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Netflix·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
May 2026

Summary

Netflix system design round for a software engineer role. The main problem was building a key-value cache with per-key TTL, and the conversation went pretty deep into eviction, concurrency, and scaling. Left feeling okay about it but not great.

Questions Asked (1)

Q1

Design a key-value cache that supports setting a value with a per-key TTL, retrieving a value only if it hasn't expired, and deleting a key. Walk through your eviction strategy, thread-safety considerations, and how this scales.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

Started with the basics: a hashmap storing value plus expiry timestamp, check on get and return null if stale.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., expected scale, consistency needs, TTL precision) and then design a basic in-memory key-value store with per-key TTL using a hash map and a min-heap or timing wheel for expiration. Discuss eviction strategies like LRU or LFU, thread-safety via sharding and locks or lock-free structures, and scaling through consistent hashing and replication. Conclude by addressing trade-offs and potential bottlenecks.

Pro tip: Emphasize that TTL expiration should be lazy (on access) combined with active background sweeping to balance accuracy and performance, and mention that Netflix often deals with high-throughput, low-latency systems, so consider using off-heap memory or a distributed cache like EVCache.

1. Clarify Requirements and Constraints

Ask about expected throughput, latency, data size, TTL precision, consistency, and persistence needs to tailor the design.

2. Design Core Data Structures

Propose a hash map for key-value storage and a separate structure (e.g., min-heap, timing wheel, or sorted set) to track expiration times efficiently.

3. Define Eviction and Expiration Policies

Explain how to handle expired keys (lazy deletion on access plus periodic sweeping) and eviction when capacity is reached (e.g., LRU, LFU, or TTL-based).

4. Address Thread-Safety and Concurrency

Discuss locking strategies (e.g., fine-grained locks, read-write locks), sharding to reduce contention, or lock-free approaches using atomic operations.

5. Scale the System

Describe horizontal scaling via sharding (consistent hashing), replication for fault tolerance, and potential use of a distributed cache layer.

Key Points to Mention

  • Lazy vs. active expiration and their trade-offs
  • Eviction policies: LRU, LFU, TTL-based, and their implementations
  • Thread-safety mechanisms: sharding, locks, lock-free data structures
  • Scaling strategies: consistent hashing, replication, partitioning
  • Performance considerations: memory overhead, latency, throughput
  • Real-world examples: Redis, Memcached, Netflix's EVCache

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