Exploring System Design Principles and Applications

Prankur Pandey

Completed work

Technical Writer

Google Docs

Electronics

August 20, 2025 / #System Design

Over the last three months, life has had me juggling a lot – managing my marriage, taking care of family health issues, and overseeing new construction work at home. Somehow, I got through it all. But looking back, I realised something important: I could’ve handled it much better if I had a system in place.

For me, a system means a set of rules, processes, and triggers that guide the entire workflow. This helps you conserve energy and not have to figure things out in the moment. It keeps things productive, efficient, and consistent.

Now that the chaos has settled, I’ve been thinking a lot about systems – not just in life, but in tech. I wish I had applied the same principles of system design earlier.

In this article, we’re going to explore real-world system design examples from domains like gaming and job platforms. These industries don’t just scale massively – they also demand high availability, low latency, and seamless customer experiences. Understanding how they’re built is a powerful way to level up your thinking as a developer or architect.

What We’ll Cover

Q&A

Final Notes

Conclusion

Introduction: What is System Design and Why Scale Matters

System design is the process of defining the architecture, modules, interfaces, and data of a system.

In other words, system design means explaining the different parts of a system, like its structure, building blocks (modules), and components.

It’s a process used to define, develop, and design a system in a way that meets the specific needs of a business or organisation.

The main goal of system design is to give enough information and details about the system, and to properly implement its parts using models and views. Let’s now talk about the different parts of a system.

Elements of a System:

Architecture: This is a basic structure or model that shows how the system works, looks, and behaves. We often use flowcharts to explain and represent this architecture.

Modules: These are smaller parts or sections of the system. Each module handles a specific task. When all modules are combined, they make the complete system.

Components: These provide a specific function or a group of related functions. Components are usually made from one or more modules.

Interfaces: This is the connection point where different parts (components) of the system exchange information with each other.

Data: This refers to managing information and how it flows through the system.

Why System Design Matters

System design is important for a number of practical reasons. First, it can help companies and teams solve complex business problems and make sure they thoroughly analyse all requirements before building. It also reduces the chance that errors will be introduced into processes while making design phases more efficient and structured. Finally, it helps you efficiently gather and present your data in a useful format and improves the overall quality of the system.

Approaches to System Design

There are several methods you can use to approach system design. The main ones are:

1. Bottom-Up Approach

In this method, the design starts from the lowest-level components or subsystems. These small parts are gradually combined to form higher-level components. This process continues until the entire system is built as one complete structure.

The more abstraction we use, the higher the level of the design becomes.

Advantages:

Components can be reused in other systems.

It’s easier to identify risks early.

It helps in hiding low-level technical details and can be combined with the top-down approach.

Disadvantages:

It’s not very focused on the overall structure of the problem.

Building high-quality bottom-up solutions is hard and time-consuming.

2. Top-Down Approach

Here, the design starts from the entire system, and you break it down into smaller subsystems and components as you go. Each of these subsystems then gets broken down further, step by step, creating a hierarchical structure.

In simple terms, you start with the big picture and keep dividing it until you reach the smallest parts of the system.

To sum up, design starts with defining the whole system, then continues by defining its subsystems and components. When all definitions are ready and fit together, the system is complete.

Advantages:

The focus is on understanding the requirements first, which leads to a responsive and purpose-driven design.

It’s easier to handle errors in interfaces between components.

Disadvantages:

Components can’t be reused easily in other systems.

The resulting architecture is often less flexible or not very clean.

3. Hybrid Design

The hybrid design approach is a mix of top-down and bottom-up methods. Instead of committing to just one way, it takes the strengths of both. You start by looking at the overall system (like in top-down) so that you don’t lose sight of the big picture. At the same time, you also focus on building solid, reusable modules or components (like in bottom-up).

In simple terms, you first plan the big picture, then create smaller components that can work independently, and finally combine everything into a cohesive system.

