Debug This Architecture

Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in this architecture, along with proposed solutions and their trade-offs:

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1. Real-Time Sync Issues

A. WebSocket Connection Failures

• Problem: If a WebSocket connection drops (e.g., due to network issues), the client may miss updates until it reconnects. The server may not detect the disconnection immediately (e.g., due to TCP keepalive timeouts).
• Solution:
  • Implement exponential backoff reconnection on the client with a max retry limit.
  • Use heartbeat messages (ping/pong) every 30 seconds to detect dead connections.
  • Trade-off: Increases client-side complexity and network overhead.

B. WebSocket Server Failures

• Problem: If an API server crashes, all WebSocket connections on that server are lost. Clients must reconnect to another server, but may miss updates during the failover.
• Solution:
  • Use a WebSocket-aware load balancer (e.g., AWS ALB with WebSocket support) to route connections to healthy servers.
  • Implement session affinity (sticky sessions) so clients reconnect to the same server if possible.
  • Trade-off: Sticky sessions reduce load balancing flexibility and may lead to uneven server loads.

C. Cross-Server Sync Latency

• Problem: Servers poll PostgreSQL every 2 seconds for changes, creating a 2-second sync delay between servers. This can cause conflicts if two users on different servers edit the same paragraph.
• Solution:
  • Replace polling with PostgreSQL logical replication or CDC (Change Data Capture) to stream changes to all servers in real-time.
  • Use Redis Pub/Sub for cross-server broadcast of changes (each server subscribes to a Redis channel for document updates).
  • Trade-off:
    • CDC adds complexity to PostgreSQL setup.
    • Redis Pub/Sub is fast but not persistent (messages lost if Redis crashes).

D. Clock Skew in Last-Write-Wins (LWW)

• Problem: LWW relies on client timestamps, which can be skewed (e.g., due to incorrect system clocks). This can lead to lost edits if a client with a slow clock sends a change after a newer one.
• Solution:
  • Use server-side timestamps (from a centralized NTP-synchronized clock) instead of client timestamps.
  • Alternatively, use operational transformation (OT) or CRDTs (Conflict-Free Replicated Data Types) for conflict resolution.
  • Trade-off:
    • Server-side timestamps add latency (client must wait for server ack).
    • OT/CRDTs are complex to implement and may increase storage overhead.

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2. Database Bottlenecks

A. PostgreSQL Write Contention

• Problem: Every keystroke triggers a write to PostgreSQL, leading to high write load and potential lock contention.
• Solution:
  • Batch writes (e.g., coalesce changes for 100ms before writing to DB).
  • Use optimistic locking (e.g., UPDATE ... WHERE version = X) to avoid lost updates.
  • Trade-off:
    • Batching increases latency for real-time sync.
    • Optimistic locking requires retry logic on conflicts.

B. Full HTML Snapshots Every 30 Seconds

• Problem: Storing full HTML snapshots is inefficient (large storage, slow writes) and doesn’t scale for large documents.
• Solution:
  • Store deltas (changes) instead of full snapshots (e.g., using a diff algorithm like google-diff-match-patch).
  • Use PostgreSQL’s JSONB or a dedicated document store (e.g., MongoDB) for structured deltas.
  • Trade-off:
    • Deltas require more complex conflict resolution.
    • Reconstructing documents from deltas may be slower.

C. Read Replicas Lag

• Problem: Read replicas may lag behind the primary, causing stale data to be served to clients.
• Solution:
  • Use synchronous replication for critical reads (e.g., synchronous_commit = remote_apply in PostgreSQL).
  • Implement client-side caching (e.g., Redis) for frequently accessed documents.
  • Trade-off:
    • Synchronous replication reduces write performance.
    • Client-side caching adds complexity and staleness risks.

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3. Authentication and Security

A. JWT in localStorage

• Problem: JWTs in localStorage are vulnerable to XSS attacks. If an attacker injects JavaScript, they can steal the token.
• Solution:
  • Store JWTs in HTTP-only, Secure, SameSite cookies instead of localStorage.
  • Use short-lived JWTs (e.g., 15-minute expiry) with refresh tokens stored in HTTP-only cookies.
  • Trade-off:
    • Cookies are vulnerable to CSRF (mitigated with SameSite and CSRF tokens).
    • Refresh tokens add complexity to the auth flow.

B. No Token Revocation

• Problem: JWTs are valid until expiry (24 hours), so compromised tokens cannot be revoked.
• Solution:
  • Implement a token denylist (e.g., in Redis) for revoked tokens.
  • Use short-lived JWTs (e.g., 15 minutes) with refresh tokens.
  • Trade-off:
    • Denylist adds latency to token validation.
    • Refresh tokens require additional storage and logic.

