← Warner Bros Discovery Interview Insights
Start by clarifying requirements: scale, latency, ad types, and experimentation needs. Then design a high-level architecture with separate services for ad selection, experimentation, and tracking, and dive into key components like targeting, ranking, and A/B test assignment. Finally, discuss trade-offs around consistency, latency, and data collection.
Pro tip: Emphasize that experimentation should be baked into the system from the start, not bolted on—this shows you understand that A/B testing is a first-class concern in ad delivery. Also, mention the importance of a consistent user experience by ensuring the same user sees the same experiment variant across sessions.
Ask questions to understand scale (QPS, users), latency requirements, ad types (display, video), targeting criteria, and experimentation goals (metrics, variants). This ensures the design meets actual needs.
Outline main components: ad selection service, experimentation service, user profile/targeting service, ad inventory, and tracking/analytics. Describe data flow from ad request to ad serving and event logging.
Explain how ads are chosen: candidate retrieval based on targeting, ranking using models (e.g., CTR prediction), and business rules (budget, frequency capping). Discuss real-time vs. batch processing.
Detail how A/B tests are assigned (e.g., user bucketing via hashing), how variants are configured, and how the system ensures consistent assignment. Cover metrics collection and analysis.
Discuss trade-offs: latency vs. personalization, consistency vs. flexibility in experiments, and data storage choices. Address scaling with caching, sharding, and async logging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked about pre-filtering with indexed lookups and keeping a hot cache of campaign eligibility rules.
Start by clarifying the scale and latency requirements, then propose a multi-layered architecture that separates targeting rule evaluation from ad/content serving. Focus on pre-computing user segments and using in-memory data structures for fast lookups, while discussing trade-offs between consistency and throughput.
Pro tip: Mention the importance of monitoring and gradual rollout (e.g., canary deployments) when changing targeting logic, as it minimizes risk in high-traffic systems. Also, highlight that caching user segments with appropriate TTLs can drastically reduce database load.
Ask about expected QPS, latency SLA, data freshness, and the complexity of targeting rules. This ensures your solution aligns with business needs.
Propose a schema for user attributes, segments, and targeting rules. Consider using a columnar store or in-memory database for fast reads.
Pre-calculate user segments and targeting decisions offline or near-line, then cache them in a low-latency store like Redis or a local cache.
At serving time, fetch pre-computed segments and apply any dynamic rules using efficient data structures (e.g., bitsets, bloom filters).
Use horizontal scaling, sharding, and asynchronous updates. Implement monitoring and fallbacks to handle failures gracefully.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a write-to-queue approach, consumers fan out to a real-time counter store for pacing and a batch pipeline for reporting.
Start by outlining the end-to-end pipeline from client-side event capture to storage and serving, then explicitly categorize each stage as real-time or eventually consistent based on business needs. Emphasize trade-offs between latency, cost, and accuracy, and how you'd validate the system with monitoring and reconciliation.
Pro tip: Anchor your answer in concrete business use cases: real-time for ad pacing and fraud detection, eventual consistency for billing and analytics. This shows you understand that technical decisions must be driven by product requirements, not just engineering preferences.
Ask about expected event volume, latency SLAs, and key consumers (e.g., ad server, dashboards, billing). This ensures your design targets the right priorities.
Describe client-side SDKs, edge collection (e.g., API gateway), and a durable message queue (e.g., Kafka) to buffer and decouple producers from consumers.
Explain stream processing (e.g., Flink, Spark Streaming) for low-latency aggregations, anomaly detection, and immediate actions like ad pacing or fraud alerts.
Detail batch processing (e.g., Spark, Hadoop) or micro-batch for accurate, cost-effective aggregation, billing, and historical reporting where slight delays are acceptable.
Cover storage choices (e.g., time-series DB, data lake), serving layers (e.g., cache, API), and mechanisms to reconcile real-time and batch results (e.g., lambda architecture, Kappa).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Hashing user ID plus experiment ID to get a stable bucket assignment, that part I had.
