Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Got a system design coding question for a SWE role at OpenAI, specifically around building an in-memory rate limiter with thread safety. The problem was well-scoped but had enough depth to go sideways fast if you picked the wrong algorithm or forgot about cleanup for idle keys.

Questions Asked (1)

Implement a thread-safe in-memory rate limiter supporting both sliding window and token bucket policies, with an allow(key, now_ms) API that works across many keys and handles memory cleanup for idle ones.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The actual coding part wasn't the hard bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a thread-safe data structure that supports both policies with efficient per-key operations. Discuss the trade-offs between sliding window and token bucket, and explain how to handle memory cleanup for idle keys using techniques like lazy eviction or background sweeping.

Pro tip: Mention that you would use a sharded lock or concurrent hash map to reduce contention, and that you'd consider using a time wheel or hierarchical timing wheel for efficient cleanup of idle keys.

1. Clarify Requirements and Constraints

Ask about expected throughput, number of keys, memory limits, and whether strict accuracy is required. Confirm the API semantics and policy parameters.

2. Design Data Structures

Propose a concurrent hash map (e.g., ConcurrentHashMap) for key storage, with each key mapping to a policy-specific state (e.g., a deque of timestamps for sliding window, or token count and last refill time for token bucket).

3. Ensure Thread Safety

Use per-key locks or atomic operations to avoid race conditions. Discuss lock striping or sharding to reduce contention across keys.

4. Implement Memory Cleanup

Describe a strategy to evict idle keys, such as a background thread that periodically scans and removes keys not accessed within a threshold, or using a time-based eviction queue.

5. Analyze Trade-offs and Optimizations

Compare sliding window and token bucket in terms of accuracy, memory, and performance. Discuss potential optimizations like approximate sliding window using counters, or lazy token bucket refill.

Key Points to Mention

Thread safety mechanisms: locks, atomics, concurrent data structures
Sliding window implementation: timestamp deque or circular buffer
Token bucket implementation: token count, refill rate, last refill timestamp
Memory cleanup: lazy eviction, background sweeper, time-based eviction
Trade-offs: accuracy vs. memory, contention vs. simplicity
Scalability: sharding, lock striping, and handling many keys

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