Key Concepts
| Item | Description |
|---|
Overview | This cheat sheet covers essential concepts and best practices for this topic. |
Best Practices | Follow industry standards and proven patterns for production systems. |
Common Pitfalls | Avoid premature optimization and over-engineering without clear requirements. |
Further Reading | Consult official documentation and system design references for deeper dives. |
Quick Reference
| Concept | When to Use |
|---|
| Horizontal Scaling | Add more machines — works for stateless services |
| Vertical Scaling | Bigger machine — simpler, hits hardware limits |
| Caching | Reduce latency for frequently accessed data |
| Async Processing | Decouple services, handle spikes gracefully |
Implementation Tips
| Item | Description |
|---|
Start Simple | Begin with minimal viable architecture, iterate based on real bottlenecks |
Measure First | Profile before optimizing — do not guess where the bottleneck is |
Design for Failure | Assume everything will fail — build redundancy and graceful degradation |
Document Decisions | Record architecture decisions (ADRs) with context and tradeoffs |
Key Tradeoffs
| Tradeoff | Option A | Option B |
|---|
| Consistency vs Availability | Strong consistency (CP) | High availability (AP) |
| Performance vs Durability | In-memory (fast, volatile) | Disk-backed (slower, durable) |
| Simplicity vs Flexibility | Monolith (simple) | Microservices (flexible) |
| Cost vs Speed | Serverless (pay-per-use) | Provisioned (faster, predictable) |
Pro Tip: Start with a monolith and split when you have clear bounded contexts. Premature distribution creates complexity without the scale to justify it.