For instance, in our sports team site, we’d use top-down to define the whole fan journey (homepage → match details → live scores). But bottom-up, we’d build modular components like authentication or stats tracking, which can later be reused in new features like ticket booking or merchandise sales.

Advantages:

You get the clarity of a top-down plan while still building reusable modules.

It strikes a balance between high-level design and detailed implementation.

Risks are easier to manage since you’re considering both structure and components.

Disadvantages:

It can be complex to manage since you’re juggling two approaches.

Requires more coordination between teams working on different levels.

It might take more time compared to using a single approach.

Important Concepts in System Design

Before exploring core components, I want you to first understand two key concepts:

Full stack web application components

How computers talk to each other (via the internet)

Full Stack Web Application Components

A full-stack web application is a software application that combines both the frontend (what users see and interact with) and the backend (the server, database, and logic that power the app) into one complete system.

Generally, simple websites don’t require much system design – and in some cases, no system design at all. But when it comes to viral applications or platforms offering complex services, system design becomes essential. Most modern applications are full-stack applications, meaning they involve multiple interconnected layers working together.

Here’s a simplified overview of a typical full-stack application:

Before diving deep into each of these components, let me first give you a quick, high-level overview of what they are and how they fit into the bigger picture (starting from the bottom of the image above).

Frontend – The user interface where people interact with your application.

Backend – The logic and brain of the application that processes requests.

APIs – The bridge that allows communication between frontend, backend, and external services.

Database – The storage system where all your structured information lives.

Server – The infrastructure that hosts, runs, and delivers your application.

Now, we need to understand how computers talk to each other.

How Computers Talk to Each Other (The Internet)

When you type a website's URL into the browser – and this site could be a simple portfolio site or a full stack app – how does your computer know where to send the request? It uses the Domain Name System (DNS). The DNS is like a phonebook for the internet – it translates a human-readable website name, like "example.com," into a unique numeric IP address that computers can understand.

Once your computer has the IP address, it uses communication protocols to send and receive data. One of the most important protocols is TCP. It breaks data into small, numbered packets. If a packet gets lost or arrives out of order, TCP ensures it's resent and reassembled correctly, making it a very reliable way to send data.

On top of TCP, we use higher-level protocols like HTTP. This is an application-level protocol that's easier for developers to use. It's the language your browser speaks to the server.

HTTPS is the same, but it adds an extra layer of encryption for security.

Now that we understand the basics of the Internet, remember that it serves billions of people worldwide.

Let’s break this down with a real-life example. Imagine you own a restaurant with a seating capacity of 50 people. One day, 10 extra guests arrive – and with a bit of adjustment, you still manage. But suddenly, a thousand more people show up at your door. What would you do then? It’s not just about adding more chairs and tables anymore – you’ll need extra food supplies, more staff, and a bigger setup to handle such massive traffic.

This simple example reflects the real challenge of growth and scalability. And that’s exactly what I’ll be diving into in the next chapter of this tutorial.

The Problem of Growth

Imagine you've built a simple website for a local sports team. Initially, it's just you and a few friends using it, so a single server is sufficient. This server holds all the website's logic and connects to a single database where player stats are stored.

As the team becomes more popular, though, more people visit your site, and it suddenly becomes slow. This is a scaling issue. Your system can't handle all the new traffic.

There are two ways to solve this. The first is vertical scaling. This is like giving your one server a bigger engine and more memory. You'd upgrade the CPU (the brain) or add more RAM (temporary memory). You could also use a faster disk storage like an SSD.

The problem is, you can only upgrade so much before you hit the limits of what's available. Plus, if that single server fails, your entire website goes down.

A better approach is horizontal scaling. This means adding more servers instead of just upgrading one. Now you have a team of servers, and each can handle a portion of the incoming user requests.

This approach allows for almost unlimited growth. It also creates redundancy and fault tolerance, because if one server breaks, the others can pick up the slack, and your site stays online.

