System Design
System design is the process of defining the architecture, components, modules, interfaces, and data flows of a system to satisfy specified requirements. It bridges the gap between high-level requirements and concrete implementation by making explicit decisions about scalability, availability, consistency, and performance trade-offs. Good system design anticipates failure modes, models traffic patterns, and decomposes complex domains into independently manageable components. The discipline spans both high-level architectural choices (monolith vs. microservices, synchronous vs. asynchronous communication) and low-level data model and API decisions. At scale, system design choices become increasingly difficult to reverse, making early structural decisions among the most consequential in a project’s lifecycle.
Key Concepts
- Scalability: The ability of a system to handle increased load by adding resources — horizontally (more machines) or vertically (larger machines). Stateless services scale horizontally more easily than stateful ones.
- Availability and Fault Tolerance: Availability is the percentage of time a system is operational. Fault tolerance is achieved through redundancy, health checks, graceful degradation, and circuit breakers.
- CAP Theorem: In a distributed system, you can guarantee at most two of: Consistency, Availability, and Partition Tolerance. Real systems make deliberate trade-offs (e.g., eventual consistency for higher availability).
- Data Partitioning (Sharding): Splitting data across multiple storage nodes by key range, hash, or directory to distribute load and avoid single-node bottlenecks.
- Caching: Storing computed or retrieved results closer to the consumer (in-process, CDN, or distributed cache) to reduce latency and database load.
Common Patterns or Tools
- Load Balancing: Distributing incoming requests across multiple backend instances to prevent overloading any single node. See Load Balancing.
- Event-Driven Architecture: Decoupling producers and consumers via a message broker so components can scale and evolve independently. See Event-Driven Architecture.
- Database Read Replicas: Offloading read traffic to replica nodes while writes flow to a primary, improving read throughput without full horizontal sharding complexity.
- API Gateway: A single entry point for client requests that handles authentication, rate limiting, routing, and protocol translation before forwarding to backend services.