How do load balancers actually work in system design?
When interviewers ask,
“How would you distribute requests among multiple servers?”
they’re really testing whether you understand load balancing — the backbone of scalability and availability in modern distributed systems.
1️⃣ What is a load balancer?
A load balancer evenly distributes incoming network traffic across multiple backend servers (or nodes) so that no single server becomes a bottleneck.
Think of it like a traffic cop directing cars into multiple lanes — ensuring smooth flow even during rush hour.
Load balancers help you achieve:
- Scalability (handle more users by adding servers)
- High availability (redirect traffic from failed nodes)
- Performance (reduce latency via smart routing)
🔗 Learn the basics: Load Balancer vs Reverse Proxy vs API Gateway
2️⃣ The two main types: L4 vs L7 load balancing
| Type | Operates At | Example Protocol | Use Case |
|---|---|---|---|
| Layer 4 (Transport) | TCP/UDP | Nginx Stream, AWS NLB | Efficient routing without inspecting traffic |
| Layer 7 (Application) | HTTP/HTTPS | Nginx, HAProxy, Envoy | Smart routing based on URLs, headers, cookies |
Tip:
Mention that L7 balancers can make intelligent decisions based on application data, like routing /api/payments to one service and /api/feeds to another.
3️⃣ Common load balancing algorithms
| Algorithm | How It Works | When to Use |
|---|---|---|
| Round Robin | Sends each request to the next server in order | Default, simple systems |
| Least Connections | Routes to the server with the fewest active connections | Variable load per request |
| IP Hash | Routes based on user’s IP | Sticky sessions |
| Weighted Round Robin | Gives heavier servers more traffic | Mixed hardware capacity |
| EWMA (Adaptive) | Considers latency and load dynamically | High-performance systems |
🔗 Related concept: Scaling 101: Learning for Large System Designs
4️⃣ How load balancers maintain health and resilience
Load balancers regularly probe each backend with health checks (ping or HTTP requests). If a node fails, traffic is instantly rerouted to healthy instances.
To make your answer stronger, add:
“In a multi-region setup, I’d use DNS-level or Global Server Load Balancing (GSLB) to route users to the nearest healthy region.”
🔗 Explore further: High Availability System Design Basics
5️⃣ Handling sticky sessions and scaling pitfalls
Many candidates forget this one. Explain that sticky sessions (session affinity) can cause uneven load or downtime if a node fails. Always recommend:
- Stateless session handling
- Or storing sessions in Redis or a shared cache
That earns extra points in interviews.
6️⃣ What to draw during the interview
┌──────────────┐
User ──► │ Load Balancer│──► App Server 1
│ (L4/L7) │──► App Server 2
└──────────────┘ ...
Then mention that downstream layers (DB, cache) also need redundancy and replication.
💡 Interview Tip
When asked “How does your system scale with more traffic?”, start with:
“I’d introduce an L7 load balancer like Nginx or HAProxy to distribute requests evenly, handle failover, and enable auto-scaling.”
It’s concise, clear, and system-architect level.
🎓 Learn More
Master load balancing and distributed system reliability inside:
Both courses explain advanced load balancing — including GeoDNS, Anycast, and Global Server Load Balancing used at scale by Google and Netflix.
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