Directing Traffic with a Load Balancer

With multiple servers, you need a way to make sure no single server gets overwhelmed. This is where a load balancer comes in. It acts like a traffic cop, sitting in front of your servers and directing each new request to the best-suited server. It uses different algorithms to decide where to send the traffic.

For example, the Round Robin method sends requests to servers one by one, in a cycle. Another method is Least Connection, which sends the request to the server that has the fewest active connections.

Speeding Up Your Website with Caching and CDNs

Imagine your website is now used by people all over the world. A user in another country might experience slow loading times because their request has to travel all the way to your servers.

To fix this, you can use a Content Delivery Network (CDN). A CDN is a network of servers around the world that store copies of your website's static files – like images, videos, and text files. When a user requests one of these files, the CDN serves it from the closest server, making the website load much faster for them.

This process is a form of caching. Caching is the general idea of making copies of data and storing them in a faster-to-access location. You can cache data on your server so it doesn't need to fetch the same player stats from the database every time. This reduces the load on your database and speeds up the entire application.

You can read more about the difference between CDNs and caching here.

Building Your Application: Monolith vs. Microservices

As your website grows, its code can become a tangled mess. You might start with a monolith, where all the features (like player stats and live scores, in our example) are built into a single, large program. A monolith is easier to start with, but it can be hard to manage and update.

A better approach for a large-scale application is to use a microservice architecture. This means breaking your application into smaller, independent services, each with a specific job. For example, one service could handle player stats and another could handle live scores. This makes your code more organised and easier to update, because a change in one service won't affect the others.

With microservices, you need an API Gateway. This acts as a single entry point for all user requests, directing them to the correct microservice behind the scenes. It also handles security and other common tasks.

The APIs

Think of APIs (Application Programming Interfaces) as the “middlemen” that let different pieces of software talk to each other.

In simple terms, an API is like a waiter in a restaurant. You (the user) tell the waiter what you want, the waiter takes your order to the kitchen (the system), and then brings the food (data or result) back to you.

Without APIs, your app, website, or software wouldn’t know how to ask another system for information or services.

For example, on our sports team website:

The front-end (what fans see) uses an API to fetch player stats from the database.

When someone buys match tickets, the API talks to the payment system to confirm the transaction.

If fans want live score updates, the API makes sure the real-time data flows smoothly from the server to their screen.

So, APIs are important for system design because they shape how efficiently different systems connect, share data, and stay reliable under real-world use.

Your front-end and back-end services can communicate in several ways. The most common is a REST API. It's a standardised set of rules that uses HTTP to create a consistent way for a client and server to talk to each other. For example, it defines a standard way to signal a successful request ("OK") or a server error ("Internal Server Error").

When to use REST

Best when: you need simplicity, broad adoption, and easy integration with browsers, mobile apps, or third-party services.

Example: CRUD apps (blogging platforms, e-commerce sites, user management).

Strength: Human-readable JSON, stateless, widely supported.

Weakness: Over-fetching (getting more data than needed) or under-fetching (not enough data).

Another style is GraphQL. Instead of getting all the data a REST API provides, GraphQL lets the client ask for only the specific data it needs, which can make things faster and more efficient.

When to use GraphQL

Best when: clients (like mobile apps) need fine-grained control over exactly what data they fetch.

Example: social media feeds, dashboards with lots of widgets, mobile apps with limited bandwidth.

Strength: Flexible queries, reduces over-fetching, strong typing system.

Weakness: More complex server setup, which can cause performance issues if queries aren’t optimised.

For server-to-server communication, gRPC is often used. It's known for being very fast because it uses a more efficient data format called Protocol Buffers instead of JSON.

When to use gRPC

Best when: services talk to each other in microservice architectures, and speed/efficiency is critical.

Example: real-time systems (streaming, payments, IoT, machine learning inference).

Strength: Super fast (binary Protocol Buffers), built-in support for streaming, strong contracts.

