Scalability and Load Balancer Questions for System Design Interviews

When it comes to system design and backend architecture interviews, two of the most frequently tested topics are scalability and load balancing. Interviewers want to assess your ability to build systems that perform well under heavy traffic and scale seamlessly. This page features a comprehensive set of scalability interview questions and load balancer interview questions designed to help you prepare for high-impact technical roles.

Mastering Interview Questions on Scalability

Interview questions on scalability often focus on your ability to design systems that handle increased traffic, data volume, or user growth efficiently. These software scalability interview questions may include:

• • How would you scale a web application to handle 1M+ users?
• • Vertical vs. horizontal scaling: When and why?
• • Database sharding and replication strategies
• • Caching for read-heavy systems
• • Stateless architecture and microservices patterns

Understanding how to scale backend services, databases, and APIs is a critical skill for software engineers, architects, and DevOps professionals alike.

Load Balancer Interview Questions and Best Practices

Interview questions on load balancer usage explore your knowledge of distributing traffic efficiently across multiple servers. Key areas include:

• • Layer 4 vs. Layer 7 load balancers
• • Common load balancing algorithms include IP Hashing, Least Connections, and Round Robin.
• • Health checks and failover mechanisms
• • Global vs. local load balancing
• • Load balancing in cloud-native environments (AWS ELB, GCP Load Balancer, etc.)

These load balancer interview questions are designed to test both your theoretical understanding and your ability to apply best practices in real-world scenarios.

By practicing these scalability and load balancer interview questions, you’ll gain the confidence and clarity to tackle system design interviews at top tech companies. Dive into this curated deck and learn how to build systems that are not just functional—but built to last at scale.

Maps keys to nodes so that only a few keys move when nodes join/leave.

Simulate route withdrawal at a POP

Use ML forecasts on traffic patterns to scale before spikes occur.

Advertise the same IP from multiple locations; routing sends clients to nearest point.

Store session data in an external store (e.g.

Use canary environment

L4 routes by IP/port (transport); L7 routes by HTTP data like URL and headers.

Offloads encryption/decryption work from backend servers.

Mark instance draining

Uses HTTP/2 multiplexing and built-in client-side LB with richer health checks.

It cycles through servers in order

CPU utilization crossing a threshold; average request latency.

Periodic probes (TCP/HTTP) to verify if a server can accept traffic.

Complex matching (headers

Run two identical environments (blue/green); switch traffic via LB after testing.

To distribute incoming requests across multiple servers to optimize resource use.

Requests go to the server with the fewest active connections.

The ability of a system to handle growing amounts of work by adding resources.

Ensuring a client’s requests go to the same backend instance for a session.

When many nodes spin up simultaneously on a spike and overwhelm downstream systems.

DNS can return multiple IPs (round-robin DNS) to distribute traffic across servers.

A buffer of pre-initialized instances ready to serve traffic immediately.

You can send more traffic to higher-capacity nodes by assigning weights.

Vertical scaling adds CPU/RAM to a single machine; horizontal adds more machines.

Use global DNS LB → regional LB → local LB for fault isolation and latency optimization.

Edge offloads CPU but risks unencrypted traffic in internal network.

To stop cascading failures by halting calls to unhealthy services.

High churn can overload LB even with moderate QPS.

It avoids single points of failure and offers practically infinite growth by adding nodes.

To serve repeatable content quickly and reduce backend load.