What is load balancing and how does it improve system reliability and performance?

Have you ever visited a website that was slow or kept crashing? Chances are the site wasn’t using effective load balancing. Load balancing is a technique in system architecture that distributes incoming traffic across multiple servers. This prevents any single server from getting overwhelmed, keeping websites fast and reliable for users. It’s a critical concept in modern system architecture and a frequent topic in system design interview questions. In this article, we’ll explain what load balancing is, how it works, and how it boosts both reliability and performance. We’ll also share real-world case studies (including FAANG examples) and interview tips, so you can confidently discuss load balancing in a technical interview or mock interview practice session.

What Is Load Balancing?

Load balancing is the process of distributing network traffic or workloads across multiple servers so that no single server becomes a bottleneck. In simpler terms, a load balancer acts like a traffic cop that sits in front of your servers and directs client requests to whichever server can best handle each request. By spreading out work evenly, load balancing ensures your system stays responsive and available even under heavy load.

Key aspects of load balancing include:

• Even Traffic Distribution: Incoming requests are spread across multiple servers, preventing any one server from handling all the load.
• High Availability: If one server fails or goes offline, the load balancer reroutes traffic to other healthy servers, minimizing downtime.
• Improved Performance: No single server is overwhelmed, which means each request can be processed faster. Users experience quicker responses and fewer slowdowns.

For a deeper dive into load balancing fundamentals (especially in an interview context), check out our guide on understanding load balancing for system design interviews.

How Does Load Balancing Work?

In a typical setup, clients (users) send requests to a website or service, and these requests first hit the load balancer instead of going directly to a server. The load balancer then forwards each request to one of the backend servers in the server pool. There are various strategies for how the load balancer decides which server should get the next request:

• Round Robin: Sends each new request to the next server in line, cycling through all servers in order.
• Least Connections: Sends the request to whichever server currently has the fewest active connections (i.e. the least busy server).
• IP Hash / Session Persistence: Uses a hash of the client’s IP (or session ID) to consistently route a user to the same server (useful for user sessions).
• Weighted Distribution: Some servers can be assigned a higher weight (capacity) so they receive a larger share of traffic if they are more powerful.

These are just a few examples of load balancing algorithms. Each algorithm has its use cases and trade-offs. For instance, round robin is simple but doesn’t account for server load, while least connections actively measures load but may have overhead. (Interested in more algorithms? See our detailed list of different load balancer algorithms for a comprehensive overview.)

Types of Load Balancers: Load balancers can be implemented in different ways:

• Hardware load balancers – dedicated physical devices (often used in enterprise data centers).
• Software load balancers – software running on standard servers (e.g. HAProxy, Nginx) that perform load balancing logic.
• DNS load balancing – using the Domain Name System to distribute traffic by mapping a single domain name to multiple server IPs (e.g. big companies like Google and Amazon use DNS round-robin techniques for global traffic distribution).
• Client-side load balancing – where the client or a client-side service decides which server to contact (common in microservices architectures with a service discovery mechanism).

Despite the different implementations, the goal is the same: efficiently share the workload among servers so that the system as a whole can handle more traffic smoothly.

How Load Balancing Improves System Reliability

System reliability means your service stays up and running consistently, even when parts of it fail. Load balancing plays a huge role in improving reliability:

• No Single Point of Failure: In an unbalanced system, one overloaded server can crash and take down the whole service. With load balancing, you have multiple servers. If one server goes down, the load balancer quickly diverts incoming requests to other servers that are still working. This redundancy keeps the service available to users.
• Automatic Failover: Modern load balancers often perform health checks on servers. If a server is unresponsive or unhealthy, the load balancer stops sending traffic to it and automatically fails over to the healthy machines. Users might not even notice that one server went offline, because their requests seamlessly go to others.
• Consistent Availability: By spreading requests out, load balancing prevents any single machine from getting overwhelmed and crashing under pressure. This means fewer outages and more uptime. For example, many cloud services use load balancers to achieve “five nines” availability (99.999% uptime) by having backups ready to take traffic if one server fails.
• Maintenance Without Downtime: With a load balancer, you can perform routine maintenance on servers one at a time. While one server is taken down for updates, the load balancer sends all traffic to the remaining servers. This way, you can update or fix servers without taking the entire site offline.

