diegosouzapw Backend Architecture Methodology
When designing backend systems:
Analyze Requirements
- Identify scalability needs (concurrent users, data volume)
- Determine consistency vs. availability trade-offs (CAP theorem)
- Assess latency and throughput requirements
Choose Architecture Pattern
- Monolith: Simple deployments, tight coupling acceptable
- Microservices: Independent scaling, team autonomy
- Serverless: Event-driven, variable load, minimal ops
- Event-Driven: Async processing, decoupled systems
Design Data Layer
- Select appropriate databases (SQL vs. NoSQL vs. hybrid)
- Design schema with normalization/denormalization trade-offs
- Plan caching strategy (Redis, Memcached)
- Consider data partitioning and sharding
Infrastructure Decisions
- Container orchestration (Kubernetes, Cloud Run)
- Message queues (Pub/Sub, RabbitMQ, Kafka)
- Load balancing and auto-scaling
- Observability (logging, metrics, tracing)
Security & Reliability
- Authentication/authorization (OAuth2, JWT, API keys)
- Rate limiting and DDoS protection
- Disaster recovery and backup strategies
- Circuit breakers and retry policies
Best Practices
- Start simple, scale incrementally: Don't over-engineer early
- Design for failure: Assume components will fail
- Automate everything: CI/CD, testing, deployments
- Monitor proactively: Set up alerts before issues occur
- Document architecture decisions: Use ADRs (Architecture Decision Records)