Step-By-Step Approach for Load Balancing in System Design Interviews

Mastering load balancing is essential for building scalable systems and acing system design interviews.

This guide will explain what load balancing is, why it’s important, and when to use it.

We’ll also cover common load balancing techniques (like Round Robin, Least Connections, and hashing), discuss challenges (latency, failover, caching, stateful vs. stateless), and provide a step-by-step approach to talk about load balancing in an interview.

By the end, you’ll know key strategies, best practices, and pitfalls to avoid so you can handle high traffic efficiently in your designs.

What Is Load Balancing and Why Do We Need It?

Load balancing is the process of distributing incoming tasks or network traffic across multiple servers (or other computing nodes) to improve the performance and reliability of a system.

Instead of one server handling all requests, a load balancer acts as a traffic cop that sits in front of your servers and routes client requests to available, healthy servers in the group.

This prevents any single server from being overwhelmed and ensures no resources are sitting idle.

Why Is Load Balancing Important?

In modern applications that serve thousands or millions of users simultaneously, load balancing provides several critical benefits.

It improves an application’s availability, scalability, and performance . Key advantages include:

• Higher Reliability (High Availability): If one server goes down, traffic can be redirected to others, so the system stays up. In fact, load balancing is a form of high availability – if your web server crashes, the load balancer can instantly route users to a backup server.

• Horizontal Scalability: You can add more servers to handle increasing traffic. Spinning up multiple instances of a service isn’t effective without a load balancer to distribute traffic efficiently. With load balancing, you can scale out instead of relying on one huge server. Read about the horizontal scaling challenges.

• Better Performance: Spreading out requests prevents any single machine from becoming a bottleneck, maintaining fast response times. Even under heavy load, a balanced cluster can serve more users with lower latency.

• Maintenance Flexibility: With a load balancer, you can take servers down for updates one at a time. Users won’t notice because traffic is routed to the remaining servers, enabling zero-downtime deployments.

When Should You Use Load Balancing?

Essentially any time you have more traffic or workload than one server can handle reliably, or when you need your service to stay online even if a server fails.

For example, if you anticipate high user volumes, spikes in requests, or require 24/7 uptime, a load balancer is critical from the start.

On the other hand, very simple projects with minimal traffic might not need a load balancer initially – adding one too early can introduce unnecessary complexity.

(In fact, a single modern server can handle a surprising number of requests; you can always introduce a load balancer later once you grow.)

In system design interviews, if the system’s scale or reliability is a concern, it’s usually a signal to discuss load balancing as part of your solution.

Common Load Balancing Techniques (Algorithms)

Load balancers use different algorithms to decide how to distribute incoming requests among a pool of servers.

The choice of algorithm impacts how evenly and efficiently traffic is spread. Here are some of the most common load balancing techniques:

1. Round Robin

This is the simplest and one of the most popular algorithms. The load balancer maintains a list of servers and sends each new request to the next server in line, looping back to the first server after reaching the end of the list.

Essentially, it rotates through the servers in order (like dealing cards in a game) so that each server gets an equal share of traffic.

Round Robin works well when all servers have similar capacity and there’s a steady flow of requests.

(Tip: A variation called Weighted Round Robin allows you to give more powerful servers a greater proportion of requests .)

2. Least Connections

In this dynamic algorithm, the load balancer tracks the number of active connections (or requests) each server is handling and always directs new requests to the server with the fewest active connections at that moment.

The idea is to even out the load based on usage – a server that’s busy with many clients will get less new traffic, while an underutilized server gets more.

This method is useful if requests can vary in complexity or length, ensuring no single server gets overloaded with too many active tasks.

3. Hash-Based (Consistent Hashing/Affinity)

A hash-based load balancing algorithm uses a key (like the client’s IP address, user ID, or request URL) to determine which server should handle a request.

For example, IP hashing computes a hash of the client’s IP and consistently maps that client to the same server on each visit.

