Debug This Architecture

This architecture is designed to support a real-time collaborative document editor but has several potential failure modes, race conditions, and scaling bottlenecks. Let's identify these issues and propose solutions for each:

Potential Failure Modes and Solutions

1. WebSocket Connection Failure:

   • Issue: WebSocket connections can be unstable and may disconnect frequently.
   • Solution: Implement robust reconnection logic on the client-side to automatically attempt reconnection with exponential backoff. Use heartbeats to detect dropped connections quickly.
   • Trade-offs: Increased complexity on the client-side and potential delays in re-establishing connections.

2. API Server Failure:

   • Issue: If an API server fails, all connected clients lose their connection.
   • Solution: Use a shared session store (e.g., Redis) to allow clients to reconnect to any available server. Implement load balancing that considers server health.
   • Trade-offs: Slightly increased latency due to Redis lookups, additional complexity in session management.

3. Database Write Bottleneck:

   • Issue: High volume of writes to PostgreSQL might become a bottleneck.
   • Solution: Implement a write-ahead cache using Redis to batch writes before committing them to PostgreSQL. Consider using event sourcing to append changes and process them asynchronously.
   • Trade-offs: Increased complexity and potential for data inconsistency if not managed correctly.

4. Cache Invalidation for API Responses:

   • Issue: Cached API responses might become stale, especially with a 5-minute TTL.
   • Solution: Use cache busting strategies or WebSockets to push updates to clients when data changes.
   • Trade-offs: Increased network traffic and complexity in managing cache invalidation.

5. Data Consistency and Sync Issues:

   • Issue: Last-write-wins can lead to lost updates and inconsistent views across clients.
   • Solution: Implement operational transformation (OT) or conflict-free replicated data types (CRDTs) for better conflict resolution and consistency.
   • Trade-offs: Increased complexity in conflict resolution logic and potential performance overhead.

Race Conditions and Solutions

1. Timestamp-based Conflict Resolution:

   • Issue: Client-side clock discrepancies can lead to incorrect conflict resolution.
   • Solution: Use a server-generated timestamp to ensure consistency, or synchronize client clocks using a protocol like NTP.
   • Trade-offs: Additional server-side processing and potential latency in generating timestamps.

2. Polling for Changes:

   • Issue: Polling every 2 seconds can lead to race conditions and unnecessary load on the database.
   • Solution: Implement a more efficient change data capture (CDC) mechanism like logical replication or listen/notify in PostgreSQL.
   • Trade-offs: Increased complexity in setup and potential for missed updates if not configured correctly.

Scaling Bottlenecks and Solutions

1. WebSocket Scalability:

   • Issue: Each API server managing its own WebSocket connections can lead to scalability issues as user count grows.
   • Solution: Use a dedicated WebSocket server or service (e.g., AWS AppSync or a custom solution using a message broker like Kafka or RabbitMQ) to manage connections.
   • Trade-offs: Additional infrastructure cost and complexity in integrating with existing architecture.

2. Database Scalability:

   • Issue: Single PostgreSQL instance can become a bottleneck for both reads and writes.
   • Solution: Use database sharding or partitioning beyond organization ID, and introduce read replicas to offload read operations.
   • Trade-offs: Increased complexity in database management and potential for data distribution issues.

By addressing these issues with the proposed solutions, the architecture can be made more resilient, scalable, and consistent, accommodating growth and providing a better user experience.