Debug This Architecture

This architecture for a real-time collaborative document editor is a solid starting point, but it contains several potential failure modes, race conditions, and scaling bottlenecks. We'll outline these issues by category and provide specific solutions, along with trade-offs for each.

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🔥 Failure Modes

1. WebSocket Session Stickiness

• Problem: API servers maintain their own WebSocket connections, but a load balancer using round-robin may route a user to a different server upon reconnection, losing context/state.
• Impact: Lost session state, missed updates, or document desyncs.
• Solution:
  • Use sticky sessions via load balancer (e.g., AWS ALB with session affinity).
  • Better: Use a shared pub/sub layer (e.g., Redis Pub/Sub, Apache Kafka) where all servers broadcast/receive real-time updates.
• Trade-off: Adds operational complexity and latency, but ensures state consistency across servers.

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2. Client Clock Drift

• Problem: Last-write-wins with client-side timestamps assumes synchronized clocks. Clock skew can cause updates to be applied out-of-order.
• Impact: Data loss or incorrect overwrites.
• Solution:
  • Use server-generated timestamps.
  • Alternatively, implement vector clocks or operational transforms (OT) / conflict-free replicated data types (CRDTs).
• Trade-off: Server timestamps add round-trip latency. OT/CRDTs are complex to implement but provide precise conflict resolution.

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3. PostgreSQL Polling Delay

• Problem: Servers poll PostgreSQL every 2 seconds for changes. This introduces latency in update propagation and increases DB load.
• Impact: Delayed updates between users on different servers.
• Solution:
  • Replace polling with PostgreSQL LISTEN/NOTIFY or use a real-time change data capture (CDC) system (e.g., Debezium + Kafka).
• Trade-off: Requires infrastructure changes. LISTEN/NOTIFY has limits on payload size and connection count.

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4. Single Point of Failure: PostgreSQL

• Problem: PostgreSQL is a single point of failure for writes, even with read replicas.
• Impact: Downtime or data loss on DB failure.
• Solution:
  • Use managed PostgreSQL with automated failover (e.g., AWS Aurora).
  • Consider sharding or partitioning documents by org ID.
• Trade-off: Sharding adds complexity in query logic and data management.

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5. Redis Failure

• Problem: Redis used for session cache is a potential single point of failure.
• Impact: Session loss, auth issues, degraded performance.
• Solution:
  • Use Redis in a clustered or replicated setup with failover support (e.g., Redis Sentinel or AWS ElastiCache).
• Trade-off: Slightly more expensive and complex.

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6. JWT in localStorage

• Problem: JWTs stored in localStorage are vulnerable to XSS attacks.
• Impact: Token theft, unauthorized access.
• Solution:
  • Store JWTs in HttpOnly, Secure cookies.
  • Use short-lived access tokens with refresh tokens stored securely.
• Trade-off: Slightly more complex auth flow, but significantly more secure.

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⚠️ Race Conditions & Data Consistency Risks

1. Concurrent Edits in Same Paragraph

• Problem: Last-write-wins can cause loss of intermediate edits.
• Impact: Overwrites and inconsistent user experience.
• Solution:
  • Use OT or CRDTs for conflict-free merging of edits.
  • Or implement paragraph-level locking/versioning.
• Trade-off: OT/CRDTs are complex but scalable. Locking can cause UX issues under high contention.

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2. Simultaneous Server Writes

• Problem: Two servers may write to the DB for the same document based on stale state.
• Impact: Write conflicts, inconsistent document state.
• Solution:
  • Use optimistic concurrency control (e.g., version column with each write).
  • Reject or retry conflicting updates.
• Trade-off: Adds complexity to write logic.

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3. CDN Caching API Responses

• Problem: CDN caches API responses for 5 minutes, which may serve stale data (e.g., document state or user permissions).
• Impact: Users see outdated content.
• Solution:
  • Use cache headers appropriately: Cache-Control: no-store or short TTLs for dynamic content.
  • Use cache-busting query params or ETags.
• Trade-off: Reduces CDN cache hit rate.

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🚧 Scaling Bottlenecks

1. WebSocket Scalability

• Problem: Each server maintains its own WebSocket connections, leading to duplication and scalability issues.
• Impact: Hard to scale horizontally, inconsistent state across servers.
• Solution:
  • Use a shared WebSocket backend (e.g., Socket.IO with Redis adapter, or a dedicated message broker like NATS).
  • Or offload WebSocket handling to a service like AWS API Gateway + Lambda or Ably/Pusher.
• Trade-off: Increased architectural complexity, but essential for scale.

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2. Document Save Strategy

• Problem: Saving full HTML snapshots every 30 seconds is storage-intensive and inefficient.
• Impact: Inefficient storage, difficult to support fine-grained undo/history.
• Solution:
  • Save a diff/patch log (event sourcing) and periodically snapshot for recovery.
  • Use versioned documents with granular delta storage.
• Trade-off: More complex, but enables better history, undo, and auditing.

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3. Load Balancer Round-Robin

• Problem: Round-robin doesn’t account for server load or sessions.
• Impact: Uneven load, missed sessions after reconnect.
• Solution:
  • Use a load balancer with health checks and weighted routing or session affinity.
• Trade-off: Slightly more infrastructure config.

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4. Organization-Based Partitioning

• Problem: Partitioning by organization ID is good, but can lead to hotspots for large organizations.
• Impact: Uneven load, potential DB bottlenecks.
• Solution:
  • Further partition data by document ID or user ID within organizations.
  • Use distributed databases if scale demands (e.g., CockroachDB, YugabyteDB).
• Trade-off: Increases data model complexity.

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✅ Summary of Recommendations

Issue	Solution	Trade-off
Client timestamp conflicts	Use server timestamps or CRDT	Complexity vs correctness
Polling DB for changes	Use LISTEN/NOTIFY or CDC	Infra changes
WebSocket scaling	Use Redis Pub/Sub or managed service	Increased infra complexity
JWT in localStorage	Use Secure HttpOnly cookies	Auth flow complexity
Full document snapshots	Store diffs + snapshots	More storage logic
CDN caching API	Use no-store / ETags	Lower cache hit rate
Redis single point	Use Redis cluster	Higher cost
PostgreSQL SPOF	Use managed DB with failover	Cost, setup
Load balancer routing	Use sticky sessions	State management
Org-based partitioning	Add finer-grained partitioning	Complexity

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By addressing these failure modes and bottlenecks with targeted improvements, this architecture can scale more robustly while maintaining real-time collaboration and data integrity.