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

Senior
Apr 2026Remote

Summary

Netflix virtual onsite for a software engineering role. One system design question, focused on rate limiting and frequency capping. Pretty niche problem if you haven't thought about ad delivery or notification systems before.

Questions Asked (1)

Q1

Design a frequency cap system that limits how often a user is shown a particular piece of content or notification within a given time window.

System DesignTechnical Trade-offsData Modeling
Author's notes

This one requires you to think about scale pretty fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: what content types, time windows, and scale (users, events per second). Then propose a high-level design using a distributed counter store like Redis with TTL, and discuss trade-offs between accuracy, latency, and cost. Finally, dive into data modeling, sharding, and failure handling.

Pro tip: Emphasize idempotency and atomicity in counter updates to avoid over- or under-counting, and discuss how to handle hot keys (e.g., a viral notification) via sharding or local aggregation.

1. Clarify Requirements

Ask about scale (DAU, QPS), content types (e.g., notifications, recommendations), time windows (e.g., 1 per day), and consistency needs (strict vs. eventual).

2. High-Level Design

Propose a service that checks and increments counters in a fast data store (e.g., Redis) with TTL, and outline the flow: receive request, check counter, allow/deny, increment.

3. Data Modeling & Storage

Design keys (e.g., user_id:content_id:window) and choose data structures (e.g., Redis sorted sets for sliding windows, or simple counters for fixed windows). Discuss TTL and eviction.

4. Scalability & Reliability

Address sharding, replication, and hot keys. Discuss atomic operations (Lua scripts, transactions) and fallback strategies if the store is unavailable.

5. Trade-offs & Extensions

Compare fixed vs. sliding windows, accuracy vs. performance, and centralized vs. edge enforcement. Mention monitoring and analytics.

Key Points to Mention

  • Choice of time window: fixed (e.g., daily) vs. sliding (e.g., last 24 hours) and implications for storage and accuracy.
  • Use of Redis with TTL for automatic expiration and atomic INCR operations.
  • Sharding strategies to distribute load and avoid hot keys (e.g., consistent hashing, local counters with periodic sync).
  • Handling failures: idempotency, retries, and graceful degradation (e.g., allow if store is down).
  • Data modeling: key design, use of sorted sets for sliding windows, and memory considerations.
  • Trade-offs: consistency vs. availability, latency vs. accuracy, and cost of storage.

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