Debug This Architecture

This architecture has several potential failure modes, race conditions, and scaling bottlenecks. Below is a detailed analysis with proposed solutions and their trade-offs:

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1. WebSocket Connection Fragmentation (Failure Mode)

Issue: Each API server maintains its own WebSocket connections, meaning clients connected to different servers won’t receive real-time updates from each other. This breaks the collaborative experience.

Solution:

• Use a dedicated WebSocket service (e.g., Socket.io with Redis adapter) to centralize WebSocket connections.
• Trade-offs:
  • Adds complexity (another service to manage).
  • Increases latency slightly due to an extra hop via Redis.

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2. Last-Write-Wins Conflict Resolution (Race Condition)

Issue: Relying on client timestamps for conflict resolution is unreliable due to clock skew. Two users editing the same paragraph could lead to lost updates if timestamps are close.

Solution:

• Use Operational Transformation (OT) or Conflict-Free Replicated Data Types (CRDTs) for proper conflict resolution.
• Trade-offs:
  • OT/CRDTs are complex to implement.
  • Requires significant changes to the sync logic.

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3. Polling for Changes (Scaling Bottleneck)

Issue: Servers poll PostgreSQL every 2 seconds, which is inefficient and doesn’t scale well with many servers.

Solution:

• Use PostgreSQL logical replication or a pub/sub system (e.g., Kafka, Redis Pub/Sub) to push changes to all servers.
• Trade-offs:
  • Adds infrastructure complexity.
  • Requires handling message ordering and deduplication.

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4. Full HTML Snapshots (Performance & Storage Issue)

Issue: Saving full HTML snapshots every 30 seconds is inefficient for large documents and can lead to high storage costs.

Solution:

• Use incremental updates (deltas) instead of full snapshots (e.g., store only changes since the last snapshot).
• Trade-offs:
  • More complex reconstruction logic.
  • Requires a robust diffing algorithm.

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5. JWT in localStorage (Security Risk)

Issue: Storing JWT tokens in localStorage is vulnerable to XSS attacks.

Solution:

• Use HTTP-only cookies with SameSite and Secure flags for better security.
• Trade-offs:
  • Cookies are slightly less flexible for client-side access.
  • Requires proper CORS handling.

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6. API Response Caching (Stale Data Risk)

Issue: Caching API responses for 5 minutes in CloudFront can lead to stale document data.

Solution:

• Use shorter cache TTLs (e.g., 10 seconds) or cache invalidation on document updates.
• Trade-offs:
  • Higher load on the backend if cache misses increase.
  • More complex cache invalidation logic.

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7. Database Read Replicas (Eventual Consistency Issue)

Issue: Read replicas can serve stale data, leading to inconsistencies in real-time collaboration.

Solution:

• Use synchronous replication for critical reads or direct reads from the primary for real-time operations.
• Trade-offs:
  • Higher latency for reads.
  • Reduced scalability for read-heavy workloads.

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8. Document Partitioning by Organization ID (Hot Partitioning Risk)

Issue: If one organization has a very active document, its partition could become a bottleneck.

Solution:

• Use a more granular partitioning strategy (e.g., by document ID or user ID).
• Trade-offs:
  • More complex sharding logic.
  • Potential for uneven distribution if partitioning isn’t balanced.

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9. WebSocket Broadcasts Limited to One Server (Scalability Issue)

Issue: Changes are only broadcast to clients connected to the same server, requiring other servers to poll for updates.

Solution:

• Use a distributed pub/sub system (e.g., Redis Pub/Sub, Kafka) to broadcast changes to all servers.
• Trade-offs:
  • Adds infrastructure complexity.
  • Requires handling message ordering and deduplication.

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10. No Rate Limiting on WebSocket Messages (Abuse Risk)

Issue: A malicious user could spam WebSocket messages, overwhelming the server.

Solution:

• Implement rate limiting per user/connection at the WebSocket level.
• Trade-offs:
  • Adds complexity to the WebSocket handling logic.
  • May require additional tracking of user activity.

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Summary of Key Fixes:

1. Centralize WebSocket connections (e.g., Socket.io + Redis).
2. Replace last-write-wins with OT/CRDTs for proper conflict resolution.
3. Replace polling with pub/sub (e.g., Kafka, PostgreSQL logical replication).
4. Use incremental updates instead of full HTML snapshots.
5. Move JWT to HTTP-only cookies for better security.
6. Shorten or invalidate CloudFront caches to reduce stale data.
7. Use synchronous replication for critical reads to avoid eventual consistency issues.
8. Refine partitioning strategy to avoid hot partitions.
9. Use a distributed pub/sub system for cross-server WebSocket broadcasts.
10. Add WebSocket rate limiting to prevent abuse.

These changes would significantly improve reliability, scalability, and security but come with added complexity and operational overhead.