How Do Load Balancers and Reverse Proxies Improve System Scalability?
Concept
Modern large-scale systems rely on load balancers and reverse proxies to distribute traffic efficiently, improve reliability, and scale horizontally.
While both components handle incoming requests, their roles, placement, and focus differ in system architecture.
1. Load Balancer — Distributing Traffic for Scalability
A load balancer evenly distributes client requests across multiple backend servers to ensure high availability and optimal resource utilization.
Core Responsibilities:
- Distribute load to multiple servers.
- Detect unhealthy nodes and reroute traffic automatically.
- Maintain session persistence (sticky sessions).
- Reduce single points of failure.
Common Load Balancing Algorithms:
- Round Robin: Each request goes to the next server cyclically.
- Least Connections: Directs traffic to the server handling the fewest active connections.
- IP Hash: Maps clients to specific servers based on IP.
- Weighted Distribution: Prioritizes servers based on capacity or performance.
Example (safe for MDX):
Client → Load Balancer → [Server1, Server2, Server3]
Real-World Tools: AWS Elastic Load Balancer (ELB), NGINX, HAProxy, F5, Google Cloud Load Balancing.
2. Reverse Proxy — Gateway for Control and Security
A reverse proxy sits in front of web servers and intercepts client requests before forwarding them. It acts as a single entry point for all client traffic, providing abstraction, security, and performance optimization.
Core Responsibilities:
- Caching static responses to reduce server load.
- Handling SSL termination (offloading HTTPS encryption/decryption).
- Rate limiting and access control.
- Request routing and compression.
- Shielding backend infrastructure from direct exposure.
Example (safe for MDX):
Client → Reverse Proxy → Web Servers
Real-World Tools: NGINX, Apache HTTP Server (mod_proxy), Traefik, Envoy.
3. Combined Usage — Layered Architecture
Load balancers and reverse proxies are often used together in large-scale distributed architectures:
| Layer | Component | Function |
|---|---|---|
| Edge Layer | Reverse Proxy | Security, SSL termination, caching |
| Middle Layer | Load Balancer | Request distribution, scaling |
| Backend Layer | Application Servers | Business logic and computation |
Example Flow:
Client → CDN → Reverse Proxy → Load Balancer → App Servers
This layered approach provides both horizontal scalability and resilience.
4. Benefits to System Scalability
| Benefit | Description |
|---|---|
| Increased Throughput | Distributes traffic evenly across nodes. |
| Fault Tolerance | Automatically removes unhealthy servers. |
| Elasticity | Supports autoscaling — add/remove servers dynamically. |
| Improved Security | Hides backend servers and filters malicious requests. |
| Performance Optimization | Enables caching, compression, and protocol upgrades (e.g., HTTP/2). |
5. Common Design Patterns
Active-Passive Failover
One node remains idle until a failure occurs — ensures high availability.
Global Load Balancing
Distributes requests across geographically distributed regions (using DNS-based or Anycast routing).
Application-Layer Routing
Reverse proxies can route based on content type, headers, or user region (e.g., /api vs /static).
6. Real-World Example
Netflix uses a combination of:
- Zuul (reverse proxy) for intelligent routing and API gateway functions.
- Elastic Load Balancers (AWS) for distributing traffic to microservices.
- Eureka for service discovery and dynamic instance registration.
This layered setup ensures global fault tolerance and near-zero downtime at scale.
7. Interview Tip
- Clarify placement: Load balancers distribute requests among servers; reverse proxies manage traffic before it hits them.
- Mention common tools and cloud-native equivalents (AWS ALB, NGINX, HAProxy).
- Discuss failover mechanisms, session persistence, and caching strategies.
- Use a diagram to show how both components fit in modern distributed systems.
Summary Insight
Load balancers scale systems by distributing traffic; reverse proxies secure and optimize it. Together, they form the backbone of high-performance, fault-tolerant, and horizontally scalable architectures.