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4. Scaling Bottlenecks

A. WebSocket Connection Limits

• Problem: Each API server maintains WebSocket connections, which consume memory and file descriptors. A single server may hit OS limits (e.g., ulimit -n).
• Solution:
  • Use connection pooling (e.g., ws library with connection reuse).
  • Offload WebSocket connections to a dedicated service (e.g., Pusher, Ably, or a custom WebSocket cluster).
  • Trade-off:
    • Dedicated services add cost and vendor lock-in.
    • Custom clusters require operational overhead.

B. PostgreSQL Single Point of Failure

• Problem: If the primary PostgreSQL instance fails, writes are blocked until failover completes.
• Solution:
  • Use PostgreSQL streaming replication with automatic failover (e.g., Patroni + etcd).
  • Deploy in a multi-AZ setup (e.g., AWS RDS Multi-AZ).
  • Trade-off:
    • Multi-AZ increases cost and complexity.
    • Failover may take seconds to minutes.

C. Redis as a Single Point of Failure

• Problem: Redis is used for session cache and Pub/Sub. If Redis fails, cross-server sync breaks.
• Solution:
  • Use Redis Cluster for high availability.
  • Fall back to PostgreSQL polling if Redis is unavailable (degraded mode).
  • Trade-off:
    • Redis Cluster adds complexity.
    • Fallback to polling increases latency.

D. CDN Caching API Responses

• Problem: CDN caches API responses for 5 minutes, which can serve stale data (e.g., outdated document versions).
• Solution:
  • Disable CDN caching for API responses (only cache static assets).
  • Use cache-control headers (e.g., no-cache for dynamic endpoints).
  • Trade-off:
    • Disabling caching reduces CDN benefits for API traffic.

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5. Race Conditions

A. Concurrent Edits on the Same Paragraph

• Problem: Two users on different servers edit the same paragraph simultaneously. The last write (by timestamp) wins, but the "losing" edit is silently discarded.
• Solution:
  • Use operational transformation (OT) or CRDTs to merge concurrent edits.
  • Implement conflict resolution at the paragraph level (e.g., merge changes if they don’t overlap).
  • Trade-off:
    • OT/CRDTs are complex to implement.
    • Paragraph-level merging may not handle all cases (e.g., overlapping deletions).

B. Lost Updates During Server Failover

• Problem: If a server crashes after receiving a change but before writing to PostgreSQL, the change is lost.
• Solution:
  • Acknowledge changes only after PostgreSQL write (not just WebSocket send).
  • Use write-ahead logging (WAL) in PostgreSQL for durability.
  • Trade-off:
    • Acknowledging after DB write increases latency.
    • WAL adds storage overhead.

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6. Other Issues

A. No Offline Support

• Problem: If a user’s internet disconnects, they cannot edit the document until reconnecting.
• Solution:
  • Implement client-side offline editing with a local copy of the document.
  • Sync changes when reconnecting (using a conflict-free merge strategy).
  • Trade-off:
    • Offline support adds complexity to the client and sync logic.

B. No Document Versioning

• Problem: If a user accidentally deletes content, there’s no way to recover it (only full snapshots every 30 seconds).
• Solution:
  • Store every change as a delta in PostgreSQL with timestamps.
  • Implement document versioning (e.g., store a new version on every save).
  • Trade-off:
    • Versioning increases storage costs.
    • Reconstructing old versions may be slow.

C. No Rate Limiting

• Problem: A malicious user could spam the server with changes, causing high load.
• Solution:
  • Implement rate limiting (e.g., 100 changes/minute per user).
  • Use Redis to track rate limits (e.g., INCR + EXPIRE).
  • Trade-off:
    • Rate limiting may block legitimate users during bursts.

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Summary of Key Solutions

Issue	Solution	Trade-off
WebSocket disconnections	Heartbeat + exponential backoff	Network overhead
Cross-server sync latency	Redis Pub/Sub or PostgreSQL CDC	Complexity, Redis persistence
Clock skew in LWW	Server-side timestamps or OT/CRDTs	Latency or implementation complexity
PostgreSQL write contention	Batch writes + optimistic locking	Latency or retry logic
Full HTML snapshots	Store deltas instead	Complex conflict resolution
JWT in localStorage	HTTP-only cookies + refresh tokens	CSRF risk
WebSocket connection limits	Dedicated WebSocket service	Cost, vendor lock-in
Concurrent edits	OT/CRDTs or paragraph-level merging	Complexity
PostgreSQL SPOF	Multi-AZ setup + automatic failover	Cost, complexity
No offline support	Client-side offline editing	Complex sync logic

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Recommendations for Improvement

1. Replace LWW with OT/CRDTs for better conflict resolution.
2. Use Redis Pub/Sub for cross-server sync (instead of PostgreSQL polling).
3. Store deltas instead of full snapshots to reduce storage and improve performance.
4. Move JWTs to HTTP-only cookies to mitigate XSS.
5. Implement document versioning for recovery.
6. Use a dedicated WebSocket service (e.g., Pusher) to offload connection management.
7. Add rate limiting to prevent abuse.

This architecture is workable but has several scalability and reliability risks. Addressing these issues will make it more robust for production use.