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
Gpt 4'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
Failure Modes:
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Single Point of Failure: If PostgreSQL database fails, the whole system will be unavailable.
- Solution: Implement a multi-master or master-slave replication for the PostgreSQL database. This will provide redundancy and high availability.
- Trade-offs: There could be a slight delay in data synchronization between master and slave. Additionally, setting up and managing database replication can increase complexity and cost.
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Session Cache Failure: If Redis fails, all sessions will be lost.
- Solution: Implement Redis Sentinel for high availability and automatic failover.
- Trade-offs: There could be a delay in failover and some sessions might be lost during the switchover.
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JWT Token Expiry: If a JWT token expires during an active session, the user will be logged out.
- Solution: Implement a token refresh mechanism.
- Trade-offs: This could potentially increase the attack surface for token theft.
Race Conditions:
- Last-Write-Wins Strategy: Two users might edit the same paragraph at the same time, leading to potential loss of data.
- Solution: Implement an Operational Transformation or Conflict-free Replicated Data Type (CRDT) algorithm to manage concurrent document changes.
- Trade-offs: These algorithms are complex to implement and can increase the server load.
Scaling Bottlenecks:
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Database Scalability: The PostgreSQL database might become a bottleneck when there are many concurrent writes.
- Solution: Use database sharding to distribute the load across multiple databases.
- Trade-offs: Sharding increases the complexity of the database infrastructure and can lead to difficulties in maintaining data consistency.
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WebSocket Connections: Each server maintains its own WebSocket connections. If a server fails, all its connections will be lost.
- Solution: Implement a messaging queue or pub/sub system to distribute messages across all servers.
- Trade-offs: This adds complexity and could potentially impact performance due to the overhead of the message queue.
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API Server Load: Increased user activity might overload the API servers.
- Solution: Implement auto-scaling for the API servers based on the load.
- Trade-offs: Auto-scaling can lead to cost fluctuations and has a delay in response to changes in load.
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About GPT-4
Capabilities
Categories
Specifications
- Provider
- Openai
- Released
- 2023-05-29
- Size
- XLARGE
- Parameters
- Not disclosed
- Context
- 8,191 tokens
