How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a medium difficulty System Design question, commonly asked during Technical Screen rounds at Palo.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Palo during technical interviews.

Design a thread-safe high-QPS rate limiter

Quick Overview

A Palo Alto Networks system-design screen: design and implement a thread-safe, high-QPS rate limiter that enforces per-identity request quotas. The answer covers algorithm choice (token bucket vs. fixed/sliding window), atomic per-key state with locks or CAS, hot-path performance, bounded-memory eviction, distributed scaling (Redis+Lua, token leasing, consistent-hash partitioning), and rejection semantics.

Question

You are building a backend service that must enforce API rate limits. Design and (at a high level) implement a rate limiter, then work through the following:

Per-identity limit. Enforce a limit per identity (e.g., per user ID, per IP, or per API key), such as N requests per second .
Thread safety. The limiter must be safe when accessed concurrently by many threads in a multi-threaded server, with no corruption of per-key state and no allowing more requests than intended.
Very high QPS. Handle very large request volume (e.g., millions of checks/sec) with low latency. Discuss the performance bottlenecks and the scaling strategies you would use.
Algorithm choice. State which rate-limiting algorithm you would choose (e.g., token bucket, leaky bucket, fixed window, sliding window), what guarantees it provides (burstiness, fairness, accuracy), and the trade-offs.
State and data structures. Describe the per-key state and the data structures you would store.
Rejection behavior. Explain what happens when a request is rejected (e.g., HTTP 429, Retry-After , queue/delay).
Distributed scaling. Explain how you would scale the solution when the service is distributed across many instances and traffic for a given key can land on any of them.

Be explicit about what you would clarify with the interviewer: limiting scope (per-user / per-IP / per-route / global), whether bursts are allowed or the rate is strict, single-node vs. distributed, exact vs. approximate consistency, and the preferred failure mode.

Quick Overview

Question

You are building a backend service that must enforce API rate limits. Design and (at a high level) implement a rate limiter, then work through the following:

Per-identity limit. Enforce a limit per identity (e.g., per user ID, per IP, or per API key), such as N requests per second .
Thread safety. The limiter must be safe when accessed concurrently by many threads in a multi-threaded server, with no corruption of per-key state and no allowing more requests than intended.
Very high QPS. Handle very large request volume (e.g., millions of checks/sec) with low latency. Discuss the performance bottlenecks and the scaling strategies you would use.
Algorithm choice. State which rate-limiting algorithm you would choose (e.g., token bucket, leaky bucket, fixed window, sliding window), what guarantees it provides (burstiness, fairness, accuracy), and the trade-offs.
State and data structures. Describe the per-key state and the data structures you would store.
Rejection behavior. Explain what happens when a request is rejected (e.g., HTTP 429, Retry-After , queue/delay).
Distributed scaling. Explain how you would scale the solution when the service is distributed across many instances and traffic for a given key can land on any of them.

Design a thread-safe high-QPS rate limiter

Quick Overview

Design a thread-safe high-QPS rate limiter

Question

Submit Your Answer to Earn 20XP

Design a thread-safe high-QPS rate limiter

Quick Overview

Design a thread-safe high-QPS rate limiter

Question

Submit Your Answer to Earn 20XP