Load Balancers

A load balancer is a critical component that distributes incoming network traffic across multiple backend servers to ensure no single server becomes overwhelmed. Load balancers improve application availability, reliability, and scalability by preventing server overload and providing failover capabilities. They act as a reverse proxy, sitting between clients and servers to optimize resource utilization and minimize response times.

Load Balancing Technologies

Envoy Proxy

Modern, high-performance edge and service proxy designed for cloud-native applications. Features advanced load balancing, observability, and service mesh capabilities.

NGINX

High-performance web server and reverse proxy that can act as a load balancer, API gateway, and static content server with advanced caching capabilities.

AWS Application Load Balancer

Layer 7 load balancer that makes routing decisions at the application layer, supporting HTTP/HTTPS with advanced request routing capabilities.

AWS Elastic Load Balancer

Classic Layer 4 load balancer that operates at the transport layer, distributing traffic based on IP addresses and ports with high availability.

Kubernetes

Container orchestration platform with built-in load balancing through Services, providing automatic service discovery and traffic distribution for containerized applications.

Core Concepts of Load Balancing

Load balancing might seem straightforward on the surface—just distribute traffic across servers, right? But there's actually a lot of nuance involved in doing it well. Understanding these core concepts will help you design systems that can handle real-world traffic patterns and failures gracefully.

Think of load balancers as intelligent traffic directors. They need to know which servers are healthy, how to route requests consistently when needed, and how to adapt when servers come online or go offline. This is especially important in modern cloud environments where servers are constantly scaling up and down based on demand.

Backend Servers: The pool of servers that receive distributed traffic from the load balancer.
Health Checks: Periodic monitoring of backend servers to ensure they are healthy and capable of handling requests.
Session Affinity/Sticky Sessions: Ensuring that requests from the same client are consistently routed to the same backend server.
Load Balancing Algorithms: Methods for determining how to distribute incoming requests across available servers.
High Availability: The ability to continue operating even when some backend servers fail.
Auto Scaling: Automatically adding or removing backend servers based on current load and demand.

Load Balancing Algorithms

Understanding different algorithms helps you choose the right approach for your specific use case. The choice of algorithm can significantly impact your application's performance, especially under different traffic patterns and server configurations.

In practice, you'll often start with simple algorithms like Round Robin for development and testing, then move to more sophisticated approaches as your traffic patterns become more complex. Companies like Netflix use multiple algorithms depending on the service—simple round robin for stateless services, but weighted algorithms for services where different server instances have varying capabilities.

Round Robin: Distributes requests sequentially across all available servers in a circular order
Weighted Round Robin: Assigns different weights to servers based on their capacity or performance characteristics
Least Connections: Routes requests to the server with the fewest active connections
Weighted Least Connections: Combines connection count with server weights for more sophisticated routing
IP Hash: Uses a hash of the client's IP address to consistently route to the same server
Random: Distributes requests randomly across available servers

Types of Load Balancers

Load balancers operate at different layers of the OSI model, each with distinct capabilities. Understanding this distinction is crucial because it affects both performance and the types of routing decisions you can make.

Layer 4 load balancers are like express lanes on a highway—they're fast because they only look at basic information like IP addresses and ports. They can't see what's inside your HTTP requests, but they're incredibly efficient at moving traffic. This makes them perfect for high-throughput applications where you don't need fancy routing logic.

Layer 7 load balancers, on the other hand, are more like smart traffic controllers. They can read HTTP headers, look at URLs, and even examine cookies to make intelligent routing decisions. Want to send mobile users to different servers? Route API calls differently than web pages? Layer 7 is your friend, though you'll pay a small performance cost for that intelligence.

Layer 4 (Transport Layer):

Routes traffic based on IP addresses and ports
Faster processing with lower latency
Protocol-agnostic (works with TCP, UDP)
Cannot inspect application content
Examples: AWS Classic ELB, HAProxy in TCP mode

Layer 7 (Application Layer):

Routes traffic based on HTTP headers, URLs, cookies
Content-aware routing and advanced features
SSL termination and HTTP compression
Higher latency due to deeper packet inspection
Examples: AWS ALB, NGINX, Envoy Proxy

Envoy Proxy in Microservices

Envoy has become the gold standard for microservice load balancing due to its advanced features and cloud-native design. If you're working with microservices—and let's be honest, if you're interviewing at any major tech company, you probably are—understanding Envoy is becoming as important as understanding HTTP.

What makes Envoy special is that it was designed by engineers at Lyft who were dealing with the same problems you'll face in production: thousands of services, constant deployments, and the need to debug issues quickly when things go wrong. They built Envoy to solve real problems, not theoretical ones.

