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Roblox·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Roblox SWE interview with a system design coding question focused on rate limiting and bot detection. Pretty meaty for a single problem, they wanted you to think through the whole thing from data structures to memory management.

Questions Asked (1)

Q1

Design and implement an IP-based bot detection system. It should track how many requests each IP makes within a sliding time window, flag IPs that exceed a threshold, and periodically clean up stale data to keep memory bounded. Implement record(ip, timestamp) and isBot(ip, timestamp).

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I went straight for a per-IP deque of timestamps, which felt right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., window size, threshold, memory limits, distributed vs. single-node). Then propose a sliding window algorithm using a deque or circular buffer per IP, and discuss cleanup strategies like periodic pruning or lazy deletion. Finally, analyze time/space complexity and trade-offs, and consider extensions for scale.

Pro tip: Mention that you would use a hash map from IP to a deque of timestamps, and for cleanup, either periodically remove stale IPs or use a time-bucketed approach to avoid O(n) scans. Also, discuss how to handle out-of-order timestamps and the importance of monotonic clocks.

1. Clarify requirements and constraints

Ask about window size, threshold, expected number of IPs, memory limits, and whether the system is distributed. This shows you think before coding.

2. Design data structures

Propose a hash map from IP to a deque (or circular buffer) of timestamps within the window. Explain how record and isBot operations work.

3. Implement sliding window logic

For record, append timestamp and evict old ones. For isBot, count timestamps in window and compare to threshold. Discuss lazy vs. eager eviction.

4. Address cleanup and memory bounds

Describe periodic cleanup of stale IPs (e.g., using a background thread or time-bucketed map) to prevent memory leaks. Discuss trade-offs between cleanup frequency and overhead.

5. Analyze complexity and trade-offs

State time complexity (O(1) amortized for record, O(k) for isBot where k is window size) and space complexity. Discuss alternatives like fixed windows or token buckets.

Key Points to Mention

  • Sliding window vs. fixed window vs. token bucket algorithms and their trade-offs
  • Using a deque or circular buffer for efficient timestamp storage and eviction
  • Lazy deletion vs. periodic cleanup for memory management
  • Handling out-of-order timestamps and clock skew
  • Scalability considerations: sharding by IP, distributed cache, or approximate counting
  • Thread safety and concurrency if the system is multi-threaded

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