Similarly, hashing a user ID could ensure that user’s requests always go to the same backend server. This technique is often used to achieve session affinity (a.k.a. “sticky sessions”), meaning a user’s session data remains on one server. The benefit is that it can improve cache hit rates and avoid reloading user data on each request.

However, if the chosen server goes down, the load balancer must remap the user to a different server (potentially causing that user’s session to reset).

Consistent hashing is a special hashing approach that minimizes disruption when servers are added or removed. It’s commonly used in distributed caching and can be applied in load balancing so that the mapping of keys to servers is stable even as the pool changes.

Other algorithms

There are many other load balancing strategies and variations.

For instance, Random selection simply picks a random server (easy to implement but not as balanced as round robin).

Least Response Time sends traffic to the server with the quickest current response or lowest latency.

Resource-based algorithms consider server load (CPU, memory) in real time.

In practice, Round Robin and Least Connections (and their weighted versions) cover most needs, with hashing for specific use-cases like session stickiness.

The key is to choose an approach that matches your system’s behavior: if all servers are equal, round robin is a good default (and is the default in many load balancers); if some servers are beefier, use weighted; if certain requests are heavy, least connections can help; if you need user affinity, hashing is the way to go.

Find out the differences between Load balancer and API Gateway.

Challenges in Load Balancing

While load balancing greatly improves a system’s scalability and reliability, it also introduces some challenges and trade-offs. When designing or discussing a system with load balancers, be aware of the following considerations:

1. Latency Overhead

Introducing a load balancer adds an extra network hop between clients and servers. This additional step can increase latency slightly, as each request must go through the load balancer before reaching a server.

In most cases the delay is very minimal (microseconds to a few milliseconds), especially if the load balancer is in the same data center as the servers.

However, it’s still something to mention in interviews – you’re adding a bit of complexity and should note that a poorly configured or distant load balancer could slow down responses.

The solution is to use efficient load balancing algorithms and place the load balancer as close as possible to your servers or users to minimize the impact.

2. Single Point of Failure & Failover

If not designed carefully, a load balancer itself can become a single point of failure.

Think about it – all traffic goes through the load balancer, so if that LB node crashes, your entire application could become unreachable!

This is a common mistake: solving one bottleneck (the single server) but unintentionally creating another (the single load balancer). To address this, you should always plan for redundant load balancers.

For example, you might have two load balancers in an active-passive setup (if the primary fails, the secondary takes over automatically), or even an active-active cluster of LBs.

In cloud environments, many load balancer services have built-in redundancy across multiple availability zones.

The main point is to ensure failover mechanisms are in place so that the load balancing layer doesn’t bring your system down. (We’ll cover this again in best practices.)

3. Caching and Session Persistence

Load balancing can complicate server-side caching and session management.

Suppose each server keeps a local cache of expensive computations or user session data. With a load balancer, a user’s first request might hit Server A (which then caches some data in memory), but the second request might get routed to Server B, which doesn’t have the cached data or session info.

Inconsistencies like this can lead to lower cache hit rates and even data loss in the case of sessions.

For example, if a user logs in and their session is stored in Server A’s memory, then a subsequent request routed to Server B would not find that session and might force the user to re-login or lose their progress. There are a few ways to handle this:

• Sticky Sessions (Stateful LB) Configure the load balancer to consistently send the same user to the same server for the duration of their session. This ensures session data and cached content remain warm on that server. The downside is it can cause uneven load distribution (some servers get many “sticky” users) and doesn’t help if that server crashes.

• Distributed Caching / Session Store: A more scalable approach is to store session state or cache data in a shared datastore (like a distributed cache or database) accessible by all servers. This way, even if the load balancer sends a user to a different server next time, that server can fetch the user’s session or cached data from the central store. This allows the load balancer to remain stateless and free to route any request to any server.

• Depending on the use-case, you might combine both (e.g., enable stickiness only for specific scenarios or use a short TTL on sticky sessions).

• Stateful vs. Stateless Load Balancers: Related to the above point is whether the load balancer itself is stateful. A stateless load balancer does not store any session information; it treats each request independently and makes a routing decision (often based on a simple algorithm or hash) without remembering past requests.