Weakness: Not browser-native (needs extra tooling for web), harder debugging compared to REST

So to summarize based on my observations of what I have worked on so far:

If you’re building something public-facing and widely consumed → go for REST.

If your app has complex, dynamic queries from clients → go for GraphQL.

If you’re dealing with high-performance internal service-to-service calls → go for gRPC.

In system design, choosing the right API style directly affects performance, scalability, and user experience. If you pick REST for its simplicity, GraphQL for its flexibility, or gRPC for its speed, you’re shaping how well your system can grow and adapt as real-world demands change.

Handling Real-Time Data

Real-time data handling is challenging because it requires maintaining an active connection to continuously transmit and receive data simultaneously. Traditional servers follow a request–response model, where data is only sent when explicitly requested.

That's where WebSockets come in. Unlike HTTP, which is a one-and-done request-and-response model, a WebSocket creates a continuous, two-way connection between the client and server. This allows the server to send updates to the user as soon as they happen, creating a real-time experience.

When microservices need to communicate without being directly connected, they can use message queues. A service sends a message to the queue, and another service picks it up when it's ready. This helps to decouple the services, so they don't have to worry about the other service being available at that exact moment.

On our sports site, WebSockets allow fans to see live scores instantly without refreshing the page – just like in chat apps, but here it keeps the excitement of the game alive in real time

Databases

Databases are a critical part of any full-stack application because they serve as the permanent home for user data. Once you’ve decided how to scale your servers and manage communication, you also need to consider the database layer. If everything else scales but the database does not, it can quickly become a bottleneck – leading to crashes, inconsistent records, or even data loss.

Many applications rely on relational databases (SQL), which store data in structured tables with rows and columns and are great for handling structured information. But for applications requiring high flexibility or handling massive unstructured datasets, NoSQL databases (like MongoDB or Cassandra) are often chosen. These databases don't follow the strict rules of SQL and are better for handling massive amounts of data.

Atomicity: A transaction is all or nothing.

Consistency: The data always remains in a valid state.

Isolation: Multiple transactions don't interfere with each other.

Durability: Once a transaction is complete, the data is permanently saved.

Just like with servers, you might need to scale your database. You can use sharding, which divides your data across multiple databases, or replication, which creates copies of your database to handle more read requests.

Understanding the CAP Theorem

When you're dealing with a distributed system and multiple databases, you inevitably face trade-offs. The CAP Theorem states that you can only guarantee two out of the following three properties at the same time:

Consistency – Every user sees the same, most up-to-date data.

Availability – The system is always available to respond to requests.

Partition Tolerance – The system continues to operate even if a part of the network fails.

Now, from a system design perspective, this theorem forces us to make conscious architectural choices. For example, in financial applications (like banking), consistency often takes priority over availability because even a small inconsistency in balance data can cause chaos.

On the other hand, in social media feeds, availability and partition tolerance are often prioritised – it's okay if you see a slightly outdated post, but the system should never be down.

In the flow we’ve been discussing, whenever we introduce a new component or scale out across multiple regions, we need to reassess which two guarantees matter most for our business case. That decision directly drives what database technology we pick, how we design failover strategies, and what trade-offs we accept in user experience.

In short, the CAP theorem isn’t just a theory – it’s a practical compass. It guides us to balance user expectations, business priorities, and technical feasibility without breaking existing functionality, while still leaving room for future growth.

Rate Limiting and Monitoring

When designing a system, it’s not just about making it work – it’s about making it resilient. Two core guardrails here are rate limiting and monitoring.

What is Rate Limiting?

Rate limiting is the practice of controlling how many requests a user, client, or service can make to your system within a given timeframe. For example, you might cap an API at 100 calls per user per hour. This prevents abuse, safeguards against denial-of-service attempts, and ensures fair usage across all consumers.

Rate limiting comes into play any time your service is exposed publicly or internally to multiple clients.

