I started with the counter store and storage design, which felt natural, but the interviewer kept pulling me toward identity resolution before I was ready.
Start by clarifying requirements and scale, then design a high-level architecture that separates the counting mechanism from the policy engine. Focus on data modeling for fast increments and reads, and discuss trade-offs between accuracy, latency, and cost.
Pro tip: Emphasize that frequency capping is a real-time, low-latency decision problem; propose an in-memory store like Redis with atomic increments and TTLs, but also discuss how to handle failures and eventual consistency for billing or analytics.
Ask about the number of users, campaigns, creatives, and the expected QPS for ad requests. Clarify the granularity of caps (per user, per campaign, per creative) and the time windows (daily, weekly).
Outline components: an ad decision service that checks frequency caps, a counting service that tracks impressions, and a policy service that manages cap configurations. Consider using a fast in-memory store for counters and a durable store for configurations.
Choose a data model for counters, e.g., key = user_id + campaign_id + time_window, value = count. Use Redis with atomic INCR and EXPIRE for TTL. For cross-advertiser caps, consider aggregating at the advertiser level.
Discuss sharding by user_id, replication for availability, and trade-offs between strong consistency (e.g., using Redis transactions) and eventual consistency (e.g., using a distributed counter with CRDTs). Address hot keys and failure modes.
Compare synchronous vs asynchronous counting, discuss caching strategies, and consider approximate counting (e.g., Bloom filters) for high-scale scenarios. Mention monitoring and alerting for cap violations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly a question I should have anticipated but didn't prep for explicitly.
Start by contrasting the core purpose: a generic rate limiter enforces a simple request-per-time-window limit, while a frequency capping service enforces per-user, per-advertiser, per-campaign constraints with complex business rules. Then, detail the additional components needed for advertiser configuration and onboarding, such as a flexible policy engine, a self-service portal, and a metadata store for advertiser-specific settings.
Pro tip: Emphasize that frequency capping is a business-critical feature that directly impacts advertiser satisfaction and revenue, so the design must prioritize correctness, auditability, and seamless onboarding to avoid manual errors.
Explain that a generic rate limiter is a technical safeguard against abuse, while a frequency capping service is a business logic layer that enforces contractual ad delivery limits per user, per advertiser, and per campaign.
List the unique requirements: per-advertiser and per-campaign caps, time windows (e.g., daily, weekly), user-level tracking, and real-time enforcement with low latency.
Detail the extra components: a policy engine to evaluate complex rules, a configuration store for advertiser-specific settings, a self-service portal for onboarding, and an audit log for compliance.
Walk through how these components interact: advertisers configure caps via the portal, settings are stored and versioned, and the policy engine enforces caps in real-time using a fast counter store (e.g., Redis).
Discuss trade-offs like consistency vs. latency, and how to scale for millions of users and advertisers, possibly using sharding or approximate counting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and latency requirements, then propose a distributed, in-memory counting system with local caching and asynchronous updates to a central store. Emphasize trade-offs between accuracy and latency, and discuss how to handle hot keys and consistency.
Pro tip: Mention that you would use a probabilistic data structure like a count-min sketch for approximate counting to reduce memory and latency, and combine it with a local cache for exact counts of frequent users. This shows you can balance precision with performance at scale.
Ask about QPS, latency SLA, accuracy requirements, and whether the cap is global or per-user. This ensures you design for the right constraints.
Propose a distributed system with local in-memory counters at each node, backed by a centralized store like Redis or a custom service. Use consistent hashing to partition users across nodes.
Suggest using count-min sketch or HyperLogLog for approximate counting to save memory, and exact counters for users near the cap. Discuss time-window management (e.g., sliding window with buckets).
Explain how to handle updates: asynchronous replication to central store, with eventual consistency. For strict caps, use a two-phase approach or lease-based tokens to avoid overshooting.
Mention techniques like batching, pipelining, local caching, and avoiding synchronous network calls. Discuss monitoring and fallback strategies for when the central store is unavailable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about a config service with versioned schemas, validation at write time, and async propagation to edge nodes.