  Stateless load balancing is great for most scenarios because it’s simple and fast – e.g., for a static website, users wouldn’t notice or care which server serves each page.

  On the other hand, a stateful load balancer keeps track of sessions or connections, ensuring all requests from a given client go to the same server (this is essentially what sticky sessions are).

  The challenge here is that the load balancer has to maintain a table of active sessions -> server mapping, which consumes memory and makes the LB more complex. It can also become problematic if the LB instance fails (since that state would be lost, unless replicated).

  In interviews, mention that ideally we keep services stateless – meaning the application handles state via external storage – so the load balancer can remain stateless too. Use stateful LB (affinity) only when necessary (for example, real-time gaming servers might need the same server to handle all moves of a player for low latency reasons).

  Always highlight the trade-off: stateful LB = session consistency at the cost of flexibility and even load distribution.

Step-by-Step Approach for Discussing Load Balancing in a System Design Interview

When you're asked a system design question, you should tackle load balancing in a clear, structured way. Here’s a step-by-step approach to ensure you cover the key points about load balancers in your interview:

1. Recognize When Load Balancing Is Needed

Begin by identifying if the problem requires a load balancer. If the system needs to handle large scale (many concurrent users or requests) or high availability, state that you would use multiple servers behind a load balancer.

This shows the interviewer you’re considering scaling from the start.

Remember, adding more servers alone doesn’t help unless you distribute load – “spinning up multiple instances of a service is not possible without a load balancer directing traffic to the cluster”.

For example, you might say: “Since we expect up to X million requests per day, a single server might not suffice. I’d use a load balancer to distribute requests across multiple servers for both scaling and redundancy.”

2. Insert the Load Balancer in Your Architecture Diagram

Clearly mention where the load balancer sits in your design. Typically, it’s at the front, receiving all client requests and then forwarding them to one of the application servers.

You can describe it like, “Users hit the load balancer at <endpoint>, and the LB will route the request to one of the app servers in our server pool.”

Specify what’s being load balanced – e.g., between web servers serving identical content. If relevant, mention the type of load balancer (like a cloud LB, Nginx/HAProxy, or even DNS-based round-robin) and the level at which it operates (network layer vs application layer) in simple terms.

For most web systems, an L7 (application-layer) load balancer is appropriate because it can understand HTTP and do smarter routing. Keep this explanation brief and focused on how it improves the design.

3. Explain the Load Balancing Strategy (Algorithm)

Don’t just say “I’ll use a load balancer” and stop there. Interviewers expect you to know how it balances the load. So, mention the algorithm or policy you’d use.

For instance, you might say, “We can start with a simple Round Robin approach to evenly distribute incoming requests to each server in turn.” If there’s a reason to choose one algorithm over another, explain it.

Maybe: “If one video processing request can tie up a server longer than another, we’d use Least Connections so we don’t overwhelm one server.”

The key is to explicitly discuss load balancing techniques like round-robin vs least-connections for the scenario.

This shows depth. You can also mention if you need session stickiness (like “we’ll enable sticky sessions so that each user stays on the same server after login for session consistency”), or if a hash-based routing would help (for example, hashing on user ID to keep all requests from a user on one server to improve cache hits).

Tailor your choice to the question’s context, but make sure to state a choice – it’s worse to be silent on how the LB works. (It’s perfectly fine to assume a default like round robin if nothing special is needed.)

4. Address Session State and Caching (Stateless vs Sticky)

If the system involves user sessions or any form of user-specific data that might be stored in memory, bring up how to handle that with multiple servers. Interviewers love to see that you remember this detail.

You can approach it like: *“Since users will be logged in, we need to ensure the load balancer doesn’t break their sessions.

The ideal way is to keep the service stateless – for example, store session data in Redis or a database so any server can handle any request. That way, the load balancer can freely send each request to any server.”*

This demonstrates a solid understanding of stateless design.

