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Netflix·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Phone screen for a software engineering role at Netflix. One coding problem, pretty focused, no fluff.

Questions Asked (1)

Q1

Implement a print cache that suppresses duplicate print calls: if a value has already been printed within the past 10 seconds, skip it.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

Seemed simple at first and I almost said 'just use a hashmap' before catching myself.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., single-threaded vs concurrent, memory constraints) and then propose a hash map with timestamped entries, using lazy eviction or a background thread to remove stale entries. Discuss trade-offs between time-based eviction strategies and data structures, and consider edge cases like clock skew and high throughput.

Pro tip: Mention that you would use a monotonic clock (e.g., System.nanoTime()) instead of wall-clock time to avoid issues with system time changes, and discuss how to handle concurrency with minimal locking (e.g., ConcurrentHashMap with compute()).

1. Clarify Requirements

Ask about expected throughput, concurrency, memory limits, and whether the cache should be distributed. Confirm that 'within the past 10 seconds' means a sliding window from the last print.

2. Choose Data Structures

Propose a hash map (or concurrent map) storing the last print timestamp for each value. For eviction, consider a min-heap or a time-ordered queue to efficiently remove expired entries.

3. Design Eviction Strategy

Decide between lazy eviction (check on access) and active eviction (background thread). Discuss trade-offs: lazy is simpler but may retain stale entries; active keeps memory bounded but adds complexity.

4. Handle Concurrency

If multi-threaded, use thread-safe structures and atomic operations. Consider lock striping or read-write locks to minimize contention. Ensure that the check-and-update is atomic.

5. Analyze Trade-offs and Edge Cases

Discuss time complexity (O(1) average for check/update), memory usage, and potential issues like clock skew, high cardinality, and bursty traffic. Suggest monitoring and tuning parameters.

Key Points to Mention

  • Use of monotonic clock to avoid system time adjustments
  • Choice of data structure: hash map for O(1) lookups, plus a priority queue or timing wheel for efficient eviction
  • Concurrency considerations: thread-safe map, atomic compute, and avoiding race conditions
  • Eviction strategies: lazy vs. active, and their impact on memory and CPU
  • Trade-offs between precision and performance (e.g., exact 10 seconds vs. approximate)
  • Scalability: how the design would change for distributed systems (e.g., Redis with TTL)

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