Introduction

System design is not just for interviews—it’s a daily skill for building scalable, reliable, and maintainable software. In this article, we’ll cover 20 essential system design concepts every developer should understand, with simple explanations, architecture intuition, and GitHub references.

1. Load Balancing

Load Balancing is a technique used to distribute incoming requests (traffic) across multiple servers so that no single server gets overloaded. Instead of sending all users to one server, a load balancer acts like a traffic police and decides which server should handle each request.

This makes applications:

🚀 Faster
🧱 More reliable
📈 Scalable
💥 Less likely to crash

Simple Real-Life Example

Imagine a busy restaurant with only one waiter. If 100 customers arrive at the same time → the waiter gets overwhelmed Orders are delayed Customers get frustrated.

Now imagine 4 waiters instead. Each waiter handles some tables Orders are served faster No one is overloaded

👉 Load Balancer = Restaurant Manager
👉 Servers = Waiters
👉 Customers = User requests

Why it matters:

Improves availability
Increases scalability
Prevents server overload

GitHub examples:

https://github.com/teejays/load-balancer https://github.com/github/glb-director

2. Caching

Caching is a technique where we store frequently used data in a fast storage (cache) so that future requests can be served quickly without hitting the database or backend again. In short:

| Caching = Store data once, reuse it many times faster

How Caching Works (Step by Step)

User sends a request
Application checks the cache first
If data is found → Cache Hit ✅
If not found → Cache Miss ❌
On cache miss:
- Fetch data from database / API
- Store it in cache
- Return response to user

Types of Caching (Easy to Understand)

In-Memory Cache Stored in RAM (very fast)

Examples:

Redis
Memcached
Caffeine (Java)

✔ Extremely fast
❌ Data lost on restart

Application-Level Cache Cache lives inside the application.

Example:

Spring Boot Cache (@Cacheable)

✔ Simple to implement
❌ Not shared across instances

Distributed Cache Cache shared by multiple servers.

Examples:

Redis Cluster
Hazelcast

✔ Works well with microservices
✔ Scales horizontally

Browser / CDN Cache Used for static content like images, CSS, JS.

✔ Reduces server load
✔ Faster page load

Why it matters:

Reduces database load
Improves response times
Prevents data consistency issues

GitHub examples:

https://github.com/redis/redis https://github.com/ehcache/ehcache

3. Database Sharding

Database sharding splits data across multiple databases to improve performance and scalability. Why it matters: Improves performance Increases scalability Prevents data consistency issues

GitHub examples:

https://github.com/nthieu29/mongodb-sharding-demo

4. Replication

Replication is the process of sharing information across multiple servers to increase data availability and fault tolerance.

GitHub examples:

https://github.com/datacharmer/test_db

5. CAP Theorem

CAP theorem states that a distributed system can guarantee only two of the following: Consistency Availability Partition Tolerance

GitHub reference:

https://github.com/karanpratapsingh/system-design#cap-theorem

6. Consistent Hashing

Consistent hashing minimizes data movement when nodes are added or removed in a distributed system.

GitHub example:

https://github.com/stathat/consistent

7. Message Queues

Message queues enable asynchronous communication between producers and consumers.

GitHub example:

https://github.com/rabbitmq/rabbitmq-tutorials

8. Rate Limiting

Rate limiting controls how many requests a client can make in a given time window.

GitHub example:

https://github.com/nfriedly/express-rate-limit

9. API Gateway

An API Gateway acts as a single entry point for multiple backend services.

GitHub example:

https://github.com/Kong/kong

10. Microservices

Microservices architecture breaks applications into small, independently deployable services.

GitHub examples:

https://github.com/callicoder/spring-boot-microservices-series https://github.com/dotnet-architecture/eShopOnContainers

11. Service Discovery

Service discovery helps services dynamically locate each other in distributed systems.

GitHub examples:

https://github.com/Netflix/eureka https://github.com/hashicorp/consul

12. CDNs (Content Delivery Networks)

CDNs cache content at edge locations to reduce latency for global users.

GitHub example:

https://github.com/opencdn/opencdn

13. Database Indexing

Indexes speed up data retrieval by providing quick lookup paths.

GitHub example:

https://github.com/greenlion/simdb

14. Partitioning

Partitioning divides tables into smaller segments for performance and manageability.

GitHub example:

https://github.com/oliver006/partitioned-tables-example

15. Eventual Consistency

Eventual consistency ensures all replicas converge to the same state over time.

GitHub example:

https://github.com/aws-samples/aws-dynamodb-eventual-consistency

16. WebSockets

WebSockets enable real-time, bidirectional communication.

GitHub example:

https://github.com/websockets/ws

17. Scalability

Scalability measures a system’s ability to handle increased load.

GitHub reference:

https://github.com/donnemartin/system-design-primer#scalability

18. Fault Tolerance

Fault-tolerant systems continue operating even when components fail.

GitHub example:

https://github.com/Netflix/Hystrix

19. Monitoring

Monitoring tracks system health, performance, and errors.

GitHub examples:

https://github.com/prometheus/prometheus https://github.com/grafana/grafana

20. Circuit Breaker Pattern

The Circuit Breaker pattern prevents cascading failures by failing fast when services are unhealthy.

GitHub examples:

https://github.com/Netflix/Hystrix https://github.com/resilience4j/resilience4j

Final Thoughts

Mastering these system design concepts will help you build better software and perform confidently in system design interviews. If you’re a backend developer (especially Java), these patterns show up everywhere in real-world systems.