Box·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

Box system design round for a software engineering role. The whole thing was one long question about rate limiting, which sounds narrow but they kept pulling threads until there was a lot to cover.

Questions Asked (1)

Implement a leaky-bucket rate limiter that enforces a maximum average request rate with a fixed drain rate. Write unit tests covering steady-state behavior, burst handling, and boundary conditions. Then discuss concurrency safety and how you'd extend this to a distributed setting with shared state, covering consistency and failure modes.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This started manageable and then just kept expanding.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (average rate, burst capacity, drain rate) and then implement a simple leaky-bucket algorithm using a counter and timestamp. Write unit tests that cover steady-state, burst, and boundary conditions, and then discuss concurrency safety using locks or atomic operations. Finally, extend to a distributed setting by using a shared store like Redis with atomic operations, and discuss consistency trade-offs and failure modes.

Pro tip: Emphasize the difference between average rate and burst capacity, and how the leaky bucket enforces a fixed drain rate. In distributed settings, highlight the importance of idempotency and handling partial failures to avoid over-limiting or under-limiting.

1. Clarify requirements and define the algorithm

Ask about expected request rate, burst size, and drain rate. Define the leaky bucket as a counter that increments on each request and decrements at a fixed rate, rejecting requests when the counter exceeds capacity.

2. Implement the rate limiter

Write a class with methods to allow or deny requests. Use a timestamp to compute leaked tokens since the last request, and update the counter accordingly. Ensure the implementation is efficient and correct.

3. Write unit tests

Create tests for steady-state (requests at exactly the drain rate), burst handling (sudden spike within capacity), and boundary conditions (exactly at capacity, just over capacity, and after idle periods).

4. Discuss concurrency safety

Explain how to make the limiter thread-safe using locks, atomic operations, or synchronized methods. Discuss trade-offs between coarse-grained and fine-grained locking.

5. Extend to distributed setting

Describe using a shared store like Redis with Lua scripts for atomicity. Discuss consistency models (e.g., eventual vs. strong), failure modes (e.g., network partitions, store unavailability), and mitigation strategies like local fallbacks or rate limiting at multiple layers.

Key Points to Mention

Leaky bucket vs. token bucket: leaky bucket enforces a fixed drain rate, while token bucket allows bursts up to a capacity.
Time-based leak calculation: use timestamps to compute how many tokens have leaked since the last request, avoiding a background thread.
Thread safety: use locks or atomic compare-and-swap to prevent race conditions in concurrent environments.
Distributed rate limiting: use a centralized store (e.g., Redis) with atomic operations to maintain a global counter.
Consistency trade-offs: strong consistency ensures accurate limiting but may increase latency; eventual consistency may allow temporary over-limiting.
Failure modes: handle store unavailability with fallback strategies (e.g., local rate limiting, fail-open vs. fail-closed).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.