Debug This Architecture

Potential Failure Modes, Race Conditions, and Scaling Bottlenecks

1. WebSocket Broadcasting Limited to Same Server

• Issue: Users connected to different servers experience up to 2-second delays, and cross-server updates rely on polling.
• Solution: Implement a pub/sub message broker (e.g., Redis Pub/Sub or Kafka) to broadcast changes to all servers in real-time.
• Trade-offs: Adds latency from broker propagation (~10-50ms), introduces single point of failure unless clustered, and increases system complexity.

2. Last-Write-Wins Conflict Resolution

• Issue: Client clocks may be unsynchronized, leading to incorrect ordering; concurrent edits to the same paragraph can cause data loss.
• Solution: Use a Conflict-Free Replicated Data Type (CRDT) or Operational Transformation (OT) for robust merging without central coordination.
• Trade-offs: CRDTs have higher storage overhead and complexity; OT requires a central server to sequence operations, creating a bottleneck.

3. Database Write Bottleneck

• Issue: Every keystroke is written to PostgreSQL, overwhelming the primary database under high load.
• Solution: Buffer changes in memory or a fast log (e.g., Redis or Kafka) and batch-write to PostgreSQL periodically (e.g., every 1-5 seconds).
• Trade-offs: Increases risk of data loss on server crash (mitigated with write-ahead logging), adds latency for persistence, and complicates recovery.

4. Polling PostgreSQL for Changes

• Issue: Inefficient polling (every 2 seconds) creates unnecessary load and latency; scales poorly with more servers.
• Solution: Replace polling with event-driven notifications using database triggers (e.g., PostgreSQL LISTEN/NOTIFY) or a message broker.
• Trade-offs: Requires changes to PostgreSQL configuration; notifications may not scale across many servers without a broker.

5. CDN Caching Private API Responses

• Issue: Caching API responses (e.g., document content) for 5 minutes can serve stale data to users.
• Solution: Configure Cache-Control headers as private, no-cache for private data; use a separate API endpoint for real-time data via WebSocket.
• Trade-offs: Reduces CDN benefits for dynamic content; requires careful header management.

6. JWT Expiry Without Refresh Mechanism

• Issue: Tokens expire after 24 hours, forcing re-login and disrupting active editing sessions.
• Solution: Implement refresh tokens with secure storage (e.g., HttpOnly cookies) to renew JWTs seamlessly.
• Trade-offs: Increases security attack surface; refresh tokens must be stored securely and rotated.

7. Read Replica Lag for Initial Load

• Issue: Initial document loads via REST API may hit stale replicas, causing users to see outdated content.
• Solution: Serve initial loads via WebSocket (ensuring server has latest state) or read from primary for recent changes; use synchronous replication for replicas.
• Trade-offs: Increases initial load latency; synchronous replication reduces write throughput.

8. Document Partitioning by Organization ID

• Issue: Uneven distribution if one organization has disproportionately many documents; rebalancing is complex.
• Solution: Use consistent hashing (e.g., Ketama) for document sharding across nodes; periodically rebalance with virtual nodes.
• Trade-offs: Requires migration tooling; rebalancing can cause temporary unavailability.

9. WebSocket Reconnection to Different Server

• Issue: Reconnecting to a new server may result in outdated document state, causing sync issues.
• Solution: Enable sticky sessions in load balancer for WebSocket connections, or store document state in a distributed cache (e.g., Redis) accessible by all servers.
• Trade-offs: Sticky sessions can cause uneven load; distributed cache adds latency and complexity.

10. Full HTML Snapshots Every 30 Seconds

• Issue: Periodic full writes of large documents strain the database; may lose up to 30 seconds of changes if server crashes.
• Solution: Store incremental operations and generate snapshots periodically; use a more efficient format (e.g., JSON operations) and compact over time.
• Trade-offs: Increases recovery complexity; requires periodic compaction to prevent log bloat.

11. Redis Session Cache Single Point of Failure

• Issue: Redis outage prevents session validation, blocking all authenticated requests.
• Solution: Use Redis Sentinel or Cluster for high availability; fall back to in-memory validation with JWT (stateless) during outages.
• Trade-offs: Increased infrastructure cost; fallback may reduce security (e.g., cannot revoke compromised tokens).

12. JWT Stored in localStorage (XSS Vulnerability)

• Issue: Tokens accessible via XSS attacks; long expiry (24 hours) increases window of misuse.
• Solution: Store JWT in HttpOnly, Secure cookies; use short-lived tokens (e.g., 15 minutes) with refresh tokens.
• Trade-offs: Requires Secure flag (HTTPS only); refresh token flow adds complexity.

13. Round-Robin Load Balancer for WebSocket

• Issue: Long-lived connections may be routed to different servers, causing state inconsistency.
• Solution: Configure sticky sessions (source IP or cookie-based) in load balancer; use a centralized state store (e.g., Redis) for session data.
• Trade-offs: Sticky sessions can lead to uneven load distribution; centralized state adds latency.

14. Race Condition in Concurrent Writes

• Issue: Simultaneous writes to PostgreSQL from multiple servers may interleave, causing inconsistent document state.
• Solution: Use database-level locking (e.g., SELECT FOR UPDATE) or optimistic concurrency control (version numbers) for writes to the same document.
• Trade-offs: Reduces write throughput; possible deadlocks under high contention.

15. No Idempotency for WebSocket Messages

• Issue: Duplicate or reordered messages due to network issues can cause inconsistent document state.
• Solution: Assign sequence numbers to changes and deduplicate/apply in order at the server; use exactly-once delivery semantics if possible.
• Trade-offs: Increases message overhead; complex state management on client and server.

Summary of Recommended Solutions with Trade-offs

Issue	Solution	Trade-offs
Cross-server broadcast	Pub/Sub message broker	Added latency, complexity
Conflict resolution	CRDT/OT	Implementation complexity, overhead
Write bottleneck	Buffered batch writes	Data loss risk, latency
Polling	Event-driven notifications	PostgreSQL-specific, scaling
CDN caching	Private cache headers	Reduced caching benefits
JWT expiry	Refresh tokens	Security and complexity
Read replica lag	WebSocket initial load	Latency
Partitioning	Consistent hashing	Rebalancing complexity
Reconnection	Sticky sessions/distributed cache	Uneven load, latency
Snapshots	Incremental operations + compaction	Recovery complexity
Redis failure	Redis Sentinel/Cluster	Cost, fallback security
XSS	HttpOnly cookies	HTTPS requirement
Load balancer	Sticky sessions	Uneven load
Race conditions	Database locking	Throughput reduction
Message ordering	Sequence numbers	Overhead

These issues highlight the complexity of building a real-time collaborative editor at scale. While the proposed solutions address specific problems, they often introduce trade-offs in consistency, latency, complexity, or cost. A thorough evaluation of user experience requirements and system constraints is essential when deciding which improvements to implement.