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
Gemini 2 5 Flash Preview 09 2025'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
This architecture, while functional, has several critical flaws, particularly in its real-time synchronization, data consistency, and scaling strategy.
Here is a detailed breakdown of the potential failure modes, race conditions, and bottlenecks, along with proposed solutions.
I. Real-Time Synchronization and Data Consistency Issues
The core weakness is the reliance on Last-Write-Wins (LWW) with client-side timestamps and the server-polling strategy.
| Issue Type | Failure Mode / Race Condition | Explanation | Proposed Solution & Trade-offs |
|---|---|---|---|
| Race Condition | Inconsistent State Due to Client Clock Drift | If User A (with a fast clock) and User B (with a slow clock) edit the same section, the LWW strategy will incorrectly favor User A's change, even if User B's change occurred later in real-time. | Server-Side Timestamping (Preferred): The server applies the timestamp upon receiving the change. Trade-offs: Simple to implement. Requires clients to trust the server's time. Still susceptible to network latency variation (changes arriving out of order). |
| Data Consistency | Missing Updates Due to Polling Gap | Server 1 receives a change, writes to PostgreSQL, and broadcasts to its clients. Server 2 polls PostgreSQL every 2 seconds. A client connected to Server 2 might miss the update for up to 2 seconds, leading to a stale view and potential LWW conflicts when they eventually submit a change based on old data. | Centralized Pub/Sub for Inter-Server Sync: Use a distributed message queue (e.g., Kafka, RabbitMQ) or a dedicated pub/sub layer (e.g., Redis Pub/Sub) to broadcast changes instantly across all API servers. Trade-offs: Adds complexity and another critical component (the message broker). Significantly reduces synchronization latency. |
| Data Consistency | Non-Deterministic Conflict Resolution | Using LWW on full paragraph edits is coarse-grained. A user might lose significant, non-overlapping work if another user saves a trivial change to the same paragraph slightly later. | Operational Transformation (OT) or Conflict-Free Replicated Data Types (CRDTs): Move away from LWW snapshots. Use OT or CRDTs to merge fine-grained operations (insert character, delete character) mathematically, ensuring all collaborators converge to the same state regardless of the order of arrival. Trade-offs: Highly complex to implement and debug. Required for a professional collaborative editor. |
| Race Condition | Broadcast Inconsistency (Split-Brain) | Server 1 writes to PostgreSQL, broadcasts to its 10 clients. Before Server 2 polls, Server 1 crashes. The change is in the DB, but 90% of the clients (on other servers) don't see the change until Server 2 polls 2 seconds later. | Requires Centralized Pub/Sub (Solution to Polling Gap): A centralized message broker ensures that if S1 successfully commits to the DB, it also commits to the message queue, guaranteeing the broadcast will happen even if S1 fails immediately afterward. |
| Failure Mode | Database Write Bottleneck | Every single character typed by any user results in a PostgreSQL write. This high-frequency write load will quickly overwhelm a single PostgreSQL instance, especially during peak usage. | Batching Writes and Asynchronous Persistence: The API server should buffer changes locally for a very short period (e.g., 50ms) and submit them as a single batched transaction to PostgreSQL. Use a durable message queue (e.g., Kafka) as the primary write buffer, allowing the DB writer process to consume and persist changes asynchronously. Trade-offs: Increases complexity. Improves write throughput dramatically but slightly increases the latency of final persistence. |
II. Architectural and Scaling Bottlenecks
| Issue Type | Bottleneck / Failure Mode | Explanation | Proposed Solution & Trade-offs |
|---|---|---|---|
| Scaling Bottleneck | Sticky Sessions (Load Balancer) | While not explicitly stated, if the load balancer uses round-robin without any persistence mechanism, a client might hit a different API server on a reconnect, forcing a new WebSocket connection and potentially losing context. | Use a Layer 7 Load Balancer with Consistent Hashing or Sticky Sessions: Ensure that a client is consistently routed to the same API server based on its ID (if possible) or use a sticky session duration long enough to cover typical connection drops. Trade-offs: Sticky sessions can lead to uneven load distribution if one server has a few very high-traffic users. |
| Failure Mode | Document Snapshotting Overhead | Saving the full HTML snapshot every 30 seconds for every active document is extremely storage-intensive and generates massive write spikes every half-minute. | Differential Versioning / Change Logs: Instead of full snapshots, store a chronological log of the tiny operational changes (the diffs). Periodically (e.g., every 5 minutes or 1000 operations), the server can generate a full "base" snapshot from the logs. Trade-offs: Reduces storage and write spikes significantly. Requires complex logic to rebuild the document state from the logs. |
| Scaling Bottleneck | Redis Single Point of Failure (SPOF) | Redis is used for session caching. If the single Redis instance fails, all users are logged out, forcing re-authentication and potential service outage. | Redis Sentinel or Cluster: Implement a highly available Redis setup using Sentinel (for automatic failover) or a full Redis Cluster (for sharding and high availability). Trade-offs: Increases operational complexity but eliminates the SPOF risk for session management. |
| Failure Mode | Loss of WebSocket State on Server Restart | WebSocket connections are stateful. If an API server restarts, all connected clients are dropped and must reconnect, causing a temporary service disruption and increased load on the remaining servers. | Graceful Shutdown and Connection Draining: Implement a process where the server stops accepting new connections, allows existing connections to finish their current transactions, and sends a "reconnect soon" signal before shutting down. Use a load balancer health check to quickly remove the failing server from the rotation. Trade-offs: Reduces abrupt disconnects but increases complexity in deployment pipelines. |
| Scaling Bottleneck | Document Partitioning Strategy | Partitioning only by organization ID means that very large organizations (e.g., 100,000 users) could still overwhelm a single database shard. | Compound Partitioning: Partition by Organization ID first, then by Document ID. This distributes the load for extremely active organizations across multiple shards. Trade-offs: Increases complexity of routing and cross-shard queries (if needed), but greatly enhances maximum scaling capacity. |
III. Authentication and Frontend Issues
| Issue Type | Failure Mode / Security Risk | Explanation | Proposed Solution & Trade-offs |
|---|---|---|---|
| Security Risk | JWT in localStorage | Storing JWTs in localStorage makes them highly vulnerable to XSS (Cross-Site Scripting) attacks, as any malicious script injected into the page can easily access and steal the token. | Use HttpOnly Cookies: Store the JWT token (or a reference token) in an HttpOnly cookie. This prevents client-side JavaScript from accessing the cookie, mitigating XSS risks. Trade-offs: Requires careful handling of CSRF (Cross-Site Request Forgery) protection (e.g., using a CSRF token in headers). |
| Failure Mode | CDN Caching of API Responses | Caching API responses for 5 minutes is fundamentally incompatible with a real-time system. If an API endpoint is hit directly (e.g., to fetch the document's initial state), that state could be 5 minutes old. | Disable Caching for Dynamic/API Endpoints: Ensure CloudFront only caches static assets (/images, /js, /css). API endpoints (e.g., /api/v1/docs/) must have caching explicitly disabled or set Cache-Control: no-store, max-age=0. Trade-offs: Reduces CDN cost savings for dynamic content, but ensures data freshness. |
| Failure Mode | Front-end Error Handling | If the client's WebSocket connection drops, the application must handle the reconnection gracefully, resending any operations that were queued during the disconnect. | Client-Side Operation Queue and Sequence Numbers: The client should maintain a queue of pending operations and use a global sequence number (or version number) obtained from the server. Upon reconnection, the client sends its last known sequence number, and the server replays any missed operations. Trade-offs: Adds significant complexity to the frontend state management. Essential for robust real-time applications. |
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About Google: Gemini 2.5 Flash Preview 09-2025
Capabilities
Categories
Specifications
- Provider
- Released
- 2025-09-25
- Size
- XLARGE
- Context
- 1,048,576 tokens