To incorporate it, you can implement limits at the API gateway, reverse proxy (like NGINX), or within your service logic itself. Many cloud providers (AWS API Gateway, GCP Endpoints) also have built-in support.

What is Monitoring?

Monitoring is the practice of collecting metrics, logs, and traces from your system to understand its health in real time. Typical signals include:

Error rates (for example, how often requests fail)

Latency (how long requests take)

Traffic volume (load across the system)

Resource utilisation (CPU, memory, disk, and so on)

Monitoring is important from day one – it’s your feedback loop. Without it, you’re essentially flying blind.

To work it into your system, you can use observability stacks like Prometheus + Grafana, or managed solutions like Datadog, New Relic, or CloudWatch. You can also set alerts for threshold breaches (for example, 5% error rate spike).

In practice, rate limiting and monitoring work hand-in-hand. Rate limiting proactively guards against overload, while monitoring gives you visibility into whether the limits are working, whether scaling is needed, or whether a new type of failure is emerging.

For example, if you’ve designed a booking system (like in our earlier flow), rate limiting would ensure a single user can’t spam seat reservations, while monitoring would flag anomalies such as unusual spikes in request volume or sudden latency increases – helping you act before the system collapses.

Why Does This Matter for System Design?

These topics matter for good system design because they form the foundational building blocks of how modern applications actually operate in the real world. The way systems communicate, the type of APIs we adopt, and how we manage real-time interactions directly influence whether a product feels fast, reliable, and seamless – or slow and frustrating. In short, they determine how well the overall experience holds up when real users put it to the test.

When we develop a deeper understanding of how computers communicate, we begin to see the inner mechanics of client–server architecture – how APIs fetch data from databases through backend system calls. From this baseline, we can pivot into higher-level concerns:

Scalability and resilience: Using load balancers to protect against server overload.

Security: Introducing rate limiting to mitigate potential cyberattacks.

Efficiency: Choosing the right type of API calls and leveraging caching/CDNs for speed and reduced overhead.

Reliability: Implementing logging and monitoring to detect issues early and debug faster.

Together, these practices elevate a system from simply working to being robust, performant, and future-ready.

We’ve discussed the basics of all the most important concepts you’ll need to understand before building an end-to-end system. Now it’s time to deep dive into the case studies, where I’ll show you how different types of applications use system design to scale and serve billions of users.

I have picked services that are complex to build and handle multiple different types of components at a time, like gaming, education, and job search platforms.

Now let’s decode each of them together, and I’ll explain how I would scale the application if I were the developer building it.

Case Studies: Scaling in the Real World

System design is best understood when you see it in action. To show how principles like scaling, caching, load balancing, and real-time data management come together, let’s walk through two very different types of applications:

A job search platform (focused on structured data and reliability).

An online gaming platform (focused on real-time speed and responsiveness).

Looking at both will show you that, while the tools and concepts may be similar, the way we apply them depends on the type of system we’re building.

Both are high-traffic platforms, but with totally different needs. The job portal is about accuracy, reliability, and data-driven workflows, while the gaming platform is about instant responsiveness, fairness, and global reach.

In a job portal, a 1-second delay just means waiting. In a gaming app, a 1-second delay could mean losing the match. Both are failures – but for completely different reasons, and with different consequences.

Together, they show how the same building blocks of system design (scaling, caching, APIs, monitoring) are applied differently depending on context.

Case Study 1: Scaling a Job Search Application

A job search platform is one of the most used applications nowadays, as there are always people looking for a job. And there are many different job portals out there that handle the complete process, from finding jobs to user onboarding.

We’ll look at an example site called Upstaff. It’s a platform that focuses on hiring AI engineers as its core service (although it services other job profiles as well). At its core, it handles structured information – things like user profiles, job postings, and applications.

Day one, you have a few hundred users. On day one hundred, you may have tens of thousands. And in a year? Possibly millions. That growth means you have to think about scale, speed, and data integrity from the start.

