Debug This Architecture

Comprehensive Analysis of Collaborative Document Editor Architecture

1. Real-time Synchronization Issues

Race Condition: WebSocket Broadcast Gaps

Problem: Changes only broadcast to clients connected to the same server. Clients on other servers must wait up to 2 seconds for polling.

Solution: Implement Redis Pub/Sub for cross-server communication

// When a server receives a change
redisClient.publish(`document:${docId}:changes`, JSON.stringify(change));
redisClient.subscribe(`document:${docId}:changes`);

Trade-offs:

• ✅ Near-instant cross-server propagation
• ✅ Redis handles high throughput well
• ❌ Additional Redis complexity
• ❌ Message ordering not guaranteed (needs sequence numbers)

Race Condition: Concurrent Edits

Problem: Last-write-wins based on client timestamps is unreliable (clock skew, malicious clients).

Solution: Implement Operational Transformation (OT) or CRDTs

// Example OT implementation
class OperationalTransform {
  static transform(op1, op2) {
    if (op1.position <= op2.position) {
      return { ...op2, position: op2.position + op1.length };
    }
    return op2;
  }
}

Trade-offs:

• ✅ Strong consistency guarantees
• ✅ Works with unreliable clocks
• ❌ Complex implementation
• ❌ Higher memory usage for operation history

2. Data Consistency Problems

Race Condition: Document Snapshot Conflicts

Problem: Full HTML snapshots every 30 seconds can overwrite concurrent changes.

Solution: Implement versioned storage with branching

CREATE TABLE document_versions (
  id SERIAL PRIMARY KEY,
  document_id UUID,
  version INT,
  parent_version INT,
  content TEXT,
  created_at TIMESTAMP,
  merged BOOLEAN DEFAULT FALSE
);

Trade-offs:

• ✅ Preserves edit history
• ✅ Enables conflict resolution
• ❌ Storage overhead (store diffs instead)
• ❌ Complexity in merging branches

Race Condition: Database Write Conflicts

Problem: Multiple servers writing to PostgreSQL simultaneously.

Solution: Implement optimistic locking

ALTER TABLE documents ADD COLUMN version INT DEFAULT 1;

UPDATE documents 
SET content = :newContent, version = version + 1
WHERE id = :docId AND version = :expectedVersion;

Trade-offs:

• ✅ Prevents lost updates
• ✅ Simple implementation
• ❌ Requires retry logic
• ❌ Performance degradation under high contention

3. Authentication & Security Vulnerabilities

Security Issue: JWT Token Theft

Problem: 24-hour JWT tokens in localStorage are vulnerable to XSS attacks.

Solution: Implement refresh token rotation

// Use httpOnly cookies for refresh tokens
// Short-lived access tokens (15 minutes)
// Rotate refresh tokens on each use

Trade-offs:

• ✅ Mitigates XSS impact
• ✅ Better security posture
• ❌ More complex auth flow
• ❌ Requires cookie handling

4. Scalability Bottlenecks

Bottleneck: PostgreSQL Write Pressure

Problem: Every keystroke writes to PostgreSQL, creating write amplification.

Solution: Implement write-through cache with batching

// Buffer changes in Redis
const changeBuffer = new Map();

setInterval(async () => {
  if (changeBuffer.size > 0) {
    await batchWriteToPostgres(Array.from(changeBuffer.values()));
    changeBuffer.clear();
  }
}, 1000); // Batch every second

Trade-offs:

• ✅ Reduces DB writes by 10-100x
• ✅ Better latency for users
• ❌ Potential data loss on server crash
• ❌ Additional Redis memory usage

Bottleneck: CDN Caching API Responses

Problem: 5-minute CDN cache on API responses causes stale data.

Solution: Implement cache invalidation strategy

// Use surrogate keys for fine-grained invalidation
res.set('Surrogate-Key', `doc-${docId} org-${orgId}`);
// Invalidate on updates
await fetch(`https://api.cloudfront.com/invalidate`, {
  method: 'POST',
  body: JSON.stringify({ paths: [`/api/documents/${docId}`] })
});

Trade-offs:

• ✅ Immediate updates when needed
• ✅ Still caches when appropriate
• ❌ More complex cache management
• ❌ Invalidation latency

5. Reliability Issues

Failure Mode: WebSocket Connection Loss

Problem: Users lose changes when WebSocket disconnects.

Solution: Implement offline queue with exponential backoff

class OfflineQueue {
  constructor() {
    this.queue = [];
    this.backupInterval = setInterval(() => this.backupToLocal(), 5000);
  }
  
  enqueue(change) {
    this.queue.push(change);
    localStorage.setItem('offline-queue', JSON.stringify(this.queue));
  }
}

Trade-offs:

• ✅ Preserves user work
• ✅ Seamless reconnection
• ❌ localStorage size limitations
• ❌ Complex synchronization logic

Failure Mode: Server Crash During Write

Problem: In-flight writes lost on server crash.

Solution: Implement write-ahead log

// Write to WAL before processing
await redis.lpush(`wal:${docId}`, JSON.stringify({
  id: uuid(),
  operation: operation,
  timestamp: Date.now()
}));

// Process from WAL on startup
const wal = await redis.lrange(`wal:${docId}`, 0, -1);

Trade-offs:

• ✅ Durability guarantee
• ✅ Crash recovery
• ❌ Additional Redis writes
• ❌ Slower write path

6. Performance Optimization Strategies

Bottleneck: Large Document Handling

Problem: Full HTML snapshots become expensive for large documents.

Solution: Implement chunked storage with CRDTs

// Store document as chunks
const CHUNK_SIZE = 1000; // characters
class ChunkedDocument {
  chunks = new Map();
  
  insert(text, position) {
    const chunkIndex = Math.floor(position / CHUNK_SIZE);
    this.chunks.get(chunkIndex).insert(text, position % CHUNK_SIZE);
  }
}

Trade-offs:

• ✅ Scales to large documents
• ✅ Efficient partial updates
• ❌ Complex chunk management
• ❌ Cross-chunk operations expensive

7. Recommended Architecture Refinements

Enhanced Architecture Overview

// Implement a hybrid approach
const EnhancedArchitecture = {
  // Use CRDTs for conflict-free collaboration
  syncStrategy: 'CRDT_with_vector_clocks',
  
  // Event sourcing for audit trail
  storage: 'event_sourcing_with_snapshots',
  
  // Redis Streams for reliable message delivery
  realTime: 'redis_streams_with_consumer_groups',
  
  // Implement circuit breakers
  resilience: 'circuit_breaker_with_fallback',
  
  // Use consistent hashing for document distribution
  scaling: 'consistent_hashing_by_document_id'
};

Priority Implementation Order:

1. Critical (Week 1): Fix timestamp-based conflict resolution
2. High (Week 2): Implement Redis Pub/Sub for real-time sync
3. High (Week 3): Add write-through cache for PostgreSQL
4. Medium (Month 2): Implement proper auth with refresh tokens
5. Medium (Month 2): Add offline queue support
6. Low (Quarter 2): Migrate to event sourcing architecture

This comprehensive approach addresses the most critical issues first while building toward a more robust, scalable architecture.