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

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May 2026

Summary

Amazon system design round for a software engineer role. The whole session was basically one deep question about rate limiting, and they really wanted you to get into the weeds on trade-offs, not just sketch out a solution.

Questions Asked (1)

Design a rate limiter that accepts or rejects incoming requests per user, given a configurable time window and a max request count. Implement the core data structure and an allow_request(user_id, timestamp) API, and walk through the memory implications and the trade-offs between sliding-window and fixed-window approaches.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

I started with a fixed-window counter because it's simple and I could code it fast, but they pushed back almost immediately asking about edge cases at window boundaries.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., per-user limits, window type, distributed vs. single-node) and then propose a data structure like a hash map of deques for sliding window or counters for fixed window. Implement the allow_request API with careful timestamp handling, then analyze memory and trade-offs, emphasizing precision vs. simplicity and scalability.

Pro tip: At Amazon, always tie your design to customer impact and operational excellence—mention how your choice affects latency, cost, and fairness, and propose monitoring for throttling metrics.

1. Clarify Requirements and Constraints

Ask about expected scale (users, QPS), window size, distributed environment, and whether strict accuracy is needed. This shapes your data structure and algorithm choice.

2. Design Core Data Structure and API

Propose a hash map from user_id to a deque of timestamps (sliding window) or a counter with window start (fixed window). Define allow_request(user_id, timestamp) to check and update the structure atomically.

3. Walk Through Algorithm and Edge Cases

Explain how you evict old timestamps or reset counters, handle concurrent requests, and deal with clock skew. Discuss what happens when a user is at the limit.

4. Analyze Memory and Trade-offs

Compare memory: sliding window stores up to max_requests timestamps per user, while fixed window stores a single counter. Discuss precision vs. memory, burst handling, and distributed coordination overhead.

5. Discuss Scalability and Extensions

Mention how to scale (sharding by user_id, using Redis sorted sets), and potential improvements like token bucket or leaky bucket for smoother rate limiting.

Key Points to Mention

Sliding window log provides precise rate limiting but uses O(max_requests) memory per user; fixed window uses O(1) memory but allows bursts at window boundaries.
Use a deque (or Redis sorted set) for sliding window to efficiently evict expired timestamps; ensure O(1) amortized operations.
For distributed systems, consider using a centralized store like Redis with atomic operations (e.g., Lua scripts) to avoid race conditions.
Trade-offs include accuracy vs. memory, simplicity vs. fairness, and latency vs. consistency in distributed settings.
Handle edge cases: timestamp equal to window boundary, clock skew, and concurrent requests from the same user.
Amazon leadership principles: customer obsession (fairness), ownership (monitoring and alerting), and frugality (memory efficiency).

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