1. Understand Requirements Functional Requirements: What the system should do (features, use-cases). Non-Functional Requirements: Performance, scalability, reliability, security, etc. Constraints: Budget, tech stack, time, team expertise, etc. 2. Define System Goals Clarity on performance metrics: latency, throughput, availability. Decide on trade-offs: CAP theorem (Consistency, Availability, Partition tolerance). Establish SLAs (Service Level Agreements) and SLOs (Objectives). 3. High-Level System Design (HLD) Create a block diagram showing major components: Client API Gateway Load Balancer Application Servers Databases Caches Queues External Services Define data flow between components. 4. Database Design Choose between SQL vs NoSQL. Design schema: normalization, indexing, partitioning. Plan for scalability: sharding, replication. 5. Application Layer Design Choose architecture style: monolith vs microservices. Define API contracts (REST, GraphQL, gRPC). Focus on modularity, separation of concerns, and reusability. 6. Scalability Planning Vertical Scaling: Bigger servers. Horizontal Scaling: More servers. Use Load Balancers for distribution. Apply stateless architecture where possible. 7. Caching Strategy Use caching (e.g., Redis, Memcached) to reduce load. Cache data at: Client-side CDN (Content Delivery Network) Server-side (database query results, sessions) 8. Asynchronous Processing Use message queues (RabbitMQ, Kafka, SQS) for background jobs. Helps in decoupling services and handling spikes in traffic. 9. Database Optimization Use read replicas for read-heavy applications. Use write-ahead logs, connection pooling, and query optimization. 10. Security and Privacy Implement authentication/authorization (OAuth2, JWT). Use HTTPS, data encryption (at rest and in transit). Sanitize inputs and validate data to avoid attacks (SQL injection, XSS). 11. Reliability and Fault Tolerance Design for failure: redundancy, backups, retries with exponential backoff. Use circuit breakers, failover strategies, and auto-scaling. 12. Monitoring and Observability Set up logging, metrics, and tracing (using tools like Prometheus, Grafana, ELK Stack). Alerting for SLA breaches or system failures. 13. Testing and Deployment Unit, Integration, End-to-End tests. CI/CD pipelines for automated builds and deployments. Use containers (Docker) and orchestration tools (Kubernetes). 14. Cost and Maintenance Considerations Optimize resource usage. Regularly audit system performance and update as needed. Plan for technical debt reduction.
