Debug This Architecture

Comprehensive Analysis of the Collaborative Editor Architecture

1. Critical Race Conditions

1.1 WebSocket Connection Imbalance

Issue: With round-robin load balancing, users editing the same document might connect to different servers, creating significant sync delays.

Scenario:

• User A connects to Server 1, User B to Server 2
• User A's changes take 2-4 seconds to reach User B (via polling)
• During this window, both users see different document states

Solution: Implement Document-Affinity Routing

• Use consistent hashing (document ID) to route WebSocket connections
• All users editing document X connect to the same server cluster
• Trade-off: Hot documents could overload specific servers
• Mitigation: Implement document sharding with automatic rebalancing

1.2 Last-Write-Wins with Client Timestamps

Issue: Client clocks are unreliable, causing incorrect conflict resolution.

Scenario:

• User A's clock is 5 minutes behind
• User B makes edit at 10:00:00 (real time)
• User A makes edit at 09:55:00 (but their timestamp shows 10:05:00)
• User A's outdated edit incorrectly wins

Solution: Implement Vector Clocks + Server Sequencing

• Use hybrid approach: client timestamps for ordering, server sequence numbers for authority
• Each operation gets (clientId, clientTimestamp, serverSequenceNumber)
• Conflict resolution: serverSequenceNumber has final authority
• Trade-off: Slightly more complex, but eliminates client clock dependency

2. Data Consistency Issues

2.1 Partial Update Visibility

Issue: Broadcasting only to connected WebSocket clients creates inconsistent views.

Scenario:

• Server 1 has 3 users connected to document X
• Server 2 has 2 users connected to document X
• User on Server 1 makes change
• Only 3 users see it immediately, 2 users wait 2+ seconds

Solution: Operational Transform (OT) with Redis Pub/Sub

• Implement OT algorithm for true real-time collaboration
• Use Redis Pub/Sub for cross-server real-time broadcasting
• Each operation: transform → apply → broadcast
• Trade-off: Complex algorithm, but provides Google Docs-level consistency

3. Storage Bottlenecks

3.1 Full HTML Snapshots

Issue: Storing full HTML every 30 seconds is extremely inefficient.

Scenario:

• 10,000 active documents
• Average document size: 50KB
• Storage: 10,000 × 50KB × 2 snapshots/min × 60 × 24 = 1.44TB/day

Solution: Operational Log + Delta Compression

• Store operation log instead of full snapshots
• Compress consecutive operations by same user
• Periodic compaction: create snapshot every 1000 operations
• Trade-off: More complex recovery, but 100x storage reduction

3.2 PostgreSQL Write Bottleneck

Issue: Every keystroke hits PostgreSQL, creating write pressure.

Scenario:

• 1000 concurrent editors
• 300 keystrokes/minute/user = 300,000 writes/minute
• PostgreSQL chokes on sustained write rate

Solution: Write-Through Cache Pattern

• Redis as write buffer: SET doc:operations:<docId> <operations>
• Batch flush to PostgreSQL every 5 seconds
• Use Redis Streams for operation log
• Trade-off: Potential 5-second data loss window
• Mitigation: Implement write-ahead log in Redis persistence

4. Authentication & Security Flaws

4.1 JWT in localStorage

Issue: XSS attacks can steal tokens, 24-hour expiry is too long.

Solution: Token Rotation + HttpOnly Cookies

• Use refresh tokens (7 days) + access tokens (15 minutes)
• Store refresh token in HttpOnly cookie
• Implement silent refresh via refresh token rotation
• Trade-off: More complex auth flow, but XSS-resistant

4.2 CDN Caching API Responses

Issue: 5-minute cache on API responses breaks real-time collaboration.

Scenario:

• User adds paragraph
• API response cached for 5 minutes
• User refreshes page, sees stale content

Solution: Cache-Control Headers

• Use Cache-Control: no-cache, no-store, must-revalidate for API
• Cache only static assets via CDN
• Implement API response caching at application level (Redis) with TTL < 1 second
• Trade-off: More CDN origin hits, but consistent data

5. Scaling Bottlenecks

5.1 Connection Memory Overhead

Issue: Each WebSocket connection consumes ~2MB memory.

Scenario:

• 10,000 concurrent connections per server
• Memory usage: 20GB just for WebSockets
• Server becomes memory-bound

Solution: WebSocket Connection Pooling

• Implement connection multiplexing: 1 WebSocket per user, subscribe to multiple documents
• Use Redis for document subscription state
• Switch to uWebSockets.js (C++ WebSocket implementation)
• Trade-off: More complex subscription management, 10x memory reduction

5.2 Database Connection Limits

Issue: PostgreSQL has hard connection limits (typically 100-200).

Scenario:

• 10 API servers × 100 connections each = 1000 connections
• PostgreSQL refuses connections, system crashes

Solution: Connection Pooling + PgBouncer

• Implement application-level connection pooling (max 20 per server)
• Deploy PgBouncer in transaction pooling mode
• Use prepared statements for operation queries
• Trade-off: Slight latency increase, but scales to 1000+ servers

6. Network Partitions & Failures

6.1 Redis Single Point of Failure

Issue: Redis failure breaks session cache and inter-server sync.

Solution: Redis Sentinel + Partition Tolerance

• Deploy Redis Sentinel for automatic failover
• Implement circuit breaker pattern for Redis connections
• Fallback to PostgreSQL for session storage during Redis failure
• Trade-off: Complex deployment, but high availability

6.2 WebSocket Reconnection Storm

Issue: Server restart causes thousands of simultaneous reconnections.

Solution: Exponential Backoff + Sticky Sessions

• Implement exponential backoff: 1s → 2s → 4s → 8s...
• Use sticky sessions (cookies) to reconnect to same server
• Implement connection draining during deploys
• Trade-off: Slower recovery, but prevents thundering herd

7. Proposed Enhanced Architecture

graph TD
    A[React SPA] --> B[API Gateway<br/>with WS routing]
    B --> C[Document Router<br/>Consistent Hashing]
    C --> D[Node.js Server<br/>Document Shard 1]
    C --> E[Node.js Server<br/>Document Shard 2]
    
    D --> F[Redis Stream<br/>Operations Log]
    E --> F
    
    F --> G[Compaction Service<br/>PostgreSQL Writer]
    G --> H[PostgreSQL<br/>Document Store]
    
    D --> I[Redis Pub/Sub<br/>Cross-server sync]
    E --> I
    
    J[CDN] --> K[Static Assets<br/>No API caching]

Implementation Priority Matrix

Priority	Issue	Solution	Effort	Impact
P0	Client timestamps	Vector clocks + server sequencing	Medium	Critical
P0	Storage inefficiency	Operational log + delta compression	High	Critical
P1	Cross-server sync	Redis Pub/Sub for real-time	Medium	High
P1	JWT security	Token rotation + HttpOnly	Medium	High
P2	Connection balancing	Document affinity routing	High	Medium
P2	Database writes	Write-through cache pattern	Medium	Medium
P3	CDN caching	Cache-control headers	Low	Low

This architecture transforms the system from a brittle prototype to a production-ready, Google Docs-level collaborative editor.