In short, load balancing increases reliability by adding fault tolerance and redundancy. A balanced system can handle failures gracefully, which is why highly available architectures almost always include load balancers. If you’re preparing for system design interviews, emphasizing these reliability benefits is key. (For a step-by-step tutorial on incorporating load balancing into a resilient system design, see our Step-by-Step Approach for Load Balancing in System Design Interviews.)

How Load Balancing Improves Performance

Beyond reliability, load balancing significantly boosts a system’s performance and user experience:

• Lower Latency: By sharing the workload, each server handles fewer requests on average, so it can respond faster. Users get quicker responses because no single server is tied up processing a huge queue of requests. The result is snappier websites and applications, even during peak traffic times.
• Higher Throughput: Throughput is the amount of traffic a system can handle in a given time. With multiple servers working in parallel, a load-balanced system can serve far more users at once than a single server could. For example, a single server might handle a few hundred requests per second, but five servers behind a load balancer could handle thousands of requests per second collectively.
• Prevents Overload Bottlenecks: Without load balancing, one busy server can become a choke point that slows everything down. Load balancing removes these bottlenecks by ensuring no server gets more requests than it can handle. This keeps the system performing smoothly under load, reducing the chance of timeouts or slow responses due to an overwhelmed component.
• Efficient Resource Utilization: Load balancers make sure all your servers are pulling their weight. If one server is lightly used and another is busy, a smart load balancer will send more traffic to the idle server. This efficient use of resources means you get the maximum performance out of the hardware or cloud instances you’re paying for.
• Scalability for Performance: Whenever you need to handle more traffic, you can scale horizontally (add more servers) and put them behind the load balancer. The load balancer will include the new servers in its rotation, immediately boosting capacity. This ability to grow the system on demand keeps performance high as user load increases. (Our Grokking System Design Fundamentals course covers how techniques like caching and load balancing improve a system’s performance and scalability.)

Real-world evidence shows the performance gains from load balancing. Think of high-traffic websites: if millions of users hit a service like YouTube or Facebook, the only way to provide fast responses is to have many servers working in parallel with traffic distributed among them. Even on a smaller scale, if you’ve ever run a web application that slowed down as users grew, adding a load balancer and more server instances often immediately improves response times. In the end, load balancing ensures users get a fast, seamless experience instead of waiting on a sluggish, overloaded server.

Real-World Examples of Load Balancing (Case Studies)

To understand the impact of load balancing, let’s look at how some tech giants (FAANG companies) use it in practice:

• Google and Amazon (Global Scale): Both Google and Amazon handle enormous amounts of global traffic. They use DNS-based load balancing (among other methods) to direct users to servers in data centers nearest to them. For example, Google’s frontend load balancers are distributed around the world; when you connect to Google, DNS might send you to the closest region’s load balancer, balancing load at a global scale. This ensures low latency for users on different continents and shares the traffic across data centers. Amazon’s AWS infrastructure similarly uses DNS round-robin and Route 53 (Amazon’s DNS service) to distribute traffic across multiple server clusters.
• Facebook (Multiple Load Balancer Layers): Facebook serves billions of user requests per day. To handle this, Facebook has implemented layered load balancing. Initially, they introduced a software load balancer to distribute traffic among application servers. As traffic grew to millions of users, a single load balancer couldn’t handle it all, so Facebook added more load balancers and even an additional layer: they put a layer-4 load balancer in front of several layer-7 load balancers (layer-7 deals with HTTP requests, layer-4 with lower-level network routing). Eventually, for hundreds of millions of users, they added a routing layer in front of the layer-4 balancers. This multi-layer approach allowed Facebook to scale to over a billion users via a software load balancing system. The result is that even as you and a billion other users scroll through your news feeds, Facebook’s infrastructure can route requests efficiently with no single point of overload.
• Netflix (Content Delivery & Microservices): Netflix uses load balancing both for its public-facing services and internally. When you hit “Play” on a Netflix show, a content delivery network (CDN) load balancer directs you to an optimal server (perhaps the one cache server nearest to your location with that video). Internally, Netflix’s microservice architecture has load balancers (often using client-side load balancing with service discovery) to distribute requests among countless microservice instances. This ensures that the failure or slowness of one instance doesn’t cripple the entire streaming service.
• E-commerce (High Traffic Sales): During events like Black Friday sales, e-commerce giants like Amazon or Walmart rely heavily on load balancing. When traffic spikes dramatically, their load balancers scale up the number of server instances automatically (in the cloud) and spread out the influx of customer requests. This prevents the site from crashing under load and keeps checkout processes smooth, which directly translates to revenue. A famous example is how Amazon’s retail site stays stable on Prime Day – their auto-scaling load balancers handle sudden surges of traffic with ease, maintaining both reliability (no downtime) and performance (quick page loads).