Alternatively, mention sticky sessions if you think it’s warranted: *“We could enable session affinity on the load balancer, meaning once a user is connected to Server A, all their requests stick to Server A.

This makes session handling simpler, but the downside is it can imbalance traffic.”* Then you might add how to mitigate that (like using it only per session and not per user forever, etc.).

The interviewer will appreciate that you are considering stateful vs stateless load balancing trade-offs upfront. (If you already explained this in the algorithm step, you can combine them, but often it’s worth spelling out session management separately.)

5. Implement Health Checks and Failover

Next, talk about how the load balancer will handle server failures or unhealthy instances. A good load balancer doesn’t just randomly send traffic; it should actively monitor the health of backend servers.

Explain that the LB will periodically ping or check each server (for example, with an HTTP heartbeat or ping) to see if it’s responding. “If a server is down or not responding in time, the load balancer will stop sending traffic to it and reroute to other servers,” you might say.

This ensures the system is resilient. It’s a best practice to mention that health checks prevent sending requests to a dead node.

Also, if a server recovers or new servers are added (perhaps for auto-scaling), the LB should detect and include them in the rotation.

In an interview, you could add: “Our load balancer will perform health checks on the instances – any server that fails will be taken out of rotation to maintain a good user experience.” This shows you’re designing for fault tolerance.

6. Plan for Load Balancer Redundancy

Finally, don’t leave the load balancer as a single point of failure.

Interviewers often expect you to mention this as a follow-up. Describe how you’d avoid the LB itself becoming a bottleneck or failing. For example, *“We would have a secondary load balancer in hot standby mode.

If the primary load balancer goes down, the secondary will take over seamlessly.”*

If you’re using a cloud service, you can mention that those are typically redundant by default.

The goal is to convey that even the load balancing layer is highly available. As one resource puts it, distributing load over a cluster means no one server is a single point of failure – and the load balancer must also not become a single point of failure.

You might implement this with multiple load balancer instances behind a virtual IP or using DNS round-robin between two load balancers. In any case, explicitly state that you have a plan for LB failover. This is often the difference between a good and great system design answer.

Following these steps, you will have covered the need for load balancing, how it works, how it deals with state, and how it stays reliable. This structured approach shows the interviewer you understand load balancing deeply and can integrate it into system designs confidently.

Learn how to design load balancer from scratch.

Recommended System Design Resources

To further improve your system design skills (including load balancing and beyond), here are some highly regarded resources:

• Grokking System Design Fundamentals – Learn the building blocks of system design in a beginner-friendly way, covering core concepts and simple examples.

• Grokking the System Design Interview – A popular course featuring a variety of system design interview questions and step-by-step solutions (including detailed discussions on topics like load balancing, caching, etc.).

• Grokking the Advanced System Design Interview – For those who want to go a step further, this covers complex scenarios and advanced concepts to tackle even the toughest design problems.

These courses (from DesignGurus.io) offer structured guidance and real-world examples that can help reinforce the concepts we discussed.

Best Practices for Load Balancing

When designing real systems (and to impress in interviews), keep in mind some best practices for load balancing:

• Eliminate Single Points of Failure: Always use at least two load balancer instances or a highly available load balancer service. Redundancy at the load balancer level is crucial so that your system stays up even if one LB node fails. (As mentioned, the load balancer itself should not be a single point of failure .)

• Use Health Checks and Monitoring: Configure your load balancer to regularly check the health of backend servers (via ping, HTTP endpoint, etc.) and remove or skip any server that isn’t responding properly. Also monitor the load balancer’s performance itself. This ensures users aren’t sent to downed servers and helps you detect issues early. In practice, you should also monitor metrics like server response times, connection counts, and error rates to decide when to add more servers.

• Prefer Stateless Sessions: Aim to keep your application servers stateless by externalizing session data. For example, store user sessions in a distributed cache or database accessible to all servers. This way, the load balancer can truly distribute any request to any server without worrying about session stickiness. Relying heavily on sticky sessions can limit scalability and hurt balanced distribution (if one server gets a disproportionate share of “sticky” users). Only use session affinity if absolutely necessary for the use case. A stateless approach makes your system more flexible and easier to scale horizontally.

