Debug This Architecture

Based on the provided architecture, here are potential failure modes, race conditions, and scaling bottlenecks along with proposed solutions and their trade-offs:

1. Database Write Contention

   • Issue: High write contention on the PostgreSQL database could lead to reduced performance and increased latency.
   • Solution: Use a distributed database like Amazon DynamoDB or Apache Cassandra that are designed for high write throughput and can scale horizontally.
   • Trade-offs:
     • Pro: Better performance and scalability for high-write workloads.
     • Con: Increased cost compared to PostgreSQL, may require changes in data modeling and querying.

2. WebSocket Connection Loss

   • Issue: Loss of WebSocket connection could lead to missed updates.
   • Solution: Implement a reconnection strategy with exponential backoff and use a message queue like RabbitMQ or Apache Kafka to buffer updates during disconnection.
   • Trade-offs:
     • Pro: Ensures no data loss during disconnections.
     • Con: Increased complexity, additional infrastructure, and potential message loss if the queue is not durable.

3. Last-Write-Wins Conflict Resolution

   • Issue: Race conditions could occur when two users edit the same paragraph simultaneously.
   • Solution: Implement Operational Transformation (OT) or Conflict-free Replicated Data Types (CRDT) algorithms to merge changes instead of overwriting them.
   • Trade-offs:
     • Pro: Provides a better user experience by preserving both users' changes.
     • Con: Increased complexity in implementing OT or CRDT algorithms, potential performance overhead.

4. API Server Polling Latency

   • Issue: Polling PostgreSQL every 2 seconds for changes might introduce unnecessary load and latency.
   • Solution: Use a message broker like RabbitMQ or Apache Kafka for servers to consume changes as they occur.
   • Trade-offs:
     • Pro: Reduces latency and server load.
     • Con: Increased complexity, additional infrastructure, and potential message loss if the queue is not durable.

5. CDN Caching Invalidation

   • Issue: Cached API responses in CloudFront might not be invalidated properly, leading to stale data.
   • Solution: Implement Content-aware DDoS protection with AWS Lambda@Edge to invalidate cached responses based on specific URI patterns or headers.
   • Trade-offs:
     • Pro: Ensures users always receive fresh data.
     • Con: Increased cost due to AWS Lambda invocations, potential complexity in managing invalidation rules.

6. Single Point of Failure (Load Balancer)

   • Issue: The load balancer is a single point of failure that could cause downtime if it fails.
   • Solution: Use multiple load balancers in an active-active configuration or implement a service mesh like Istio or Linkerd for better resiliency.
   • Trade-offs:
     • Pro: Improves availability and fault tolerance.
     • Con: Increased complexity and cost.

7. Document Partitioning by Organization ID

   • Issue: As organizations grow, partitioning documents by organization ID might lead to cold start issues when users access documents from inactive partitions.
   • Solution: Implement a caching layer like Redis or Elasticache to cache frequently accessed documents across partitions.
   • Trade-offs:
     • Pro: Improves performance for frequently accessed documents.
     • Con: Increased cost and complexity, potential eviction policies that could remove active documents from the cache.

8. API Server Desync

   • Issue: API servers might get desynchronized due to network partitions or delayed updates, leading to different servers sending conflicting updates to clients.
   • Solution: Implement a consensus algorithm like Raft or Paxos to ensure all API servers agree on the current document state before broadcasting updates.
   • Trade-offs:
     • Pro: Ensures all API servers are in sync and sends consistent updates to clients.
     • Con: Increased complexity and potential performance overhead.

Addressing these failure modes, race conditions, and scaling bottlenecks will improve the overall reliability, performance, and scalability of the real-time collaborative document editor. However, each solution comes with its own trade-offs that should be carefully considered based on the specific use case and requirements.