This is the kind of question where you think you know ad tech until you're actually drawing it out.
Start by clarifying requirements and scale (QPS, latency SLA, auction types), then sketch a high-level architecture with clear separation of concerns (bidding, auction, data). Dive into critical components like low-latency bidding, real-time auction logic, and horizontal scaling, while discussing trade-offs between consistency, latency, and cost.
Pro tip: Emphasize the importance of latency budgets and how you'd measure and monitor them end-to-end; mention that in ad tech, every millisecond impacts revenue, so you'd design for predictable tail latencies, not just averages.
Ask about expected QPS, latency SLA (e.g., <100ms), auction types (first-price, second-price), and budget constraints. Establish functional and non-functional requirements.
Outline main components: ad server, bidding service, auction engine, data stores (user profiles, campaign budgets), and analytics. Describe data flow from ad request to winning ad.
Focus on low-latency bidding (e.g., in-memory caches, edge computing), real-time auction logic (e.g., second-price auction), and budget pacing. Discuss how to handle high throughput with sharding and load balancing.
Discuss trade-offs: consistency vs. latency (e.g., eventual consistency for budgets), cost vs. performance, and how to handle failures (e.g., fallback ads). Identify potential bottlenecks like hot partitions.
Explain horizontal scaling strategies (e.g., stateless services, partitioning), and how to monitor latency, throughput, and error rates. Mention A/B testing and gradual rollouts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the real-time auction protocol, such as latency targets, bidder count, and consistency needs. Then propose a design that handles timeouts and partial bids using asynchronous collection with a deadline, and discuss trade-offs between consistency, availability, and latency. Finally, explain how you would handle edge cases like late bids and ensure fairness.
Pro tip: Emphasize the importance of idempotency and monotonicity in bid handling to avoid duplicate or out-of-order bids, and mention that you would use a centralized auctioneer with a monotonic clock to enforce deadlines consistently.
Ask about expected bidder count, latency requirements, and whether partial bids are acceptable or should be discarded. Understand the consistency model (e.g., strong vs eventual) and failure scenarios.
Propose a deadline-based approach where the auctioneer sets a fixed timeout for bid collection. Use a monotonic clock to avoid clock skew issues and consider a grace period for network delays.
Decide on a policy: either wait for all bids (with timeout) or proceed with received bids after timeout. Discuss how to handle late bids (e.g., reject or accept if within grace period) and ensure fairness.
Implement idempotent bid processing, sequence numbers, and deduplication. Use a distributed architecture with a central auctioneer or a consensus protocol if needed, and consider partitioning by auction ID.
Compare consistency vs availability (e.g., CAP theorem), latency vs completeness, and complexity vs reliability. Explain how your design balances these based on the use case.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scale, then propose a normalized relational schema for core entities (campaigns, budgets, targeting filters) with appropriate relationships and constraints. Discuss how the model supports query patterns, and mention trade-offs and potential optimizations for scale.
Pro tip: Show awareness of Roku's ad-serving scale by discussing how you'd handle high-volume reads and writes, such as denormalization or caching, and emphasize data integrity for budget enforcement.
Ask about expected scale, query patterns, and consistency needs to tailor the data model. Confirm whether budgets are per campaign or per channel, and how targeting filters are applied.
Define the main entities: Campaign, Budget, TargetingFilter, and their attributes. Consider relationships like one-to-many between campaign and budgets, and many-to-many between campaigns and targeting filters.
Propose tables with primary/foreign keys, indexes, and constraints. For example, a campaigns table, a budgets table with a foreign key to campaigns, and a targeting_filters table with a join table for campaign-targeting associations.
Explain how the model supports common queries, such as fetching active campaigns with remaining budget and targeting criteria. Suggest indexes on frequently filtered columns like status, date ranges, and targeting attributes.
Mention normalization vs. denormalization, and how you might scale reads/writes (e.g., sharding by campaign ID, caching). Highlight any consistency requirements for budget updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part I was least prepared for.
