AI & Technical question
Design a simple load balancer for Google.com. What data structures would you use?
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What this question tests
A PM-facing technical systems question testing whether the candidate understands load balancing concepts and data structures well enough to have an informed technical conversation with engineers, not to write production code.
How to approach it
- Clarify the goal: distribute incoming requests to Google.com across many backend servers to keep latency low and avoid overloading any single server.
- State the two core sub-problems: choosing which server should handle each request, and tracking server health/capacity in real time.
- Propose an algorithm: consistent hashing or weighted round-robin, explaining that consistent hashing minimizes redistribution when servers are added or removed.
- Propose data structures: a hash ring (sorted structure, e.g., a balanced tree or sorted array) for consistent hashing, and a min-heap or priority queue to track least-loaded servers for least-connections routing.
- Address health checking: a separate lightweight service pinging servers on an interval, updating a shared registry (e.g., a hash map of server ID to health/load status) that the load balancer reads.
- Address failure handling: what happens when a server drops out (redistribute via the hash ring) and geographic routing (DNS-level routing to regional load balancers before this local balancing even applies).
What a strong answer includes
- Names concrete data structures (hash ring, min-heap/priority queue, hash map for health status) tied to a specific algorithm, rather than hand-waving 'a load balancer.'
- Explains consistent hashing's specific benefit (minimal redistribution on scale up/down) as the reason to prefer it over simple modulo hashing.
- Addresses failure and health-check handling as a first-class part of the design, not an afterthought.
- Shows PM-level technical fluency: understands the trade-offs well enough to discuss with engineers, without claiming to write the actual implementation.
Common mistakes
- Describing load balancing only conceptually with no data structures at all, missing the explicit ask.
- Proposing simple round-robin without addressing server failure or uneven load.
- Ignoring geographic/DNS-level routing, which is how Google.com actually handles global scale before local load balancing.
Likely follow-up questions
- How would you handle a sudden traffic spike (e.g., a major news event)?
- How is this different at a global CDN level versus within one data center?
- How would you decide between consistent hashing and least-connections routing?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture