Public Solution
Chat App 1 - Team Messaging
Chat App 1 - Team Messaging solution gives a production-minded baseline for this prompt. You get a concise requirements recap, a component-by-component architecture breakdown, explicit tradeoffs for latency, availability, cost, and complexity, plus failure mitigations and scoring rationale so you can benchmark your own design quickly.
Requirements Recap
| Requirement | Target |
|---|---|
| Total users | ~250,000 |
| Concurrent online users | ~75,000 |
| Messages per day | ~5,000,000 |
| File upload limit | 25 MB |
| Message delivery latency | < 500 ms |
| Presence update latency | < 10 seconds |
| Availability target | 99.9% |
Architecture Breakdown (Component-by-Component)
1. Mobile Clients
Represents mobile user traffic and request patterns.
Acts as an entry layer that routes traffic into the rest of the system.
2. Load Balancer
Distributes requests across healthy backend instances.
Bridges 1 incoming flow to 1 downstream dependency.
3. API Gateway
Handles api gateway responsibilities in this design.
Bridges 1 incoming flow to 2 downstream dependencies.
4. Auth Service
Verifies identity, sessions, and authorization decisions.
Acts as a sink or system-of-record endpoint in the architecture flow.
5. API Service
Runs core business logic and orchestrates downstream calls.
Bridges 1 incoming flow to 2 downstream dependencies.
6. Realtime Bus
Handles pub sub responsibilities in this design.
Acts as a sink or system-of-record endpoint in the architecture flow.
7. Primary NoSQL DB
Stores high-scale data with flexible schema and throughput.
Acts as a sink or system-of-record endpoint in the architecture flow.
Tradeoffs (Latency / Availability / Cost / Complexity)
| Decision | Latency | Availability | Cost | Complexity |
|---|---|---|---|---|
| Keep the request path focused on core business operations | Shorter synchronous path keeps average response time stable | Fewer inline dependencies reduce immediate failure blast radius | Avoids unnecessary infrastructure in the first rollout | Lower coordination overhead for small teams |
| Keep a clear system of record for transactional writes | Predictable read/write behavior with indexed access | Strong correctness with managed backup and recovery | Storage and IOPS spend grows with write volume | Schema evolution and query tuning required |
Failure Modes and Mitigations
Failure mode: Primary datastore saturation increases latency and write timeouts
Mitigation: Tune indexes, add read offload where valid, and cap expensive query classes.
Why This Scores Well
- Availability (35%): A compact request path limits synchronous dependencies that can fail in-line.
- Latency (20%): The design keeps hot reads close to users and reduces expensive origin round-trips.
- Resilience (25%): Clear role separation and bounded dependencies reduce cascading-failure risk.
- Cost Efficiency (10%) + Simplicity (10%): Higher complexity is scoped to requirements that actually demand scale or stronger fault tolerance.
Next Step CTA
Validate this architecture by solving the prompt yourself, then practice the highest-leverage component in a guided lab and topic hub.
FAQ
What should I change first if traffic doubles?
Profile the bottleneck first, then scale the hot path component (usually compute, cache, or read path) before adding new system layers.
Why is WebSockets emphasized in this solution?
It is the highest-leverage topic for this challenge constraints and directly improves score-impacting metrics like latency, availability, or resilience.
How do I validate this architecture quickly?
Run the same challenge in the simulator, compare score breakdown metrics, and then test one tradeoff change at a time.