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

Failure Mode: WebSocket Connection Drops

• Problem: If a WebSocket connection drops (e.g., due to network issues), the client may miss updates until it reconnects. The server may also leak resources if it doesn’t detect disconnections promptly.
• Solution:
  • Implement heartbeat/ping-pong (e.g., every 30 seconds) to detect dead connections.
  • Use exponential backoff for reconnection attempts.
  • Trade-off: Increases network overhead and complexity.

Failure Mode: WebSocket Server Failure

• Problem: If an API server crashes, all its WebSocket connections are lost, and clients must reconnect to another server. This can cause temporary desync.
• Solution:
  • Use a WebSocket-aware load balancer (e.g., NGINX with ip_hash or least_conn) to ensure sticky sessions.
  • Store WebSocket connection metadata (e.g., document ID, user ID) in Redis so other servers can take over.
  • Trade-off: Adds complexity to session management and Redis dependency.

Failure Mode: Last-Write-Wins (LWW) Conflict Resolution

• Problem: LWW can lose edits if two users edit the same paragraph simultaneously (e.g., User A types "Hello", User B types "Hi" at the same time—only one wins).
• Solution:
  • Use Operational Transformation (OT) or Conflict-Free Replicated Data Types (CRDTs) for mergeable edits.
  • Trade-off: OT/CRDTs add significant complexity and computational overhead.
  • Alternative: Manual conflict resolution (e.g., show both versions and let users merge).
  • Trade-off: Worse UX but simpler to implement.

Failure Mode: Clock Skew in Timestamps

• Problem: Client clocks may be out of sync, leading to incorrect LWW decisions.
• Solution:
  • Use server-authoritative timestamps (clients send edits, server assigns timestamps).
  • Trade-off: Adds latency (requires an extra round-trip).
  • Alternative: Use logical clocks (e.g., Lamport timestamps) instead of wall-clock time.
  • Trade-off: More complex to implement.

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2. Database Issues

Failure Mode: PostgreSQL Write Bottleneck

• Problem: Every keystroke triggers a write to PostgreSQL, which can’t scale horizontally for writes.
• Solution:
  • Batch writes (e.g., buffer changes for 1-2 seconds before writing to DB).
  • Trade-off: Increases latency for real-time sync.
  • Use a write-ahead log (WAL) (e.g., Kafka) to decouple writes from the database.
  • Trade-off: Adds complexity and operational overhead.

Failure Mode: Polling Overhead

• Problem: Servers poll PostgreSQL every 2 seconds for changes, which doesn’t scale well (high read load).
• Solution:
  • Use PostgreSQL logical replication or change data capture (CDC) (e.g., Debezium) to stream changes to servers.
  • Trade-off: Adds complexity and requires additional infrastructure.
  • Alternative: Redis Pub/Sub for real-time change notifications.
  • Trade-off: Redis becomes a single point of failure.

Failure Mode: Full HTML Snapshots

• Problem: Storing full HTML snapshots every 30 seconds is inefficient (storage bloat, slow reads/writes).
• Solution:
  • Store deltas (changes) instead of full snapshots (e.g., using OT/CRDTs).
  • Trade-off: More complex to reconstruct the document.
  • Compress snapshots (e.g., gzip) or use a binary format (e.g., Protocol Buffers).
  • Trade-off: Adds CPU overhead.

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3. Scaling Bottlenecks

Failure Mode: Load Balancer Bottleneck

• Problem: Round-robin load balancing doesn’t account for WebSocket connections, leading to uneven distribution.
• Solution:
  • Use least-connections or consistent hashing in the load balancer.
  • Trade-off: More complex load-balancing logic.
  • Use a dedicated WebSocket load balancer (e.g., HAProxy, NGINX Plus).
  • Trade-off: Additional cost and complexity.

Failure Mode: Redis Session Cache Bottleneck

• Problem: Redis becomes a single point of failure for session management.
• Solution:
  • Redis Cluster for horizontal scaling.
  • Trade-off: More complex setup and higher operational cost.
  • Multi-write to multiple Redis instances (e.g., using Redis Sentinel).
  • Trade-off: Adds latency and complexity.

Failure Mode: CDN Caching API Responses

• Problem: Caching API responses for 5 minutes can cause stale data (e.g., users see outdated document versions).
• Solution:
  • Shorten CDN TTL (e.g., 30 seconds) or disable caching for dynamic endpoints.
  • Trade-off: Increases origin server load.
  • Use cache invalidation (e.g., purge CDN cache when documents update).
  • Trade-off: Adds complexity to cache management.