Why Envoy Excels in Microservices

Envoy was built from the ground up for the challenges of microservice architectures. While traditional load balancers were designed for simpler times when you had a few web servers behind a load balancer, Envoy handles the complexity of hundreds or thousands of services talking to each other.

The magic of Envoy lies in its observability. When you have 200 microservices and something breaks, you need to know exactly where the problem is. Envoy gives you metrics, tracing, and logs that make debugging distributed systems actually manageable. This is why companies like Lyft created it in the first place—they needed something that could handle their massive microservice architecture.

One thing that sets Envoy apart is its approach to configuration. Instead of requiring restarts when you change settings, Envoy can update its configuration on the fly. This is crucial in production environments where you can't afford downtime just to adjust a load balancing policy.

Advanced Traffic Management:

Circuit breakers for fault tolerance
Retry policies and timeout configurations
Rate limiting and traffic shaping
Blue-green and canary deployments

Observability and Monitoring:

Detailed metrics for all traffic flows
Distributed tracing integration
Access logs with custom formatting
Health check monitoring and alerting

Service Mesh Integration:

Core component of Istio service mesh
Automatic mutual TLS between services
Service discovery and dynamic configuration
Policy enforcement and security controls

Production Features:

Hot restarts with zero downtime
Dynamic configuration updates
Multi-protocol support (HTTP/1.1, HTTP/2, gRPC)
WebSocket and TCP proxy capabilities

Kubernetes Load Balancing

Kubernetes provides multiple load balancing mechanisms for different scenarios. One of the beautiful things about Kubernetes is how it abstracts away much of the complexity of load balancing while still giving you the control you need when things get sophisticated.

If you're new to Kubernetes, the variety of options can feel overwhelming. But there's a logical progression: start with basic Services for internal communication, add Ingress when you need to expose things externally, and consider a service mesh when your microservice communication gets complex enough that you need more advanced features.

Kubernetes Load Balancing Types

Kubernetes makes load balancing feel almost magical—you deploy your application, and suddenly traffic is automatically distributed across your pods. But understanding the different types helps you choose the right approach for your specific needs.

Most developers start with ClusterIP services for internal communication between microservices. When you need to expose services to the outside world, you'll typically use an Ingress Controller, which gives you that Layer 7 intelligence we talked about earlier. The beauty is that Kubernetes handles most of the complexity for you.

Service meshes like Istio take this even further by adding a network layer that handles all the communication between your services automatically. It's like having a smart networking team that never sleeps, constantly monitoring and optimizing your traffic patterns.

Services:

ClusterIP: Internal load balancing within the cluster
NodePort: Exposes services on each node's IP at a static port
LoadBalancer: Integrates with cloud provider load balancers
ExternalName: Maps services to external DNS names

Ingress Controllers:

Layer 7 load balancing for HTTP/HTTPS traffic
Path-based and host-based routing
SSL termination and certificate management
Integration with external load balancers

Service Mesh:

Envoy-based solutions like Istio and Linkerd
Advanced traffic management and security
Automatic load balancing between service instances
Circuit breaking and retry policies

How to Use Load Balancers in a System Design Interview

When discussing load balancers in interviews, demonstrate understanding of both technical concepts and real-world applications. The trick is to show that you understand not just what load balancers do, but how they fit into the bigger picture of real systems that millions of people use every day.

Interviewers love when you can connect technical concepts to actual companies and their challenges. Instead of just saying "we need a load balancer for high availability," explain how Netflix handles traffic spikes during popular show releases, or how Uber ensures ride requests are routed efficiently across the globe.

Here are key points to mention:

High Availability: Explain how Netflix uses multiple load balancers across different availability zones to ensure their streaming service remains available even during hardware failures or data center outages.
Geographic Distribution: Discuss how Amazon routes users to the nearest regional data center using DNS-based load balancing, reducing latency for global e-commerce traffic.
Microservice Architecture: Describe how Uber uses Envoy proxies in their service mesh to handle thousands of microservices, providing advanced routing, circuit breaking, and observability.
Auto Scaling: Mention how Instagram automatically scales backend servers based on traffic patterns, with load balancers health-checking new instances before adding them to the pool.
SSL Termination: Explain how companies like Cloudflare terminate SSL at the load balancer level to reduce computational load on backend servers while maintaining security.
Session Management: Discuss trade-offs between stateless applications versus sticky sessions, using examples like shopping cart persistence in e-commerce platforms.

Load Balancer Placement Strategies

Understanding where to place load balancers in your architecture is crucial for optimal performance. This is one of those decisions that seems simple at first but gets more nuanced as your system grows.

