|
Latest articles
If you’re not a subscriber, here’s what you missed this month.
 |
Consistency and Partition Tolerance: Understanding CAP vs PACELC
A Guide to Database Replication: Key Concepts and Strategies
To receive all the full articles and support ByteByteGo, consider subscribing:
Scalability is the ability of a system to handle growth (more users, more data, and more requests) with high performance. In modern distributed systems, scalability is not a nice-to-have. Whether serving a global user base or responding to a viral spike, systems that fail to scale often fail outright.
This becomes especially critical in cloud-native environments, where usage patterns shift rapidly and infrastructure costs scale with traffic. An application that handles 1000 users today might need to serve 100K tomorrow if things work out. If that jump requires a complete redesign, it’s already too late.
Scalability is often confused with performance or elasticity. These are related but distinct concerns:
Performance is related to how fast a system responds under a fixed load. It’s a question of latency and throughput.
Elasticity is related to how quickly and automatically a system adapts to changing demand, often in terms of infrastructure.
Scalability is related to how well a system maintains its characteristics as load increases. It asks: What happens when the load doubles?
In this article, we look at core strategies that are used to build scalable systems. Each technique solves a different problem, and most systems use a combination of them. Getting scalability right isn’t about choosing one pattern, but about knowing when to apply which tool, and where it might cause problems.
 |
