My instinct was to reach for Redis and call it a day, but the interviewer kept poking at the read vs write separation angle and I realized I'd conflated two pretty different workloads.
Start by clarifying requirements: scale, latency, consistency, and configurability. Then design a system that tracks user ad exposures and enforces caps using a fast, scalable data store, discussing trade-offs between accuracy and performance.
Pro tip: Emphasize the importance of low-latency reads for ad serving and consider using a combination of in-memory stores and eventual consistency to balance speed and accuracy. Also, discuss how to handle edge cases like clock skew and user privacy.
Ask about scale (users, ads, QPS), latency requirements, consistency needs, and configurability (time windows, caps per ad/campaign).
Outline components: a counter service to track exposures, a rules engine to evaluate caps, and a fast lookup store (e.g., Redis) for real-time decisions.
Design keys (e.g., user_id:ad_id) and time-windowed counters (e.g., using sorted sets or sliding windows). Discuss TTL and aggregation strategies.
Address partitioning, replication, and trade-offs between strong and eventual consistency. Consider caching and pre-aggregation for performance.
Discuss handling of clock skew, user privacy, failure modes, and how to update caps dynamically without downtime.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about sharding the counter key by adding a suffix and then aggregating reads, which is the obvious move.
Start by clarifying the current architecture and assumptions, then systematically address scaling bottlenecks, focusing on the hot key problem. Propose a multi-layered solution combining sharding, caching, and asynchronous processing, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize that hot keys are inevitable at scale, so design for graceful degradation and automatic mitigation rather than trying to prevent them entirely. Mention Netflix's own tools like EVCache and Hystrix to show familiarity with their stack.
Ask questions to understand the existing system: data model, sharding strategy, read/write patterns, and SLAs. State assumptions about scale (e.g., QPS, data volume) to ground your answer.
Analyze how each component (database, cache, network, application) would fail under 100x load. Highlight that a single hot key can overload a shard, causing latency spikes and cascading failures.
Suggest techniques like key splitting (adding a random suffix), local caching, request coalescing, and moving hot keys to dedicated shards. Discuss trade-offs of each approach.
Outline a revised architecture: multi-level caching, asynchronous writes, dynamic sharding, and auto-scaling. Include monitoring and alerting for hot keys and automatic mitigation triggers.
Summarize trade-offs between consistency, availability, latency, and cost. Explain how you would test the design (load testing, chaos engineering) and iterate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify what 'exact enforcement with zero overshoot' means in the context of the campaign (e.g., ad delivery, resource allocation, rate limiting) and identify the current design's sources of overshoot. Then propose design changes such as adding a strict admission control, using a token bucket with precise refill, or implementing a feedback loop with real-time monitoring, and analyze the trade-offs in terms of latency, throughput, cost, and complexity.
Pro tip: Quantify the cost: e.g., 'This change would increase latency by X ms and require Y additional servers, but ensures zero overshoot.' Also, mention that you would validate with load testing and canary deployments to ensure the strict enforcement holds under peak conditions.
Ask questions to understand what 'exact enforcement' means: is it about ad impressions, budget spend, API rate limits, or something else? What is the current system and where does overshoot occur?
Analyze the existing design to pinpoint why overshoot happens: e.g., distributed counters with eventual consistency, batching, asynchronous processing, or lack of real-time coordination.
Suggest specific modifications: e.g., centralized enforcement point, synchronous checks, token bucket with precise refill, or a two-phase commit protocol. Explain how each ensures zero overshoot.
Discuss the impact on latency, throughput, scalability, availability, and operational complexity. Quantify where possible (e.g., increased latency, need for more resources).
Describe how you would test the new design (load testing, chaos engineering) and monitor it in production to ensure zero overshoot is maintained.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely the question I felt worst about.
Structure your answer around three phases: detection, graceful degradation, and recovery. For detection, describe monitoring lag metrics and alerting; for degradation, explain fallback strategies like serving approximate counts with clear labeling; for recovery, outline a process to recompute accurate counts and backfill data. Emphasize trade-offs and Netflix-scale considerations.
Pro tip: Show maturity by discussing how you'd communicate the issue to stakeholders and prioritize user experience over perfect accuracy during degradation. Mention that you'd add guardrails to prevent recurrence, such as automated lag alerts and circuit breakers.
Monitor pipeline lag using metrics like event-time vs processing-time, watermark delays, and consumer offsets. Set up alerts when lag exceeds thresholds (e.g., >1 hour) and track data freshness SLAs.
Serve approximate or stale counts with clear labeling (e.g., 'updated 2 hours ago'). Implement fallback to last known good state or use a separate low-latency approximate counting system (e.g., HyperLogLog) to maintain availability.
Once the pipeline is restored, reprocess the backlog using idempotent writes and checkpointing. Recompute accurate counts by replaying events from a durable log (e.g., Kafka) and merging with existing state.
Compare recomputed counts against approximate counts and investigate discrepancies. Use checksums or sampling to verify data integrity, and update serving layer atomically to avoid inconsistencies.
Conduct a root cause analysis, add automated scaling, improve monitoring, and implement circuit breakers. Document lessons learned and adjust SLAs to balance accuracy and availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the difference between an ad being 'selected/reserved' and an 'impression' as defined by IAB/MRC standards (i.e., ad must be rendered and at least 50% visible for 1 second). Then propose a client-side rendering confirmation mechanism that only fires an impression event when the ad is actually displayed, with server-side deduplication and reconciliation to handle edge cases.
Pro tip: Emphasize that over-counting is not just a technical bug but a business risk—it inflates metrics, wastes advertiser budget, and can lead to financial penalties. Show you understand the trade-off between accuracy and latency by suggesting a two-phase commit: reserve on server, confirm on client, and reconcile asynchronously.
Clarify what constitutes a valid impression according to industry standards (e.g., IAB/MRC: ad must be rendered and at least 50% visible for 1 second). This sets the ground truth for counting.
Use browser APIs like IntersectionObserver or visibilitychange to detect when the ad is actually in the viewport and rendered. Only then send an impression event to the server.
On the server, track ad reservations and only count impressions that are confirmed by the client. Use a unique impression ID to deduplicate and reconcile late or missing confirmations.
Account for ad blockers, network failures, and page unloads. Use beacon API or sendBeacon for reliable delivery, and implement timeouts to expire unconfirmed reservations.
Set up metrics to compare reserved vs. confirmed impressions, detect anomalies, and continuously refine the logic based on real-world data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.