Increased complexity in the authentication and session management logic, potential impact on user experience due to the additional server-side roundtrips.

7. Caching Effectiveness: API Response Caching

   • Issue: Caching API responses for 5 minutes may not be effective, especially for real-time collaborative scenarios where document changes need to be reflected immediately.
   • Solution: Implement a more granular caching strategy, where only the static assets are cached using the CDN, while the API responses are cached for a shorter duration (e.g., 10-30 seconds) or invalidated based on document update events.
   • Trade-offs: Increased complexity in the caching logic, potential impact on the user experience if the cache invalidation is not handled correctly.

By addressing these potential issues, the proposed architecture can be improved to achieve better reliability, scalability, and performance for the real-time collaborative document editor.