Debug This Architecture

Certainly! Let's break down the potential failure modes, race conditions, and scaling bottlenecks in the architecture, and propose solutions for each.

Potential Failure Modes

1. Single Point of Failure in WebSocket Connections:

   • Issue: If a single API server fails, all WebSocket connections to that server are lost.
   • Solution: Implement a WebSocket load balancer that can route WebSocket connections to different servers and handle failover. This adds complexity but ensures high availability.

2. Database Overload:

   • Issue: Frequent writes to PostgreSQL can lead to database overload, especially with many concurrent users.
   • Solution: Use a distributed database or a NoSQL database like Cassandra for handling high write throughput. This might require a schema redesign and additional operational complexity.

3. Redis Cache Failure:

   • Issue: If Redis fails, session data is lost, leading to user logouts.
   • Solution: Implement Redis clustering or use a managed Redis service with built-in failover. This adds cost but ensures high availability.

4. JWT Token Expiry Management:

   • Issue: If JWT tokens are not refreshed properly, users might be logged out unexpectedly.
   • Solution: Implement a token refresh mechanism that issues new tokens before expiry. This requires additional logic in the frontend and backend.

Race Conditions

1. Last-Write-Wins Conflict Resolution:

   • Issue: If two users edit the same paragraph simultaneously, the last write wins, which can lead to data loss.
   • Solution: Implement Operational Transformation (OT) or Conflict-free Replicated Data Types (CRDTs) for more sophisticated conflict resolution. This adds complexity but ensures data consistency.

2. WebSocket Broadcast Delays:

   • Issue: Delays in broadcasting changes to all WebSocket clients can lead to inconsistent views.
   • Solution: Use a message broker like Kafka or RabbitMQ to handle real-time messaging. This adds latency but ensures reliable message delivery.

Scaling Bottlenecks

1. WebSocket Server Scalability:

   • Issue: Each API server maintains its own WebSocket connections, which can become a bottleneck as the number of users grows.
   • Solution: Use a WebSocket server cluster with a shared message bus to distribute WebSocket connections across multiple servers. This adds complexity but improves scalability.

2. Database Read/Write Scalability:

   • Issue: As the number of documents and users grows, the database can become a bottleneck.
   • Solution: Implement database sharding or use a distributed database like Cassandra. This requires schema redesign and additional operational complexity.

3. Polling for Changes:

   • Issue: Servers polling PostgreSQL every 2 seconds can lead to high database load.
   • Solution: Use a message broker like Kafka to push changes to servers instead of polling. This adds latency but reduces database load.

4. CDN Caching Strategy:

   • Issue: Caching API responses for 5 minutes can lead to stale data for users.
   • Solution: Implement a more granular caching strategy with shorter TTLs or use edge caching with invalidation. This adds complexity but ensures fresher data.

Summary of Solutions and Trade-offs

1. WebSocket Load Balancer:

   • Trade-offs: Adds complexity, ensures high availability.

2. Distributed Database or NoSQL:

   • Trade-offs: Requires schema redesign, ensures high write throughput.

3. Redis Clustering or Managed Service:

   • Trade-offs: Adds cost, ensures high availability.

4. Token Refresh Mechanism:

   • Trade-offs: Requires additional logic, ensures seamless user experience.

5. Operational Transformation (OT) or CRDTs:

   • Trade-offs: Adds complexity, ensures data consistency.

6. Message Broker for Real-time Messaging:

   • Trade-offs: Adds latency, ensures reliable message delivery.

7. WebSocket Server Cluster with Shared Message Bus:

   • Trade-offs: Adds complexity, improves scalability.

8. Database Sharding or Distributed Database:

   • Trade-offs: Requires schema redesign, ensures scalability.

9. Message Broker for Change Propagation:

   • Trade-offs: Adds latency, reduces database load.

10. Granular Caching Strategy:

    • Trade-offs: Adds complexity, ensures fresher data.

By addressing these issues, the architecture can be made more robust, scalable, and reliable, albeit with increased complexity and potential costs.