🔹 The Core Components

User Management: registration, login, and role-based access (job seeker vs employer).

User Profiles: résumés, skills, preferences, stored in structured databases.

Job Posting and Listings: employers create jobs, seekers browse/search/filter.

Application Tracking: Every job seeker’s application status needs to be accurate and up to date.

Recommendation Engine: jobs matched to users based on history and profile.

Notifications: alerts for new job matches, recruiter replies, deadlines.

Every one of these features depends on the system’s ability to handle large amounts of structured data – and handle it reliably.

Step 1: Starting Small

At the beginning, everything can run on one server with a single database. This setup is enough for a few thousand users.

Step 2: Growth and Traffic Spikes

As more users join, the single server starts to slow down. To fix this, we add a load balancer and scale horizontally – adding multiple servers that share the traffic.

Step 3: Database Challenges

Soon, the database becomes the bottleneck. Searching across thousands of jobs slows things down. To fix this, we:

Use sharding (split the database by user IDs or job IDs).

Add a cache (like Redis) to store frequent queries such as “Software Engineer in New York.”

Use a CDN to deliver logos, profile pictures, and other static files faster.

Step 4: Heavy Features

New features like a résumé parser or recommendation engine require extra computing power. Instead of overloading the main app, we move these into separate microservices.

Step 5: Security and Reliability

Finally, as traffic grows, we add:

Rate limiting to stop any one user from spamming APIs.

Monitoring to track errors, latency, and user activity in real time.

API Gateway to ensure all requests are secure and validated. Here is an overview of the entire system scaling in an image :

This example shows how careful planning makes growth smooth. By scaling horizontally, caching smartly, and splitting heavy features into microservices, a job portal like Upstaff can handle millions of users without breaking.

Case Study 2: Scaling an Online Gaming Application

Now let’s flip the script. In a gaming platform like this site, speed and responsiveness matter more than anything. A 1-second delay in a job search is annoying. But in gaming, a 1-second delay can make players quit forever. Unlike job portals, the biggest challenge here is real-time responsiveness. A tiny delay can ruin the user experience.

🔹 The Core Components

User Management Service: accounts, profiles, and login.

Game Lobby and Matchmaking: pair players by skill, region, and latency.

Game Server Manager: spin up and manage live matches.

Real-Time Communication: powered by WebSockets or UDP for low latency.

Game State Store (Redis): fast sync of health, scores, and positions.

Leaderboard & Stats Engine: global rankings, achievements, and progress.

In-Game Economy: coins, tokens, inventory.

Payment Gateway: subscriptions and purchases.

Anti-Cheat Security Layer: fairness across all players.

Monitoring and Logging: server uptime, latency, and crash reports.

Unlike a job portal, every millisecond counts.

Step 1: Starting Small

At first, one powerful server is enough to run both the game logic and user accounts. With just a few players, things run smoothly.

Step 2: More Players, More Problems

As millions of players log in, the single server crashes. To fix this, we:

Add a Game Server Manager that spins up separate servers for each match.

Introduce a load balancer that assigns players to available servers.

Step 3: Real-Time Data Handling

In gaming, speed is everything. Instead of slow HTTP, we switch to WebSockets or UDP for instant communication. To keep everyone’s game view in sync:

Use in-memory databases like Redis for positions, scores, and health.

Update leaderboards in near real time.

Step 4: Scaling Features

Other services run in parallel:

Matchmaking service pairs players by skill, location, and latency.

Economy service manages coins, rewards, and in-game items.

Payment gateway handles subscriptions and purchases securely.

Notification system sends updates like “new event starting.”

Step 5: Global Expansion and Security

When the game expands worldwide:

Use a CDN to deliver maps and skins quickly to all regions.

Add an Anti-Cheat layer to detect and block unfair play.

Build an Admin and Monitoring panel to track system health and user behavior.