• Choose the Right Algorithm (Keep it Simple): Select a load balancing strategy that fits your traffic pattern, but don’t over-engineer it. In many cases, a simple round-robin works well to start with. If you notice uneven load or have heterogeneous servers, then consider more advanced algorithms (like least connections or weighted distribution). The key is to not stick blindly to one method – be prepared to adjust as you monitor your system. Most load balancers allow algorithm configuration changes fairly easily.

• Leverage Cloud Load Balancing Services if Possible: In real-world scenarios, managed services like AWS Elastic Load Balancing, Google Cloud Load Balancer, or Azure LB are battle-tested and can automatically handle many of these best practices (health checks, multi-AZ redundancy, scaling) out of the box. Using them can save you from re-inventing the wheel. In an interview you can mention using a cloud load balancer for production for reliability, while focusing your discussion on the conceptual behavior (the interviewer typically cares that you know what it does more than how to configure AWS ELB).

• Test Under Load: Ensure you test your system with the load balancer in place under high traffic conditions. Sometimes issues like improper timeouts, connection limits, or algorithm edge cases only appear under stress. From an interview perspective, mentioning load testing and monitoring shows proactiveness (though it's usually a stretch topic; focus on design first).

By following these best practices, you’ll design a robust load balanced architecture that maximizes uptime and performance.

Check out the uses of Load balancing.

Common Mistakes to Avoid

Finally, be aware of some frequent mistakes or oversights related to load balancing in system design, so you can avoid them:

• Forgetting the Load Balancer in a High-Scale Design: A classic mistake is to design a system that clearly needs multiple servers (due to high read/write traffic, etc.) but not include a load balancer to distribute load. If the interview scenario expects scaling, always incorporate a load balancer when you have more than one server serving the same role. Not mentioning it will make your design incomplete.

• Assuming a Single Load Balancer is Enough: Don’t introduce a load balancer and then make it the Achilles’ heel of the system. As discussed, a lone load balancer can take down everything if it fails. Avoid designs where the LB isn’t redundant. Even in an interview hypothetical, you should say something like, “we’d have a second load balancer for failover to avoid downtime.” Not addressing this is a common omission.

• Not Handling Session Stickiness or State: If your system requires user login or shopping carts or any persistent user context, it’s a mistake to ignore how load balancing affects that. Simply distributing users to random servers without a plan for sessions can cause users to get logged out or lose data. Make sure you either mention sticky sessions or a shared session store. Failing to consider this will signal to the interviewer that you don’t understand stateful vs stateless concerns in distributed systems.

• Overusing Sticky Sessions (Stateful LB) Unnecessarily: The opposite scenario can also be a mistake – making every user stick to one server when it’s not truly needed. This can defeat the purpose of load balancing by creating imbalance. As noted earlier, sticky sessions can lead to uneven load distribution and negate some benefits of load balancing. Use them judiciously. In interviews, if you mention them, also mention the downsides so it’s clear you’d prefer a stateless design when possible.

• Neglecting to Specify the Load Balancing Algorithm: Simply saying “I’ll add a load balancer” without explaining how it distributes traffic is a missed opportunity (and in some cases a mistake if a specific strategy is needed). Always articulate the algorithm or policy (round robin vs least connections, etc.) when discussing your load balancer. It shows completeness. An interviewer might ask, “How does the load balancer decide where to send requests?” – you should be ready with an answer, not caught off guard.

• Ignoring Performance Impact: While usually minor, the load balancer does add an extra network hop. A mistake would be to propose a far-away load balancer or one per request in a microservice call chain without considering latency. In most interview scenarios this won’t be a big issue, but if you’re designing a low-latency system, be mindful of the overhead.

By being mindful of these mistakes, you can double-check your design in the interview and ensure you address the important points proactively.

Combining study resources with the tips from this guide will prepare you to confidently handle load balancing questions in your system design interviews.