Debug This Architecture

Potential Failure Modes:

1. Single Point of Failure (SPOF) in Load Balancer: If the load balancer fails, all incoming traffic will be lost. Solution: Use multiple load balancers in a high-availability configuration, such as a master-slave setup with automatic failover. Trade-off: Additional complexity and cost.

2. Database Connection Pool Exhaustion: If too many API servers connect to the database simultaneously, the connection pool may become exhausted. Solution: Implement a connection pooling mechanism that can scale with the number of API servers, or consider using a message queue to handle updates. Trade-off: Additional complexity and potential performance impact.

3. Client-Side WebSocket Connection Failure: If a client's WebSocket connection is lost, they may not receive updates or be able to send changes. Solution: Implement automatic reconnection with exponential backoff, and consider using a message queue to handle updates. Trade-off: Additional complexity and potential performance impact.

4. Server-Side WebSocket Connection Failure: If an API server's WebSocket connections are lost, clients will not receive updates or be able to send changes. Solution: Implement automatic reconnection with exponential backoff, and consider using a message queue to handle updates. Trade-off: Additional complexity and potential performance impact.

5. Auth Token Expiration: If a user's auth token expires, they will be logged out and unable to access the document. Solution: Implement automatic token renewal or consider using a longer-lived token. Trade-off: Potential security risks if tokens are not properly secured.

6. CDN Cache Invalidation: If the CDN cache is not properly invalidated, users may see stale data. Solution: Implement a cache invalidation mechanism that updates the CDN cache when data changes. Trade-off: Additional complexity and potential performance impact.

Race Conditions:

1. Last-Write-Wins Conflict Resolution: If two users edit the same paragraph simultaneously, the last write may win, potentially causing data loss for the first user. Solution: Implement a more sophisticated conflict resolution mechanism, such as operational transformation or multi-version concurrency control. Trade-off: Additional complexity and potential performance impact.

2. Database Update Race Conditions: If multiple API servers update the database simultaneously, race conditions may occur. Solution: Implement a locking mechanism or consider using a message queue to handle updates. Trade-off: Additional complexity and potential performance impact.

Scaling Bottlenecks:

1. Database Write Bottleneck: As the number of users increases, the database may become a bottleneck due to high write traffic. Solution: Implement a sharding mechanism to distribute write traffic across multiple databases. Trade-off: Additional complexity and potential data consistency issues.

2. API Server WebSocket Connection Bottleneck: As the number of users increases, API servers may become bottlenecked due to high WebSocket connection traffic. Solution: Implement a connection pooling mechanism or consider using a message queue to handle updates. Trade-off: Additional complexity and potential performance impact.

3. CDN Cache Hit Bottleneck: As the number of users increases, the CDN cache may become bottlenecked due to high traffic. Solution: Implement a caching mechanism that can scale with traffic, or consider using a content delivery network with built-in caching. Trade-off: Additional complexity and potential performance impact.

4. Redis Session Cache Bottleneck: As the number of users increases, the Redis session cache may become bottlenecked due to high traffic. Solution: Implement a sharding mechanism to distribute session cache traffic across multiple Redis instances. Trade-off: Additional complexity and potential data consistency issues.

Additional Suggestions:

1. Implement Monitoring and Logging: Monitor system performance and log errors to quickly identify and resolve issues.
2. Implement Automated Testing: Automate testing to ensure the system works as expected and to catch regressions.
3. Implement a Circuit Breaker: Implement a circuit breaker to prevent cascading failures in the system.
4. Implement a Queue-Based Architecture: Consider using a queue-based architecture to handle updates and reduce the load on the database.
5. Implement a Load Testing Framework: Implement a load testing framework to test the system under high traffic conditions.