Cursor·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Cursor SWE interview with a meaty coding problem around rate limiting. The core task was implementing a sliding window notification gate across three hierarchy levels simultaneously, plus writing and manually tracing through your own test cases.

Questions Asked (1)

Implement a function `should_send_notifications(user_id, timestamp)` that returns true or false based on a hierarchy of rolling-window rate limits: at most 3 per user, 10 per team, and 20 per company, all within a 10-minute window. The user-to-team-to-company mapping is provided. If the notification is allowed, record it and return true; otherwise return false. Also write real test cases and trace through them by hand.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The per-entity sliding window part clicked pretty fast for me, keep a list of timestamps per user/team/company and prune anything older than 10 minutes on each call.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and edge cases, then design a solution using a sliding window with efficient data structures like deques or circular buffers for each user, team, and company. Implement the function with proper locking for concurrency, and write comprehensive test cases covering boundaries, concurrency, and mapping scenarios, tracing through them manually.

Pro tip: Discuss the trade-offs between different sliding window implementations (e.g., timestamp lists vs. counters with buckets) and how to handle distributed rate limiting if the system scales beyond a single node.

1. Clarify Requirements and Edge Cases

Ask about the mapping data structure, concurrency requirements, timestamp format, and whether the window is inclusive/exclusive. Confirm that all three limits must be checked and that recording happens only if all pass.

2. Design Data Structures and Algorithm

Choose a sliding window approach: maintain a queue of timestamps for each user, team, and company. On each call, evict timestamps older than 10 minutes, then check if counts are below limits. If allowed, append the timestamp to all three queues.

3. Implement with Concurrency and Efficiency

Use appropriate locking (e.g., per-user, per-team, per-company locks) to ensure thread safety. Optimize by using circular buffers or deques for O(1) amortized operations, and consider memory usage for large numbers of entities.

4. Write and Trace Test Cases

Create tests for: exactly at limit, just over limit, window expiration, multiple users in same team/company, and concurrent calls. Manually trace through each test to verify correctness, including edge cases like empty mapping or timestamp exactly 10 minutes old.

5. Discuss Trade-offs and Scalability

Explain alternative approaches (e.g., token bucket, fixed windows) and their pros/cons. Discuss how to scale to distributed systems using Redis or similar, and the impact of clock skew and precision.

Key Points to Mention

Sliding window vs. fixed window: sliding window provides more accurate rate limiting but requires storing timestamps.
Data structures: deques or circular buffers for O(1) amortized eviction and insertion.
Concurrency: need thread-safe operations, possibly with fine-grained locking to avoid bottlenecks.
Mapping hierarchy: user->team->company; ensure all three limits are checked atomically.
Test cases: include boundary conditions (exactly 3/10/20), window expiration, and concurrent access.
Scalability: consider distributed rate limiting with Redis sorted sets or similar for multi-node systems.

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