Start by clarifying requirements: who sets caps (advertisers, internal teams), at what granularity (campaign, creative, user), and what consistency guarantees are needed. Then propose a RESTful API with versioning, strict validation, and an event-driven propagation pipeline that balances consistency and availability. Discuss trade-offs like synchronous vs asynchronous updates, caching, and idempotency.
Pro tip: Emphasize idempotency and conflict resolution: advertisers may retry updates, so design PUT/PATCH semantics with versioning (ETags) to prevent lost updates. Also, consider a staged rollout with canary validation to catch propagation issues before global impact.
Ask about scale (number of advertisers, update frequency), consistency needs (strong vs eventual), and failure modes (what happens if propagation fails). Identify stakeholders and downstream systems.
Define resource-oriented endpoints (e.g., /advertisers/{id}/frequency-caps), HTTP methods, request/response schemas, and versioning. Include validation rules (e.g., cap must be positive integer, time window valid).
Implement layered validation: syntactic (schema), semantic (business rules), and authorization (who can set caps). Use a policy engine or RBAC to enforce permissions.
Choose between synchronous (write-through to all services) and asynchronous (event-driven with a message queue). Discuss trade-offs: latency vs consistency, and how to handle failures (retries, dead-letter queues).
Define consistency model (e.g., eventual with read-your-writes). Implement monitoring, alerting, and audit logs. Plan for rollback and canary deployments to mitigate risks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the purpose of frequency capping (e.g., to limit ad or content exposure per user) and the metrics needed to monitor its effectiveness. Then discuss how privacy regulations (like GDPR, CCPA) and Netflix's internal policies shape data collection, storage, and access, emphasizing anonymization and aggregation. Finally, propose a design that balances reporting needs with compliance, such as using aggregated data and privacy-preserving techniques.
Pro tip: Demonstrate awareness that frequency capping data can be a privacy risk if tied to individuals; propose using differential privacy or k-anonymity to aggregate data while still providing actionable insights. Also, mention that Netflix often uses pseudonymized IDs and short retention windows to comply with global regulations.
Ask clarifying questions to understand what frequency capping is for (e.g., ads, content recommendations) and what decisions the reporting data will inform. This ensures you focus on relevant metrics.
List essential metrics such as cap hit rate, distribution of impressions per user, time between exposures, and effectiveness (e.g., engagement lift). Consider both real-time and batch reporting needs.
Identify applicable regulations (GDPR, CCPA, COPPA) and Netflix's privacy policies. Determine what data can be collected, how it can be stored, and who can access it, focusing on user consent and data minimization.
Propose techniques like aggregation, anonymization, pseudonymization, and differential privacy to expose data without compromising individual privacy. Ensure data retention limits and access controls are in place.
Suggest A/B testing or simulation to validate that the reporting data meets business needs without violating privacy. Plan for regular audits and updates as regulations evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said you can fail open (serve the ad anyway and risk overcapping) or fail closed (block the ad and risk underspend).
Start by clarifying the requirements and constraints of the frequency capping service, then discuss failure modes of the counter store and propose a layered mitigation strategy. Emphasize trade-offs between consistency, availability, and user experience, and how you would monitor and recover from failures.
Pro tip: Netflix values graceful degradation and resilience; mention how you would design the system to fail open or closed based on business impact, and how you'd use chaos engineering to validate failure handling.
Ask about the expected scale, latency requirements, and business impact of frequency capping (e.g., ad delivery, content recommendations). Understand what happens if capping fails: is it better to over-serve or under-serve?
Enumerate potential failures: counter store unavailability, network partitions, latency spikes, data inconsistency. Consider both transient and prolonged outages.
Propose solutions like local caching with TTL, fallback to approximate counts, circuit breakers, and asynchronous writes. Discuss trade-offs between consistency and availability (e.g., CAP theorem).
Decide whether to fail open (allow requests) or fail closed (block requests) based on business rules. Implement dynamic configuration to adjust behavior without redeployment.
Set up monitoring for counter store health, error rates, and fallback usage. Use alerts and automated recovery (e.g., retries with backoff, failover to secondary store). Conduct post-mortems and chaos tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.