Load balancers are the unsung heroes of modern computing. They distribute incoming network traffic across multiple servers, ensuring that no single server is overwhelmed with too many requests. They play a crucial role in maintaining the responsiveness and availability of applications. And yet, the intricacies of how they work often go unnoticed.
With the growing popularity of microservices and cloud-native architectures, I decided it was the perfect time to dive deeper into this topic. The best way to learn? Build one myself! I chose Go (Golang) for this project (GitHub link here) because of its simple syntax, strong concurrency support, and efficient execution.
Why Go?
Go has gained a lot of traction in the fields of system programming, cloud computing, and especially in building robust and scalable distributed systems. Its lightweight threads (goroutines), simple yet powerful syntax, and strong standard library make it an ideal choice for this kind of project.
Algorithms Implemented
In my implementation, I included four types of algorithms to distribute incoming HTTP requests among backend servers:
Least Connections
In this method, incoming requests are sent to the server with the fewest active connections. It’s a straightforward yet effective way to distribute load evenly across all servers.
Random
This is as simple as it sounds. Every incoming request is randomly assigned to a backend server. This algorithm is useful for scenarios where each request is roughly the same in terms of computational load.
Weighted Random
Here, each server is assigned a static “weight,” or priority level. The servers with higher weights get a proportionally larger share of incoming requests.
Dynamic Weighted Random
This is an advanced variant of the Weighted Random algorithm. In this method, the weight assigned to each server is dynamically adjusted based on its current number of active connections. This allows for more intelligent distribution based on real-time server load.
Open Source and Further Contributions
The complete codebase for this project is available on GitHub, and it serves as an educational example. While it’s not production-ready, it provides a solid foundation for understanding the core concepts of load balancing. I invite you to fork the repository, explore the code, and contribute to its improvement.
What I Learned
- Algorithms matter: The choice of load balancing algorithm can greatly influence application performance.
- Concurrency is key: Managing active connections in a thread-safe manner is crucial for a reliable load balancer.
- Measurements are crucial: Implementing more sophisticated metrics can make the load balancer more adaptive and efficient.
Conclusion
Building a load balancer from scratch has been an incredibly educational experience. It gave me a newfound respect for the complexity and importance of this seemingly “invisible” technology. If you’re curious about networking, distributed systems, or just like a good coding challenge, I highly recommend giving it a try!
Source code: https://github.com/araujo88/simple-load-balancer
