10 System Design Terms Every Developer Should Know
Learn how big systems handle millions of users. We simplify caching, load balancing, CDNs, and more so you can build and scale like a pro.
This blog breaks down 10 fundamental system design concepts – from scalability and load balancing to caching and the CAP theorem – in plain language. By the end, you’ll have a solid foundation for system design discussions and interviews.
Massive apps like Netflix or Facebook handle millions of users seamlessly thanks to system design.
Whether you’re preparing for a system design interview or just curious how large systems work, you’ll hear key terms again and again.
And if you don’t even know what they are, then this guide is for you.
In this guide, we explain the most common system design concepts in simple terms.
Let’s get started!
1. Scalability
Scalability is a system’s ability to handle increasing load by adding resources (like more servers).
In simple terms, if your app suddenly gets a flood of new users, a scalable design lets it grow without crashing.
This can involve vertical scaling (upgrading a server’s hardware) or horizontal scaling (adding more servers to share the work).
2. Load Balancer
A load balancer is like a traffic cop that directs incoming requests across multiple servers so none of them overload. This keeps your application responsive under heavy use.
If one server fails or gets swamped, the load balancer routes traffic to other servers so users won’t notice a hiccup.
3. Microservices
Microservices is an architectural style where a large application is split into many small, independent services that communicate over APIs.
Instead of one big monolithic app, you have separate services (for example, user accounts and payments), each handling a specific function.
Each service can be developed and deployed on its own, making it easier to update or scale parts of the system without affecting the rest.
4. CAP Theorem
The CAP theorem says that a distributed system can’t guarantee more than two of the following at once: Consistency, Availability, and Partition Tolerance.
In short, consistency means all nodes see the same data, availability means every request gets a response (even if some data is stale), and partition tolerance means the system still works despite network outages.
You can’t have all three at the same time, so you must choose which to prioritize.
Some NoSQL databases favor availability over consistency – they return data even if some replicas are behind, trading accuracy for uptime.
5. Sharding
Sharding is a way to scale databases by splitting a large dataset into smaller pieces called shards. Each shard holds part of the data and can be stored on a different server, so no single database handles everything.
You might shard a user database by region (Europe on one shard, America on another).
Sharding greatly improves capacity and performance because queries and traffic are divided up, preventing any one database server from becoming a bottleneck.
6. Latency and Throughput
Latency is the delay between sending a request and getting a response – how long a user waits. If you click a button and it takes two seconds to respond, that’s high latency. Lower latency means a faster, snappier user experience.
Latency can come from network delays or slow processing. System designers try to reduce latency by using faster networks, caching data, or putting servers closer to users.
7. Throughput
Throughput is the amount of work or data a system can process per unit of time. It’s often measured in requests per second or data per second.
If a service handles 5,000 requests per second, that number is its throughput. High throughput means the system can serve a lot of users or transactions in parallel.
8. Cache (Caching)
A cache is a high-speed storage layer (usually in memory) that stores copies of frequently accessed data for quick access.
Caching means using such a store so you don’t repeat expensive operations. If an application keeps requesting the same user profile info, it can store that data in an in-memory cache (e.g. Redis).
Then future requests get the data in milliseconds instead of hitting the database each time.
9. Content Delivery Network (CDN)
A Content Delivery Network is a distributed network of servers around the world that deliver content to users from the nearest location.
A user in Asia requesting a video from a US website might get it from an Asia-based CDN server, making it load much faster.
CDNs cache files like images, videos, and scripts at edge servers worldwide, which greatly cuts down load times and reduces strain on the main servers.
10. Replication
Replication means keeping copies of the same data on multiple servers or data centers. This boosts reliability and availability.
If one database server crashes, another server with the same data can take over immediately, so the application keeps running.
Replication also helps distribute read workload – for instance, one database node can handle writes while several replicas handle read queries.
These terms form a solid base for designing scalable and reliable systems.
With these fundamentals in your toolkit, you’ll be better prepared to build robust applications and tackle system design interviews with confidence.
FAQs
Q1: What is system design in software engineering?
It’s the process of defining a software system’s architecture and components – creating a blueprint for how different parts (database, servers, etc.) work together.
Q2: How do I prepare for a system design interview as a beginner?
Learn the core concepts (like caching, load balancing, scalability, etc.) and understand why they’re used. Practice by designing simple systems (like a URL shortener or a chat app) to apply these ideas. Using a structured plan or roadmap (for example, DesignGurus’ System Design Interview Roadmap) will help you cover all key topics.
Q3: What’s the difference between horizontal and vertical scaling?
Horizontal scaling means adding more machines or servers to share the load (like adding more lanes to a highway), whereas vertical scaling means upgrading one machine’s resources (like making a lane wider). Horizontal scaling lets you increase capacity by adding servers, whereas vertical scaling is limited by the hardware of a single machine.