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. Clock Synchronization and Last-Write-Wins (LWW)

Issue:

• LWW relies on client clocks, which can be unsynchronized (e.g., due to timezone differences, device clock drift, or malicious tampering). This can lead to lost edits or incorrect conflict resolution.
• Example: User A (clock 5s behind) and User B (correct clock) edit the same paragraph. User A’s edit might overwrite User B’s even if User B’s edit was later in real time.

Solution:

• Use server-side timestamps (or hybrid logical clocks) instead of client clocks for conflict resolution.
  • Trade-off: Adds latency (requires server round-trip for timestamps), but ensures fairness.
• Operational Transformation (OT) or CRDTs for conflict-free merging.
  • Trade-off: OT/CRDTs are complex to implement and require more storage (e.g., tracking edit operations instead of snapshots).

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2. WebSocket Connection Management

Issue:

• Sticky sessions required: Since each API server maintains its own WebSocket connections, clients must reconnect if their server crashes or is scaled down. This breaks real-time collaboration.
• No cross-server WebSocket broadcast: Changes are only broadcast to clients connected to the same server. Other servers poll PostgreSQL every 2 seconds, introducing lag for users on different servers.
• Memory leaks: Long-lived WebSocket connections can exhaust server memory if not properly managed (e.g., zombie connections).

Solutions:

• Use a pub/sub system (e.g., Redis Pub/Sub, Kafka) for cross-server WebSocket broadcasting.
  • Trade-off: Adds complexity and latency (extra hop), but enables horizontal scaling.
• Shared WebSocket layer: Offload WebSocket management to a dedicated service (e.g., Pusher, Ably, or a custom service using Socket.io with Redis adapter).
  • Trade-off: Adds vendor lock-in or operational overhead, but simplifies scaling.
• Heartbeat and connection cleanup: Implement ping/pong frames and timeouts to detect and close zombie connections.
  • Trade-off: Increases network traffic slightly.

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3. PostgreSQL as a Real-Time Sync Backend

Issue:

• Polling overhead: Servers poll PostgreSQL every 2 seconds, which doesn’t scale well (high CPU/network load for many documents).
• Write contention: Frequent writes to the same document can cause lock contention, slowing down the system.
• Snapshot storage: Storing full HTML snapshots every 30 seconds is inefficient (storage bloat, slow reads/writes).

Solutions:

• Change Data Capture (CDC): Use PostgreSQL’s logical decoding (e.g., Debezium) to stream changes to Redis/Kafka instead of polling.
  • Trade-off: Adds complexity, but reduces polling overhead.
• Optimistic locking: Use a version column to detect conflicts and reject stale writes (instead of LWW).
  • Trade-off: Requires client-side retry logic, but prevents lost updates.
• Delta updates: Store only the diffs (e.g., OT operations or CRDT deltas) instead of full snapshots.
  • Trade-off: More complex to implement, but reduces storage and bandwidth.
• Partitioning: Shard documents by organization_id to distribute load.
  • Trade-off: Adds complexity to queries (e.g., cross-shard joins).

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4. Database Scaling Bottlenecks

Issue:

• Read replicas lag: Replicas may fall behind under high write load, causing stale reads.
• Single-writer bottleneck: PostgreSQL’s primary node can become a bottleneck for writes.
• No caching for real-time data: Redis is only used for sessions, not for document changes.

Solutions:

• Multi-primary or sharded PostgreSQL: Use tools like Citus or PostgreSQL with logical replication to distribute writes.
  • Trade-off: Adds complexity and operational overhead.
• Read replica consistency: Use synchronous replication for critical reads or accept eventual consistency.
  • Trade-off: Synchronous replication adds latency.
• Cache document changes in Redis: Store recent changes in Redis (e.g., as a list of deltas) to reduce PostgreSQL load.
  • Trade-off: Adds cache invalidation complexity.

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5. Authentication and Session Management

Issue:

• JWT in localStorage: Vulnerable to XSS attacks (malicious scripts can steal tokens).
• 24-hour expiry: Long-lived tokens increase the risk of token theft.
• No token revocation: If a token is compromised, it remains valid until expiry.

Solutions:

• Use HTTP-only, Secure, SameSite cookies for JWT storage.
  • Trade-off: Less convenient for mobile apps (requires custom headers), but more secure.
• Short-lived JWTs + refresh tokens: Issue short-lived (e.g., 15-minute) JWTs with long-lived refresh tokens (stored in HTTP-only cookies).
  • Trade-off: Adds complexity (refresh token rotation), but improves security.
• Token revocation: Maintain a Redis set of revoked tokens and check on each request.
  • Trade-off: Adds latency and Redis load.

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6. CDN Caching for API Responses

Issue:

• Stale data: CDN caches API responses for 5 minutes, which can serve outdated document content.
• Cache invalidation: No mechanism to invalidate CDN cache when documents are updated.

Solutions:

• Disable CDN caching for API responses: Only cache static assets (JS/CSS).
  • Trade-off: Increases backend load, but ensures freshness.
• Cache invalidation: Use cache tags or purge API when documents are updated.
  • Trade-off: Adds complexity and cost (e.g., CloudFront invalidations).
• Versioned API responses: Include a Cache-Control: no-cache header or ETag for dynamic content.
  • Trade-off: Reduces caching benefits.

