What is High-Level Design (HLD)?
Mental Model
Connecting isolated components into a resilient, scalable, and observable distributed web.
graph TD
Client[Mobile/Web Client] -->|HTTPS| API[API Gateway]
API -->|gRPC| Service[Core Microservice]
Service -->|Read/Write| Cache[(Redis Cache)]
Service -->|Async| Queue[Kafka Event Bus]
Service -->|Persist| DB[(Primary Database)]
HLD focuses on the system architecture, major components, and how they interact. It's about scalability, availability, and reliability.
Key Pillars:
- Scalability: Can the system handle 10x more users?
- Availability: Is the system always up?
- Consistency: Do all users see the same data?
Real-World Analogy:
Designing a city's plumbing and electrical grid without worrying about the specific fixtures in a single bathroom.
Technical Trade-offs: Messaging Systems
| Pattern | Ordering | Durability | Throughput | Complexity |
|---|---|---|---|---|
| Log-based (Kafka) | Strict (per partition) | High | Very High | High |
| Memory-based (Redis Pub/Sub) | None | Low | High | Very Low |
| Push-based (RabbitMQ) | Fair | Medium | Medium | Medium |
Key Takeaways
- Multi-Region Architecture: Active-Active, Active-Passive, and Consistency Trade-Offs
- System Design: Designing an Online Auction System (eBay Scale)
- The Saga Pattern: Managing Distributed Transactions in NoSQL
Production Readiness Checklist
Before deploying this architecture to a production environment, ensure the following Staff-level criteria are met:
- High Availability: Have we eliminated single points of failure across all layers?
- Observability: Are we exporting structured JSON logs, custom Prometheus metrics, and OpenTelemetry traces?
- Circuit Breaking: Do all synchronous service-to-service calls have timeouts and fallbacks (e.g., via Resilience4j)?
- Idempotency: Can our APIs handle retries safely without causing duplicate side effects?
- Backpressure: Does the system gracefully degrade or return HTTP 429 when resources are saturated?
Read Next
- Multi-Region Architecture: Active-Active, Active-Passive, and Consistency Trade-Offs
- System Design: Designing an Online Auction System (eBay Scale)
- The Saga Pattern: Managing Distributed Transactions in NoSQL
Verbal Interview Script
Interviewer: "How would you ensure high availability and fault tolerance for this specific architecture?"
Candidate: "To achieve 'Five Nines' (99.999%) availability, we must eliminate all Single Points of Failure (SPOF). I would deploy the API Gateway and stateless microservices across multiple Availability Zones (AZs) behind an active-active load balancer. For the data layer, I would use asynchronous replication to a read-replica in a different region for disaster recovery. Furthermore, it's not enough to just deploy redundantly; we must protect the system from cascading failures. I would implement strict timeouts, retry mechanisms with exponential backoff and jitter, and Circuit Breakers (using a library like Resilience4j) on all synchronous network calls between microservices."