Netflix·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePass

May 2026

Summary

Phone screen for a Netflix SWE role, just one coding round where they had me build a cache with a time-based expiry. Passed through to onsite two days later.

Questions Asked (1)

Design and implement a cache that supports time-based expiration of entries.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

Classic enough that I'd seen variations before, but the time-limit piece added a wrinkle I didn't think through fast enough at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what does 'time-based expiration' mean (TTL, absolute expiry, sliding window)? Then outline a design using a hash map for storage and a priority queue or min-heap for efficient expiration, discussing trade-offs between lazy and active expiration. Finally, walk through implementation details, including thread safety and eviction policies, and analyze time/space complexity.

Pro tip: Mention that Netflix often deals with high-throughput, low-latency systems, so you should discuss how your cache handles concurrency and avoids blocking reads/writes, perhaps using a lock-free or read-write lock approach.

1. Clarify Requirements

Ask questions to understand the expected scale, expiration semantics (TTL vs. absolute), eviction policy when full, and concurrency needs. This shows you think before coding.

2. Choose Data Structures

Propose a hash map for O(1) key lookup and a min-heap or time-ordered queue for expiration. Discuss alternatives like timing wheels for high-resolution timers.

3. Design Expiration Strategy

Decide between lazy expiration (check on access) and active expiration (background thread). Explain trade-offs: lazy is simpler but may hold expired items; active is more complex but frees memory promptly.

4. Implement Core Operations

Write pseudocode for get, put, and delete, ensuring expiration is checked. For active expiration, describe a background thread that periodically removes expired entries.

5. Address Concurrency and Edge Cases

Discuss thread safety (locks, concurrent data structures) and edge cases like clock skew, memory limits, and eviction when cache is full. Analyze time/space complexity.

Key Points to Mention

Time complexity: O(1) for get/put with hash map, O(log n) for heap operations if using min-heap.
Space complexity: O(n) for storing entries plus overhead for expiration tracking.
Trade-offs between lazy and active expiration: memory usage vs. CPU overhead.
Concurrency: use read-write locks or concurrent hash map to allow parallel reads.
Eviction policies: LRU, LFU, or TTL-based eviction when cache is full.
Handling clock skew and using monotonic time for expiration.

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