Understanding Load Balancing Algorithms: Round-robin and Consistent Hashing

📆 · ⏳ 4 min read · ·


Hey there! Today, we’ll be diving into the heart of load balancing algorithms, the backbone of distributed systems.

Whether you’re building web applications or managing complex infrastructures, load balancing ensures that incoming requests are evenly distributed among servers, preventing bottlenecks and optimizing system performance.

In this blog, we’ll focus on two popular load balancing algorithms: Round-robin and Consistent Hashing.

Understanding Load Balancing Algorithms

Load balancing is like a traffic conductor for your servers, efficiently distributing incoming requests among a group of backend servers. It helps ensure that no server gets overwhelmed, leading to a seamless user experience.

Load balancers act as the central orchestrators, intelligently routing requests to the most suitable servers, making it a critical component for high-availability and scalable systems.

Round-robin Load Balancing

Imagine you have a group of servers, and you want to distribute incoming requests in a circular manner, like passing a baton in a relay race. That’s precisely what Round-robin does! It sequentially forwards each request to the next server in line, forming a circular loop.

This simplistic approach ensures fair distribution among servers, but it may not account for varying server capacities or traffic patterns.

Consider this example, suppose we have three servers, S1, S2, and S3, and we have ten incoming requests. The round-robin algorithm would distribute the requests as follows:

Request 1: S1
Request 2: S2
Request 3: S3
Request 4: S1
Request 5: S2
Request 6: S3
Request 7: S1
Request 8: S2
Request 9: S3
Request 10: S1


The round-robin algorithm works well in scenarios where all servers have the same capacity and can handle an equal amount of traffic. However, it is not suitable for scenarios where server capacities vary significantly.

Consistent Hashing Load Balancing

Unlike Round-robin, Consistent Hashing takes a more dynamic and efficient approach to load balancing. In this algorithm, each server is mapped to a unique point on a hash ring, and incoming requests are hashed to determine which server they should be routed to.

This ensures that requests are distributed evenly across all servers while maintaining the same server for each request. If a server fails or is removed from the network, the requests that were mapped to that server are redistributed to the remaining servers.

This property of Consistent hashing makes it most suitable for distributed systems, where servers are frequently added or removed i.e upscaled or downscaled.

Consistent Hashing allows for easy addition or removal of servers without causing significant disruptions, making it ideal for large-scale distributed systems.

The Power of Consistency

Consistent Hashing’s strength lies in its ability to ensure that requests consistently go to the same server for a specific key or resource.

This is possible because of how hashing works - given the same input, we always get the same output. This means that if we hash a request, we can always map it to the same server.

This minimizes the need for data re-caching and reduces the risk of hotspots, where certain servers become overloaded due to popular keys.

The Magic of Hashing

Hashing is a powerful technique that allows us to map data of arbitrary size to a fixed-size value. This is useful for load balancing because it allows us to map requests to servers in a consistent manner.

Hashing in general is a very simple concept, given some input, we can generate a unique output. For example, if we hash the string “hello” using some hashing algorithm, we might get the following output:

hello -> some hashing algorithm -> 0sa33c402abc4b2a763234da11s

The hashing algorithm we use might not be important, as long as it is consistent. This means that if we hash the same input multiple times, we should get the same output.

Scalability and Fault Tolerance

Load balancing algorithms are crucial for scaling your applications and handling failures gracefully.

Both Round-robin and Consistent Hashing contribute to improved fault tolerance by distributing traffic across multiple servers, ensuring that if one server fails, the others can pick up the slack.

They also help improve scalability by allowing you to add or remove servers without causing significant disruptions.


In this article we dived into understanding Round-robin and Consistent Hashing. We’ve gained valuable insights into how these algorithms intelligently distribute traffic, ensuring scalable and fault-tolerant systems.

As you build and manage your distributed systems, keep these load balancing techniques in mind to optimize performance and deliver a seamless user experience. Happy load balancing!

You may also like

  • Building a Read-Heavy System: Key Considerations for Success

    In this article, we will discuss the key considerations for building a read-heavy system and how to ensure its success.

  • Building a Write-Heavy System: Key Considerations for Success

    In this article, we'll discuss crucial considerations that can guide you towards success in building a write-heavy system and help you navigate the complexities of managing high volumes of write operations.

  • Tackling Thundering Herd Problem effectively

    In this article, we will discuss what is the thundering herd problem and how you can tackle it effectively when designing a system.