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4. Auth and Security Issues

Failure Mode: JWT in localStorage

• Problem: JWTs in localStorage are vulnerable to XSS attacks.
• Solution:
  • Store JWTs in HttpOnly cookies (with Secure and SameSite flags).
  • Trade-off: More complex to implement with WebSockets (requires cookie forwarding).
  • Shorten JWT expiry (e.g., 1 hour) and use refresh tokens.
  • Trade-off: More frequent re-authentication.

Failure Mode: No Rate Limiting

• Problem: Malicious users can spam WebSocket messages or API calls, overwhelming the system.
• Solution:
  • Rate limiting (e.g., 100 edits/minute per user) at the WebSocket and API layers.
  • Trade-off: Adds complexity and may block legitimate users.
  • Use Redis for rate-limiting state (e.g., token bucket algorithm).
  • Trade-off: Redis dependency.

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5. Data Consistency Issues

Failure Mode: Eventual Consistency Between Servers

• Problem: Servers poll PostgreSQL every 2 seconds, leading to temporary inconsistencies (e.g., User A sees an edit before User B).
• Solution:
  • Reduce polling interval (e.g., 500ms) or use CDC (e.g., Debezium) for real-time updates.
  • Trade-off: Increases database load.
  • Use a distributed lock (e.g., Redis Redlock) for critical operations.
  • Trade-off: Adds latency and complexity.

Failure Mode: Document Partitioning by Org ID

• Problem: If an organization has many users editing the same document, the partition becomes a hotspot.
• Solution:
  • Shard by document ID instead of org ID (e.g., consistent hashing).
  • Trade-off: More complex query routing.
  • Use a hybrid approach (e.g., org ID for coarse partitioning, document ID for fine-grained).
  • Trade-off: Adds complexity.

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6. Operational Issues

Failure Mode: No Circuit Breakers

• Problem: If PostgreSQL or Redis fails, the entire system may crash.
• Solution:
  • Implement circuit breakers (e.g., using Hystrix or Resilience4j).
  • Trade-off: Adds latency and complexity.
  • Fallback to read-only mode during outages.
  • Trade-off: Degraded UX.

Failure Mode: No Observability

• Problem: Hard to debug real-time sync issues (e.g., why is User A not seeing User B’s edits?).
• Solution:
  • Distributed tracing (e.g., Jaeger, OpenTelemetry) for WebSocket messages.
  • Trade-off: Adds overhead and complexity.
  • Log WebSocket events (e.g., message sent/received, connection drops).
  • Trade-off: Increases log volume.

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Summary of Key Solutions

Issue	Solution	Trade-off
WebSocket connection drops	Heartbeat + exponential backoff	Network overhead
WebSocket server failure	Sticky sessions + Redis session store	Complexity
LWW conflicts	OT/CRDTs	High complexity
Clock skew	Server-authoritative timestamps	Latency
PostgreSQL write bottleneck	Batch writes + WAL	Latency
Polling overhead	CDC (Debezium) or Redis Pub/Sub	Complexity
Full HTML snapshots	Deltas + compression	Complexity
Load balancer bottleneck	Least-connections + consistent hashing	Complexity
Redis SPOF	Redis Cluster	Operational cost
JWT in localStorage	HttpOnly cookies	WebSocket complexity
No rate limiting	Redis-based rate limiting	Redis dependency
Eventual consistency	CDC or distributed locks	Latency/complexity
Hot partitions	Shard by document ID	Query complexity
No circuit breakers	Hystrix/Resilience4j	Latency
No observability	Distributed tracing	Overhead

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Final Recommendations

1. For real-time sync: Replace LWW with OT/CRDTs (despite complexity) or at least server-authoritative timestamps.
2. For database scaling: Use CDC (Debezium) to stream changes instead of polling.
3. For WebSocket reliability: Implement sticky sessions + Redis session store.
4. For auth security: Move JWTs to HttpOnly cookies.
5. For observability: Add distributed tracing for WebSocket messages.
6. For operational resilience: Add circuit breakers and fallback modes.

This architecture can work for a small-to-medium scale, but for Google Docs-level scale, consider:

• CRDTs for conflict-free merging.
• Dedicated real-time sync service (e.g., Firebase-like).
• Edge caching (e.g., Cloudflare Workers) for low-latency sync.