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7. Document Partitioning by Organization ID

Issue:

• Hot partitions: Some organizations may have many more documents/users than others, creating uneven load.
• Cross-partition queries: Queries spanning multiple organizations (e.g., global search) become slow.

Solutions:

• Fine-grained partitioning: Partition by (organization_id, document_id) or use consistent hashing.
  • Trade-off: Adds complexity to routing.
• Dedicated partitions for large orgs: Manually split large organizations into multiple partitions.
  • Trade-off: Requires manual intervention.
• Global secondary indexes: Use a separate table or service (e.g., Elasticsearch) for cross-organization queries.
  • Trade-off: Adds operational overhead.

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8. Load Balancer and API Server Scaling

Issue:

• Round-robin load balancing: Can route WebSocket connections to different servers, breaking real-time sync.
• No health checks: Unhealthy servers may still receive traffic.
• Cold starts: New API servers take time to warm up (e.g., establish WebSocket connections).

Solutions:

• Sticky sessions: Use cookie-based or IP-based session affinity in the load balancer.
  • Trade-off: Reduces load balancing flexibility (e.g., during server failures).
• Health checks: Implement /health endpoints and configure the load balancer to remove unhealthy servers.
  • Trade-off: Adds monitoring overhead.
• Pre-warming: Scale up servers gradually and pre-warm WebSocket connections.
  • Trade-off: Increases cost (idle servers).

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9. Data Loss on Server Crashes

Issue:

• In-memory WebSocket state: If a server crashes, all WebSocket connections and in-flight changes are lost.
• No idempotency: Retrying failed WebSocket messages may cause duplicate changes.

Solutions:

• Persistent WebSocket connections: Use a shared WebSocket layer (e.g., Pusher) to decouple connections from API servers.
  • Trade-off: Adds cost and vendor dependency.
• Idempotency keys: Include a unique change_id with each operation to deduplicate retries.
  • Trade-off: Adds storage overhead (tracking used change_ids).
• Write-ahead logging (WAL): Log changes to disk before applying them to PostgreSQL.
  • Trade-off: Adds latency and complexity.

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10. Network Partitions and Split-Brain

Issue:

• Split-brain: If the network partitions, different servers may accept conflicting changes, leading to divergence.
• No consensus: LWW doesn’t handle network partitions well (e.g., two disconnected groups editing the same document).

Solutions:

• Quorum-based writes: Require a majority of servers to acknowledge a write before committing.
  • Trade-off: Increases latency and reduces availability during partitions.
• Conflict-free Replicated Data Types (CRDTs): Use CRDTs to merge changes automatically after partitions heal.
  • Trade-off: Complex to implement and debug.
• Leader election: Use a consensus protocol (e.g., Raft) to elect a leader for writes.
  • Trade-off: Adds complexity and operational overhead.

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11. Thundering Herd Problem

Issue:

• Polling storms: If many servers poll PostgreSQL for changes every 2 seconds, a single document update can trigger a flood of queries.
• Cache stampedes: If Redis cache expires, multiple servers may recompute the same data simultaneously.

Solutions:

• Exponential backoff: Randomize polling intervals (e.g., 1-3 seconds) to spread load.
  • Trade-off: Increases latency for some users.
• Cache warming: Preemptively refresh Redis cache before expiry.
  • Trade-off: Increases Redis load.
• Dedicated change feed: Use PostgreSQL’s logical decoding to push changes to servers instead of polling.
  • Trade-off: Adds complexity.

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12. Frontend Race Conditions

Issue:

• Out-of-order changes: If WebSocket messages arrive out of order, the document may render incorrectly.
• Optimistic UI updates: Frontend may show changes that are later rejected by the server.

Solutions:

• Sequence numbers: Include a sequence_id with each change to order them correctly.
  • Trade-off: Adds complexity to conflict resolution.
• Rollback mechanism: If a change is rejected, revert the UI and show an error.
  • Trade-off: Poor user experience.

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Summary of Key Improvements

Issue	Solution	Trade-off
Clock sync (LWW)	Server-side timestamps or CRDTs	Latency or complexity
WebSocket scaling	Redis Pub/Sub or shared WebSocket layer	Cost or vendor lock-in
PostgreSQL polling	CDC or logical decoding	Complexity
JWT security	HTTP-only cookies + refresh tokens	Mobile app complexity
CDN caching	Disable for API or use cache tags	Higher backend load
Split-brain	CRDTs or quorum-based writes	Complexity or latency
Thundering herd	Randomized polling or change feed	Latency or complexity

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Recommended Architecture Upgrades

1. Replace LWW with OT/CRDTs for conflict-free merging.
2. Use Redis Pub/Sub for cross-server WebSocket broadcasting.
3. Adopt CDC (e.g., Debezium) to stream PostgreSQL changes to Redis/Kafka.
4. Short-lived JWTs + refresh tokens for better security.
5. Disable CDN caching for API responses or implement cache invalidation.
6. Partition documents by (org_id, doc_id) to distribute load.
7. Use a shared WebSocket layer (e.g., Pusher) to decouple connections from API servers.

These changes would make the system more scalable, resilient, and secure, albeit with added complexity.