Most developers start with a single load balancer in front of their web servers and call it done. But as your system scales, you'll discover that load balancers are like onions—they have layers. You might have an internet-facing load balancer for external traffic, internal load balancers for microservice communication, and specialized database load balancers for read/write splitting.

The key insight is that different parts of your system have different requirements. Your public API might need DDoS protection and SSL termination, while your internal service-to-service communication might prioritize low latency and high throughput. Each layer of load balancing can be optimized for its specific use case.

Internet-Facing Load Balancers: Handle external traffic from users, typically providing SSL termination and DDoS protection
Internal Load Balancers: Distribute traffic between internal services and microservices within your infrastructure
Database Load Balancers: Route read queries to read replicas while directing writes to the primary database
Multi-Tier Load Balancing: Implement load balancers at multiple levels (web tier, application tier, database tier) for comprehensive traffic distribution

Load Balancer Considerations

When designing systems with load balancers, consider these important factors. These aren't just theoretical concerns—these are the real-world issues that will keep you up at night if you don't plan for them properly.

Performance tuning often comes down to understanding your traffic patterns. Are your users mostly mobile with slower connections? You'll want aggressive compression. Dealing with lots of short-lived connections? Connection pooling becomes critical. The geographic distribution question is especially important for global applications—you don't want users in Tokyo hitting servers in Virginia if you can avoid it.

Reliability is where load balancers really shine, but it requires thoughtful configuration. The key is finding the right balance with health checks—too frequent and you'll waste resources, too infrequent and you'll route traffic to dead servers. Most teams learn this the hard way during their first production incident.

Performance:

Connection pooling and keep-alive settings
SSL termination vs SSL passthrough
Compression and caching capabilities
Geographic proximity to users

Reliability:

Health check frequency and thresholds
Failover mechanisms and backup servers
Load balancer redundancy (active-passive, active-active)
Graceful degradation during failures

Security:

DDoS protection and rate limiting
Web Application Firewall (WAF) integration
SSL/TLS certificate management
Access control and IP whitelisting

Example System Design Problems

Here are examples of system design problems where load balancers play a crucial role. These aren't just theoretical exercises—these are the types of real-world systems you'll be asked to design in interviews, and understanding how load balancers fit into each one is essential.

The key to nailing these questions is showing that you understand the different requirements each system has. YouTube needs to handle massive video files, WhatsApp needs real-time messaging, and Uber needs geographic distribution. Each of these drives different load balancing decisions.

Design YouTube: Implement multiple load balancing layers - CDN for video content, API gateways for service routing, and database load balancers for metadata queries across global infrastructure.
Design WhatsApp: Use load balancers to distribute WebSocket connections across chat servers, ensuring message delivery and real-time communication for billions of users.
Design Uber: Implement geographically-aware load balancing to route ride requests to the nearest data centers, optimizing for low latency in location-based services.
Design Netflix: Design a multi-tier load balancing strategy including CDN for video streaming, API load balancing for user preferences, and recommendation engine traffic distribution.
Design E-commerce Platform: Create load balancing for product catalogs, shopping carts, payment processing, and inventory management with different SLA requirements.

DeepSWE Recommendation

Learn Envoy for System Design Interview Success

Here's the reality: most of the services you'll design in system design interviews will be microservice-based architectures. Pretty much all the big tech companies (Google, Meta, Netflix, Uber, Airbnb, etc.) have moved to microservices, and many are using Envoy or some variation of it for load balancing and service communication.

Why This Matters for Interviews: Most system design content on the internet is outdated and doesn't include this information. While NGINX is still great for traditional web applications, if you're interviewing at modern tech companies, knowing Envoy will set you apart because it's what they actually use in production.

What Makes Envoy Different:

Service Mesh Ready: It's the core of Istio, which many companies use for microservice communication
Built for Dynamic Environments: Unlike traditional load balancers, Envoy handles the constantly changing nature of containerized services
Rich Observability: Provides the kind of monitoring and tracing that's essential when you have hundreds of microservices

Focus Areas for Interviews:

How Envoy handles service discovery in Kubernetes
Circuit breakers and how they prevent cascading failures
Load balancing algorithms for microservice traffic
How service meshes simplify security with automatic mTLS

Interview Edge: When you mention Envoy in your system design, you're showing that you understand modern production architectures, not just textbook examples. Companies want to hire people who know the tools they actually use, and Envoy is increasingly becoming that tool.

The time investment is worth it - once you understand Envoy, you'll also understand service meshes, which is where the industry is heading for microservice management.