This content originally appeared on DEV Community and was authored by CodeWithVed
Introduction
Load balancing is a critical concept in system design, ensuring that distributed systems handle traffic efficiently, maintain high availability, and scale seamlessly. In technical interviews, load balancing questions test your ability to design scalable architectures and optimize performance under varying workloads. Whether it’s distributing requests across servers or managing database queries, understanding load balancing is essential for building robust systems. This post explores load balancing strategies, their implementation, and how to ace related interview questions.
Core Concepts
Load balancing distributes incoming network traffic or computational workloads across multiple servers or resources to prevent any single server from becoming a bottleneck. It enhances scalability, reliability, and performance in distributed systems.
Types of Load Balancers
- Hardware Load Balancers: Physical devices (e.g., F5, Citrix) that manage traffic at the network level. They’re fast but expensive and less flexible.
- Software Load Balancers: Applications like NGINX, HAProxy, or cloud-based solutions (e.g., AWS Elastic Load Balancer) that offer flexibility and cost-efficiency.
- Cloud-Native Load Balancers: Managed services like AWS ALB/ELB, Google Cloud Load Balancing, or Azure Load Balancer, integrated with cloud ecosystems.
Load Balancing Algorithms
- Round Robin: Requests are sent to servers in a circular order. Simple but doesn’t account for server load or capacity.
- Least Connections: Directs traffic to the server with the fewest active connections, ideal for uneven workloads.
- IP Hash: Routes requests based on the client’s IP address, ensuring session persistence (e.g., for stateful applications).
- Weighted Round Robin/Least Connections: Assigns weights to servers based on capacity, favoring more powerful servers.
- Random: Distributes requests randomly, useful for large clusters with similar servers.
Key Features
- Health Checks: Load balancers monitor server health (e.g., via heartbeats) and route traffic only to healthy servers.
- Session Persistence: Ensures requests from the same client go to the same server (e.g., for shopping cart sessions).
- SSL Termination: Handles SSL decryption at the load balancer to offload servers.
- Global Server Load Balancing (GSLB): Distributes traffic across geographically dispersed data centers, often using DNS.
Diagram: Load Balancing Architecture
[Client Requests] --> [Load Balancer] --> [Server 1, Server 2, Server 3]
(Health Checks, Algorithm: e.g., Least Connections)
(Session Persistence, SSL Termination)
Placement
- Layer 4 (Transport Layer): Operates at TCP/UDP level, forwarding packets based on IP and port. Fast but limited to network-level decisions.
- Layer 7 (Application Layer): Understands application protocols (e.g., HTTP), enabling advanced routing based on URLs, cookies, or headers. More flexible but computationally intensive.
Interview Angle
Load balancing is a common topic in system design interviews, especially for designing scalable web services or microservices. Common questions include:
- How would you design a load balancer for a high-traffic web application? Tip: Discuss algorithm choice (e.g., Least Connections for uneven loads), health checks, and session persistence. Mention cloud-native options like AWS ELB for scalability.
- What’s the difference between Layer 4 and Layer 7 load balancing? Approach: Explain that Layer 4 is faster but less flexible, while Layer 7 supports advanced routing (e.g., URL-based). Use examples like NGINX (Layer 7) vs. IPVS (Layer 4).
- How do you handle a failing server in a load-balanced system? Answer: Describe health checks (e.g., periodic HTTP requests) and automatic rerouting to healthy servers. Discuss failover strategies like auto-scaling.
- Follow-Up: “How would you ensure session persistence in a stateless application?” Solution: Use sticky sessions (IP Hash or cookie-based) or store session data in a centralized store like Redis to make servers stateless.
Pitfalls to Avoid:
- Forgetting health checks or failover mechanisms, which are critical for reliability.
- Ignoring session persistence for stateful applications, leading to broken user experiences.
- Overcomplicating with custom algorithms when simple ones (e.g., Round Robin) suffice for the scenario.
Real-World Use Cases
- Netflix: Uses AWS ALB and NGINX for load balancing across its microservices, leveraging Layer 7 routing to direct traffic based on API endpoints.
- Google Cloud: Employs Google Cloud Load Balancing for global distribution, using GSLB to route users to the nearest data center for low latency.
- E-commerce Platforms: Amazon uses ELB with sticky sessions to ensure shopping cart consistency across user requests.
- Content Delivery Networks (CDNs): CDNs like Cloudflare use load balancing to distribute traffic across edge servers, optimizing for proximity and performance.
Summary
- Load Balancing: Distributes traffic across servers to ensure scalability, reliability, and performance.
- Key Algorithms: Round Robin, Least Connections, IP Hash, and weighted variants cater to different workloads.
- Layer 4 vs. Layer 7: Layer 4 is faster but basic; Layer 7 enables advanced routing but is slower.
- Interview Tips: Focus on algorithm choice, health checks, and session persistence. Use cloud-native examples to show practicality.
- Real-World Impact: Powers scalable systems like Netflix, Amazon, and CDNs, ensuring high availability and low latency.
Mastering load balancing equips you to design scalable, fault-tolerant systems and confidently tackle system design interviews.
This content originally appeared on DEV Community and was authored by CodeWithVed
CodeWithVed | Sciencx (2025-08-20T18:29:39+00:00) Mastering Load Balancing for System Design Interviews. Retrieved from https://www.scien.cx/2025/08/20/mastering-load-balancing-for-system-design-interviews/
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