In gaming, system design focuses less on structured data and more on low latency, real-time communication, and fairness. Scaling here means keeping gameplay smooth and secure, even when millions of players join at once. Here is the image representation of the complete game platform system design

Why Both Case Studies Matter

You might wonder – why show two different systems instead of just one? The answer is that system design isn’t “one-size-fits-all.”

The job portal teaches us how to scale structured, data-heavy applications where reliability and accuracy matter most.

The gaming platform shows us how to design for speed, real-time communication, and fairness under extreme load.

Together, these examples prove that the same system design principles of scaling, caching, monitoring, and microservices apply everywhere. What changes is how you use them to solve the unique challenges of your platform.

Q&A

How to get into system design if you don’t understand anything (yet)?

I get this question all the time – and the first thing you need to know is that system design isn’t some separate, elite domain. It’s an additional skill that complements your development journey.

If you're a full-stack developer (or aiming to be one), learning system design gives you a huge edge. After all, building an app isn't just about making it work – it’s about making it work well at scale.

So if you’re just starting and don’t even know how to become a full-stack developer yet, start there. Learn to build applications first, and then system design will start making a lot more sense. Read this guide How to Become a Full-Stack Developer in 2025 (and Get a Job) – A Handbook for Beginners to learn how to become a full-stack developer.

How do you understand system design concepts?

The short answer: with time and consistent practice.

Think of it like this: if you know how to use a pencil, it’s up to you whether you use it to sketch or to write. The pencil is just a tool. Similarly, in system design, once you understand the core concepts, it’s about knowing when and where to apply them. The rest – frameworks, tools, and technologies – are just means to an end.

It’s not about memorising patterns, it’s about developing the instinct to use the right building blocks at the right time.

What tools should you know before diving into system design?

The truth is, the list keeps growing. New tools and platforms are constantly emerging. But in my experience, having a solid foundation in the following areas makes a huge difference:

Full Stack Development – so you understand how both frontend and backend systems interact.

Cloud Platforms (like AWS, GCP, or Azure) – because most modern systems are cloud-native.

CI/CD Pipelines – for automating testing, integration, and deployment.

Deployment Strategies – to know how to roll out new changes with minimal risk.

Mastering these gives you the technical muscle to design systems that are scalable, reliable, and production-ready. I am a frontend developer, why should I know the system design

What resources should I study to learn system design?

In my last article, I shared all the resources that helped me learn system design.

System design is crucial for building reliable, high-performance applications. I explored the following resources:

Free GitHub system design repository

Another free GitHub system design repository

Free System Design Handbook

Free Hands-on System Design Learning Platform

Free Intro to System Design Blog

Case studies and real-world architectures can also help you understand large-scale systems. You can follow any big tech engineering blog (Uber has a great one).

For high-level concepts, I went through the Grokking System Design course. It’s a paid resource, and I used it to deepen my understanding of system design. It’s not mandatory, but it helped me think about architecture at scale.

Note: there are other sites and courses out there of course, but I only share what I have personally experienced and used, and I focus on FREE material first.

Where to practice system design

This is where real learning begins. Start by picking any existing application from the internet, just like I did. Google something specific, like “job application portal,” but avoid the results on the first page. Those apps are usually well-optimised and already follow best practices in system design.

Instead, dig deeper and explore results from the second or third page. Look for an app that seems to be in its early stages.

Once you find one, try to understand how the entire application works. Break it down into its core components and then imagine what would happen if that app started receiving 1 million users a day. You’ll naturally begin to see what system design elements are needed to handle that kind of load.

Final Notes

Learning system design becomes much easier when you’ve already built something. Let’s say you’ve created an app and now you're thinking about how to scale it – that’s where real learning begins. The moment you start writing down your requirements (like how your app should behave when it starts getting more traffic), you naturally begin to develop system-level thinking. It’s this process of planning and anticipating real-world usage that turns theory into a practical skill.