Scalability in System Design

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Scalability in system design refers to the ability of a system to handle an increasing amount of workload or growth gracefully. As systems grow, handling higher traffic and workloads efficiently becomes crucial. Let's explore two common approaches to scaling systems:

1. Buy a Bigger Machine/Server 🖥

Definition: This approach, known as Vertical Scaling, involves increasing the capacity of a single machine by adding more powerful hardware (e.g., more RAM, better CPUs).



Why it matters: Vertical scaling can be simpler to implement initially since it requires fewer architectural changes.


Limitations:

  • Hardware limitations eventually cap the scalability.

  • Single points of failure increase risks.

  • Costs rise exponentially with high-end hardware.

Conclusion: While vertical scaling is a quick fix, it’s not a sustainable solution for systems requiring massive scalability.

2. Buy More Machines/Servers 💻 💻 💻

Definition: This approach, known as Horizontal Scaling, involves adding more machines or nodes to distribute the workload across multiple servers. It is commonly used in distributed systems.

Why it matters: Horizontal scaling offers better scalability as you can keep adding machines to meet growing demands.


Advantages:

  • Fault tolerance improves as workloads are distributed.

  • Scaling is more cost-effective over time.

  • Supports geographically distributed systems for lower latency.

Conclusion: Horizontal scaling is a more scalable and robust solution, especially for modern systems requiring high availability and performance.

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