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Architecture

Scalability is the ability of a software system to handle increasing workloads — more users, data, transactions, or requests — by adding resources without requiring fundamental redesign of the architecture.

Scalability is measured in two dimensions: vertical scaling (scaling up) adds more CPU, RAM, or storage to an existing server, while horizontal scaling (scaling out) adds more server instances behind a load balancer and is the approach used by internet-scale systems. A scalable architecture distributes state carefully — shared databases and in-memory session state are common bottlenecks — and typically moves session data to distributed caches (Redis), uses async message queues (Kafka, RabbitMQ) to decouple components, and applies database read replicas or sharding to spread query load. Scalability must be designed into the architecture from an early stage; retrofitting scalability into a tightly coupled monolith with shared mutable state is one of the most expensive engineering efforts a growing company can face. Load testing under realistic traffic patterns — using tools like k6, Locust, or JMeter — is essential for validating that scaling assumptions hold before production traffic exposes bottlenecks.

Example

A food delivery startup designs its order service to be stateless from day one, storing session data in Redis, so that the team can simply add more application server instances during peak dinner-hour traffic without changing any code.

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