These case studies show that effective load balancing is a cornerstone of scalability for large-scale systems. From FAANG companies to startups, anyone building a high-traffic service must use load balancing to achieve the reliability and speed users expect.

(For those interested in the nitty-gritty, we have an in-depth tutorial on how to design a load balancer from scratch, which walks through building a basic load balancer step by step – a great exercise for understanding what’s happening under the hood in these real-world systems.)

Mastering Load Balancing for System Design Interviews

Load balancing isn’t just important in real systems – it’s also a common topic in system design interviews. Interviewers at top tech companies love to ask questions that involve scaling a system, and that’s where you should bring up load balancing. Here are some technical interview tips to help you incorporate load balancing in your system design answers:

• Recognize When to Use It: If an interview question mentions high traffic, millions of users, or strict uptime requirements, that’s a cue to discuss load balancing. For instance, if asked to design a web application or a service like YouTube or Instagram, you should mention that multiple servers behind a load balancer will be needed to handle user load.
• Explain the Benefits: When you propose a load-balanced architecture, clearly state why. For example: “I’ll use a load balancer to distribute requests across multiple servers. This will improve reliability (if one server goes down, others take over) and performance (no single server becomes a bottleneck).” Showing you understand why we use load balancers is crucial.
• Discuss Key Aspects: Mention the basics such as health checks (“the load balancer will monitor server health and stop sending traffic to any server that fails”), sticky sessions if relevant (“if we need the user to stay on the same server for session consistency, we might enable session affinity”), and the possibility of multiple layers of load balancing for very large systems. You don’t have to be extremely detailed at an 8th-grade explanation level, but touching on these shows depth.
• Algorithms & Types (Briefly): In an interview, you might mention a simple algorithm like round robin for simplicity. If pressed, you can say there are other algorithms (least connections, etc.) but focus on concept rather than listing all algorithms. It’s more important to convey that some algorithm will ensure even distribution. Also, you can mention whether you envision a hardware appliance or a software (like an Nginx/HAProxy) load balancer, or even a managed cloud load balancer, depending on the design scenario.
• Avoid Single Points of Failure: Ironically, the load balancer itself could be a single point of failure. In interviews, you should mention that load balancers can be redundant too (e.g., having at least two in an active-passive or active-active setup, or using a DNS load balancing scheme to direct traffic to multiple load balancers). This shows you’re thinking about reliability at every level.
• Practice and Feedback: It helps to do mock interview practice focusing on system design scenarios. Try designing familiar systems (like a URL shortener, a social media feed, etc.) and make sure to include load balancing in your architecture diagram. Explain it to a friend or use a platform like DesignGurus.io to get feedback. The more you practice, the more naturally you’ll integrate load balancing into your answers.

Remember, demonstrating a solid grasp of load balancing can significantly boost your interview performance. It shows you understand scalability and reliability – qualities every big tech system needs. Many candidates memorize concepts, but being able to discuss real examples (like “Facebook uses multiple layers of load balancers to handle billions of requests”) can set you apart. For further reading and structured preparation, you can explore courses like Grokking the System Design Interview, which covers load balancing and other core concepts, providing mock interview scenarios and model answers.

Conclusion

Load balancing is the backbone of reliable and high-performance systems. By distributing requests across multiple servers, it prevents overload, reduces latency, and increases fault tolerance. In essence, load balancing makes sure your application stays online and snappy even as user demand grows or hardware fails. From the web’s biggest giants (who use layered and global load balancing strategies) to a basic startup web app, this concept is vital for anyone designing systems today.

If you’re preparing for system design interviews, mastering load balancing is a must. It not only helps you build better systems in the real world, but it also shows interviewers that you can design for scalability, reliability, and performance – key topics in any technical interview.

Ready to deepen your system design skills? DesignGurus.io is a leading platform for system design interview prep, created by ex-FAANG engineers who have been on both sides of the interview table. Check out our courses like Grokking System Design Fundamentals and Grokking the System Design Interview to learn proven strategies and get hands-on practice with scenarios involving load balancers, caching, databases, and more. Enroll today to gain the confidence and knowledge to ace your next system design interview!

FAQ: Load Balancing in System Design