Netflix·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Phone screen for a Netflix SWE role, timed coding problem around cache design. The interviewer clearly swapped some keywords from a canned prompt, which threw me off at first. Pretty focused on implementation details and tradeoffs around cleanup strategy.

Questions Asked (1)

Design a cache where each item has a fixed TTL. Start with the simplest possible implementation, then extend it to support cache cleanup. The get() method is on the hot path, so avoid doing cleanup there. Assume a separate background process handles eviction.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

The prompt felt slightly off when I read it, like a word or two had been swapped from some other version of the question.

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AI HintsAI Generated

Suggested Approach

Start by outlining the simplest cache implementation using a hash map with TTL stored per entry, and a get() that checks expiry. Then extend it by adding a background cleanup process that periodically scans and removes expired items, ensuring get() remains O(1) and free of cleanup logic. Emphasize trade-offs between different cleanup strategies and how they affect performance and memory.

Pro tip: Mention that you would use a min-heap or timing wheel for efficient expiration tracking, but only if the scale justifies it—otherwise a simple periodic scan is sufficient. This shows you balance simplicity with scalability.

1. Clarify requirements and constraints

Ask about expected cache size, TTL uniformity, read/write ratio, and latency requirements. Confirm that get() must be O(1) and that cleanup is handled by a separate process.

2. Design the simplest implementation

Propose a hash map where each entry stores the value and an expiration timestamp. get() checks if the current time exceeds the timestamp; if so, treat as miss (but do not delete).

3. Extend for background cleanup

Introduce a background process that periodically scans the cache and removes expired entries. Discuss how to avoid locking the entire cache during cleanup, e.g., using a read-write lock or sharding.

4. Discuss trade-offs and optimizations

Compare periodic full scan vs. priority queue (min-heap) vs. timing wheel. Consider memory overhead, CPU cost, and complexity. Mention that lazy deletion in get() can be a fallback but should be avoided on hot path.

5. Address concurrency and edge cases

Explain how to handle concurrent reads and writes during cleanup, and what happens if an item expires between get() and cleanup. Suggest using atomic operations or versioning.

Key Points to Mention

TTL stored as absolute expiration timestamp (e.g., Unix time) to avoid recomputation.
get() should not perform cleanup; it only checks expiry and returns value or miss.
Background cleanup can be a separate thread/process that periodically removes expired items.
Trade-offs: periodic scan is simple but O(n); min-heap gives O(log n) insert and O(1) peek but requires synchronization.
Concurrency: use read-write locks or lock-free data structures to minimize contention on hot path.
Memory management: consider eviction policies (e.g., LRU) in addition to TTL if cache size is bounded.

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