Start by clarifying the scale and QPS requirements, then propose a distributed architecture that separates real-time decisioning from asynchronous budget and frequency state updates. Focus on trade-offs between accuracy and latency, and discuss how to handle hot keys and consistency.
Pro tip: Emphasize that perfect accuracy is often unnecessary; approximate counting with probabilistic data structures or local caching with periodic sync can meet business needs while scaling. Also, mention the importance of idempotency and graceful degradation during failures.
Ask about QPS, latency SLAs, budget granularity (e.g., daily vs. hourly), and frequency cap window (e.g., per user per day). Understand if strict accuracy is required or if approximate is acceptable.
Propose a distributed system with a real-time bidding service that checks local caches for budget and frequency data, backed by a scalable datastore (e.g., Redis, Cassandra) for global state. Use a message queue for asynchronous updates.
Discuss pacing algorithms (e.g., token bucket, proportional control) and how to distribute budget across time. Use sharded counters with eventual consistency, and handle overspend with reconciliation.
Explain using distributed counters (e.g., Redis with TTL) or probabilistic structures (e.g., Bloom filters, count-min sketch) for per-user frequency. Consider user-level sharding and local caching to reduce latency.
Discuss consistency vs. availability, latency vs. accuracy, and how to handle failures (e.g., fallback to local estimates, circuit breakers). Mention monitoring and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard streaming pipeline answer.
Start by outlining the end-to-end flow from client event capture to downstream analytics, emphasizing reliability and scalability. Then, dive into each stage, highlighting design choices and trade-offs specific to Roku's streaming platform. Finally, connect the pipeline to analytics use cases like attribution and personalization.
Pro tip: Emphasize idempotency and exactly-once processing to avoid double-counting events, a common pitfall in ad analytics. Also, mention how you'd handle late-arriving events and data quality checks to ensure accurate attribution.
Describe how events are generated on devices (e.g., Roku OS) with unique identifiers and timestamps, and buffered locally to handle offline scenarios.
Explain the ingestion layer (e.g., HTTP endpoints, Kafka) that receives events, validates them, and routes to processing pipelines, ensuring low latency and high throughput.
Detail stream processing (e.g., Flink, Spark Streaming) for sessionization, deduplication, and enrichment with metadata (e.g., ad campaign info) before writing to storage.
Describe storage solutions (e.g., data lake, OLAP) and batch aggregation jobs that pre-compute metrics for analytics and reporting.
Explain how data feeds into analytics tools (e.g., BI dashboards, attribution models) and machine learning pipelines for personalization and ad targeting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with an in-memory cache for campaign and targeting data, refreshed asynchronously from a backing store.
Start by clarifying the auction's access patterns and latency requirements, then propose a layered storage and caching architecture that balances speed, consistency, and cost. Walk through specific technologies (e.g., Redis, DynamoDB, CDN) and justify each choice with trade-offs relevant to Roku's scale and real-time bidding needs.
Pro tip: Emphasize that caching is not just about speed but also about reducing load on primary storage and handling hot keys gracefully—mention techniques like consistent hashing and cache warming to show operational maturity.
Ask about read/write ratio, data size, latency SLA, consistency needs, and query patterns (e.g., by item ID, user, or geolocation). This ensures your design targets the actual problem.
Select a database that can handle high write throughput and scale horizontally, such as DynamoDB or Cassandra, and explain how it meets the auction's persistence needs.
Propose in-memory caches (e.g., Redis) for hot data, a CDN for static assets, and possibly a local cache on application servers. Discuss cache eviction policies and TTLs.
Explain how you'll keep caches coherent (e.g., write-through, write-behind, or pub/sub invalidation) and handle race conditions during bids.
Compare your choices against alternatives (e.g., SQL vs NoSQL, Redis vs Memcached) and highlight how you'd monitor and adapt the system over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.