In today’s world, linear scalability is a critical concept that organizations must understand to meet the needs of a universe of data and users that are growing and changing. Linear scalability is the ability of a system or application to add resources in a way that makes its capacity grow linearly. By knowing how linear scalability works and what affects it, organizations can use systems that can handle more work reliably and predictably.
What is Linear Scalability?
Linear scalability is a property of a system or application that lets it handle more work by adding more machines/server/nodes in a way proportional to the increase in work. A linearly scalable system typically can handle twice as much work when its resources are doubled, and thrice as much work when its resources are tripled, and so on.
This differs from vertical scaling where a network can scale by adding more power (CPU, RAM) to existing machines. In this case, the resources added don’t always mean the performance increases in the same proportion. For example, a system that isn’t linearly scalable might only see a slight improvement in performance after a certain point, or it might show diminishing returns, where adding more resources results in improvements initially but slows down with time and increased load.
Linear scalability in blockchain is essential for systems that handle large and growing workloads. It lets networks add resource to meet increasing demand while keeping performance steady and efficient.
How Does Linear Scalability Work?
Linear scalability lets a system handle more work according to how many resources are added. This means that if the system is built to be linearly scalable, its performance will be optimal and constant no matter how many resources are used. As more and better resources are added to the system, it can handle more users and process more data without slowing down.
To achieve linear scalability in blockchain, the system needs to be set up so that resources can be added or taken away quickly without causing downtime or other problems. This can be done by using distributed systems, load balancing, sharding, and other methods that make it possible to add resources on the fly.