Basics of System Design
System design is the process of determining the overall architecture and structure of a software system. It involves planning how different components will interact to satisfy both functional requirements and non-functional requirements such as scalability, reliability, and performance.
Beyond user needs, system design also considers business goals, infrastructure constraints, and engineering trade-offs. A well-designed system should be scalable, reliable, and performant while using resources efficiently.
Scaling
Scaling refers to a system’s ability to handle increased workload or traffic. To scale a system, we can either add more capacity to a single machine (vertical scaling) or add more machines to distribute the load (horizontal scaling).
Vertical scaling involves upgrading a server’s CPU, RAM, or storage to increase its capacity. Horizontal scaling involves adding multiple machines or instances and distributing traffic across them, which helps improve capacity and also provides better availability and fault tolerance if a node fails.
Both approaches have trade-offs, and the choice depends on cost, infrastructure constraints, and the expected growth of the system. Generally vertical scaling has not so good results while horizontal scaling is more practical due to cloud infra.
Micro services
Microservices is an architectural style where a software system is decomposed into small, independent services. Each service focuses on a specific domain or task and communicates with other services through APIs or messaging. This allows teams to deploy, scale, and update services independently. Microservices help improve scalability, fault isolation, and development flexibility, but they also introduce challenges in networking, observability, and data consistency.
Distributed Systems
Distributed systems are systems in which multiple independent computers communicate over a network and cooperate to act as a single logical system. They help improve scalability, throughput, and fault tolerance by distributing workload and replicating components across machines. If one machine fails, others can continue serving requests, improving availability and reducing single points of failure. Distributed systems can also run tasks in parallel, increasing capacity, but they introduce challenges related to consistency, coordination, and network reliability.
CAP theorem describes a fundamental trade-off in distributed data systems. It states that during a network partition, a system must choose between consistency and availability, since partition tolerance must be maintained for the system to continue operating.
In CP systems, consistency is prioritized: all nodes must agree on data before responding, even if that means rejecting some requests. In AP systems, availability is prioritized: nodes keep serving requests independently, even if they temporarily return stale data.
Real-world distributed databases make intentional choices along this CAP trade-off depending on the use case.
Decoupling
Decoupling is the practice of reducing the dependencies between different modules of the system so that any change in one module does not affect the other. If one of the services goes down, the other is least affected by it. This increases the ability of teams to work on different modules in parallel, reducing development time and increasing the efficiency of the overall system.
Load Balancing
Load balancing is the process of distributing incoming network or application traffic across multiple servers. It reduces the bottleneck on any single server and prevents the system from getting overwhelmed, improving both responsiveness and availability. Load balancing also involves continuously checking the health of the servers so that requests are routed to healthy and available servers in case one fails.
To sum up, the concepts discussed here - scaling, distributed systems, micro-services, decoupling, and load balancing - form the foundation of modern system design. These principles help us build systems that are reliable, scalable, and easier to maintain. As we go deeper into system design, we will explore more of these ideas, understand the trade-offs behind them, and learn how they are applied in real-world architectures.