4 models have responded to the "Debug This Architecture" challenge. Compare their approaches side-by-side on RIVAL. This response is part of RIVAL's open dataset of 5,600+ AI model responses.
Debug This Architecture
Minimax M1's response to the 'Debug This Architecture' challenge.
?Prompt
Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with WebSocket connections - Backend: Node.js API servers behind a load balancer (round-robin) - Database: PostgreSQL for document storage, Redis for session cache - Real-time: Each API server maintains its own WebSocket connections to clients - Sync Strategy: Last-write-wins with timestamps from client clocks - Storage: Documents saved as full HTML snapshots every 30 seconds - Auth: JWT tokens with 24-hour expiry, stored in localStorage - CDN: CloudFront for static assets, also caches API responses for 5 minutes **Data Flow:** 1. User types → change event sent via WebSocket to their connected server 2. Server writes change to PostgreSQL 3. Server broadcasts change to all WebSocket clients connected to THAT server 4. Other servers poll PostgreSQL every 2 seconds for changes 5. Conflict resolution: if two users edit the same paragraph, last timestamp wins **Scaling Plan:** - Horizontal scaling by adding more API servers - Database read replicas for read-heavy operations - Document partitioning by organization ID Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs.
Response
Identified Issues and Solutions
1. Failure Modes:
a. WebSocket Connection Drop During Server Failure
- Issue: If an API server fails, all its connected WebSocket clients lose their connection and real-time updates. Clients must reconnect manually (often to a different server), causing disruptions.
- Solution: Implement automatic WebSocket reconnection with exponential backoff on the client. Use Redis Pub/Sub to broadcast changes across all servers, ensuring disconnected clients receive missed updates when reconnecting.
- Trade-offs: Adds client-side complexity; Pub/Sub introduces ~5-10ms latency and dependency on Redis reliability.
b. Database (PostgreSQL) Unavailability
- Issue: A PostgreSQL outage halts all write operations, breaking the entire system. Polling may also fail if the database is down.
- Solution: Deploy PostgreSQL with read replicas and an automated failover system. Use a write-ahead log (WAL) for data recovery. For critical writes, buffer changes in Redis until the database recovers.
- Trade-offs: Failover adds 30-60s downtime during swaps; buffering in Redis risks data loss if Redis fails.
c. Redis Session Cache Failure
- Issue: Redis downtime invalidates all user sessions (JWT tokens), forcing users to re-login and disrupting active collaborations.
- Solution: Replicate Redis across multiple nodes with a sentinel for automatic failover. Store sessions in PostgreSQL as a fallback (with higher latency).
- Trade-offs: Replication increases complexity and cost; PostgreSQL fallback reduces performance.
2. Race Conditions:
a. Last-Write-Wins Conflicts
- Issue: Conflicting edits (e.g., two users typing in the same paragraph) are resolved solely by timestamps. This can overwrite data if client clocks are desynced or network latency causes slower delivery.
- Solution: Replace timestamps with Operational Transformation (OT) or Conflict-Free Replicated Data Types (CRDTs) for automatic conflict resolution. Use a centralized server to sequence operations.
- Trade-offs: OT/CRDTs increase implementation complexity and bandwidth usage. Server sequencing may limit scalability.
b. Stale Polling in Read Replicas
- Issue: Servers polling PostgreSQL every 2 seconds may propagate stale data if read replicas lag behind the primary database.
- Solution: Replace polling with Redis Pub/Sub. When a server writes to the database, it publishes a message to a channel all servers subscribe to, triggering immediate broadcasts.
- Trade-offs: Pub/Sub adds ~5ms latency and depends on Redis reliability. Requires idempotent message handling.
3. Scaling Bottlenecks:
a. PostgreSQL Write Scalability
- Issue: Frequent document writes (every keystroke) and full snapshots every 30s overload the database. Polling exacerbates read load.
- Solution: Shard documents by organization ID (as planned). Use read replicas for polled queries. Offload snapshots to Amazon S3 (or similar) and store only deltas in PostgreSQL.
- Trade-offs: Sharding complicates data retrieval; S3 introduces eventual consistency (delay in snapshot availability).
b. WebSocket Connection Limits
- Issue: Each server maintains its own WebSocket connections. Under heavy load, servers exhaust memory/CPU, especially for large documents with many concurrent users.
- Solution: Offload WebSockets to a dedicated service (e.g., Socket.IO with Redis adapter) or use a managed service (e.g., Pusher, AWS API Gateway). This isolates real-time traffic from API servers.
- Trade-offs: Adds infrastructure complexity and cost; managed services reduce control but improve scalability.
c. CDN Caching of Dynamic Content
- Issue: Caching API responses for 5 minutes (e.g., document snapshots) serves stale data during updates, breaking real-time collaboration.
- Solution: Exclude dynamic data from CDN caching via
Cache-Control: no-storeheaders. Cache only static assets (e.g., CSS, JS). - Trade-offs: Increases load on API servers but ensures data freshness.
4. Additional Risks:
a. JWT Security & Expiry
- Issue: LocalStorage-stored JWTs are vulnerable to XSS attacks. A 24-hour expiry delays session termination after token invalidation.
- Solution: Store JWTs in HTTP-only cookies (mitigating XSS) and use token refresh endpoints. Shorten expiry to 1 hour and refresh silently.
- Trade-offs: HTTP-only cookies require strict CORS; frequent refreshes increase server load.
b. Full Snapshot Storage
- Issue: Saving full HTML snapshots every 30s wastes storage and bandwidth for large documents. Conflicts in snapshots may cause data loss.
- Solution: Store deltas (diffs) instead of full snapshots. Use content-addressable storage (e.g., S3) with versioning.
- Trade-offs: Diffs require complex merge logic; versioning increases storage overhead.
Summary of Recommendations
| Issue Category | Solution | Trade-off |
|---|---|---|
| WebSocket Drop | Auto-reconnect + Redis Pub/Sub | Latency & Redis dependency |
| Database Failure | Replicas + WAL buffering | Complexity & buffering risk |
| Conflict Resolution | OT/CRDTs + Server sequencing | Implementation complexity |
| PostgreSQL Bottleneck | Sharding + Read replicas + S3 snapshots | Data retrieval complexity |
| WebSocket Limits | Dedicated WebSocket service | Cost & operational overhead |
| Stale CDN Caching | no-store for dynamic data | Increased API server load |
| JWT Security | HTTP-only cookies + short expiry | CORS complexity & refresh overhead |
| Snapshot Storage | Deltas + Versioned S3 storage | Merge logic complexity |
Critical Paths to Implement
- Replace polling with Redis Pub/Sub to eliminate stale data and reduce database load.
- Adopt OT/CRDTs for conflict resolution to prevent data overwrites.
- Shard PostgreSQL by organization ID and offload snapshots to S3.
- Enforce HTTPS and HTTP-only cookies for JWTs to mitigate security risks.
By addressing these issues, the system can achieve robust real-time collaboration while scaling to thousands of concurrent users.
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About MiniMax M1
Capabilities
Categories
Specifications
- Provider
- Minimax
- Released
- 2025-06-17
- Size
- XLARGE
- Parameters
- 456B (45.9B active)
- Context
- 1,000,000 tokens