Start by defining a deterministic hashing scheme that maps users to buckets using stable identifiers and experiment salts, ensuring consistent assignment across sessions and services. Then explain how to log exposures idempotently at the moment of first interaction, with safeguards against contamination such as mutual exclusion and layered experiments. Finally, discuss validation and monitoring to detect and correct bucketing or logging issues.
Pro tip: Emphasize that deterministic bucketing must be consistent across all services and clients, so use a shared library or service for hashing and bucket assignment. Also, log exposures only when the user actually sees the variant, not at assignment time, to avoid dilution and contamination.
Choose a stable user identifier (e.g., user ID, device ID) and combine it with a unique experiment salt using a cryptographic hash (e.g., SHA-256). Map the hash to a bucket range (e.g., 0-9999) to assign users to variants deterministically.
Centralize bucketing logic in a shared library or service to guarantee that all services and clients compute the same bucket for a given user and experiment. Use the same hashing algorithm, salt, and bucket ranges everywhere.
Log an exposure event only when the user first encounters the experiment (e.g., sees the variant). Use a unique exposure ID (e.g., user ID + experiment ID) and idempotent writes to avoid duplicate logs.
Implement mutual exclusion groups to prevent users from being in conflicting experiments simultaneously. Use layered experiments (e.g., overlapping domains) with separate salts to avoid interference.
Run A/A tests to verify uniform distribution and consistency. Monitor exposure logs for anomalies (e.g., sudden spikes, missing data) and set up alerts for bucketing inconsistencies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Frequency capping under concurrency is genuinely annoying.
Start by clarifying the requirements and scale, then outline a layered architecture with a real-time control plane for delivery decisions and a data plane for enforcement. Focus on how you achieve atomicity and consistency for frequency capping and deduplication under high concurrency, using distributed counters and idempotent processing.
Pro tip: Emphasize that you would use a combination of Redis Lua scripts for atomic counter updates and Kafka for exactly-once processing to handle deduplication, and mention how you'd monitor and alert on cap violations to ensure correctness.
Ask about expected QPS, latency requirements, and consistency needs to tailor the solution. Understand what 'delivery controls' entail (e.g., frequency capping, deduplication, pacing).
Propose a centralized service that manages delivery rules and caps, using a fast data store like Redis for real-time decisions. Ensure it can scale horizontally and handle high read/write throughput.
Use atomic operations (e.g., Redis INCR with expiry) to track counts per user/campaign. Consider sliding windows or token buckets for precise capping, and discuss trade-offs between accuracy and performance.
Employ idempotency keys and a distributed cache (e.g., Redis) or a database with unique constraints to detect and discard duplicates. For high concurrency, use Kafka with exactly-once semantics or a streaming processor with state.
Discuss techniques like optimistic locking, distributed locks, or CRDTs to maintain consistency. Highlight the CAP theorem trade-offs and choose AP or CP based on business needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through cache stampede on cold start, budget overspend if the pacing counter lags, and experiment assignment skew if your hash function isn't uniform.
Start by clarifying the system's scope and critical user journeys, then systematically walk through failure modes across layers (client, network, service, data, infrastructure) and propose mitigations using established patterns. Emphasize trade-offs and prioritization based on business impact, especially for a media streaming context.
Pro tip: Tie failure modes to Warner Bros Discovery's streaming business: e.g., a CDN outage during a live event can cause massive churn, so design for graceful degradation (lower quality streams) and rapid failover. Show you think about cost vs. resilience trade-offs.
Ask clarifying questions to understand the system's boundaries, scale, and most critical user journeys (e.g., video playback, search, recommendations). This ensures your analysis focuses on what matters most.
Systematically go through each layer: client (device/browser), network (CDN, DNS), service (microservices, APIs), data (databases, caches), and infrastructure (cloud regions, availability zones). For each, list potential failures like timeouts, crashes, data loss, or latency spikes.
Assess each failure mode's potential impact on user experience and business metrics (e.g., playback failures, sign-up errors) and its likelihood. Prioritize the ones that are most critical and plausible.
For each prioritized failure, suggest concrete design patterns: redundancy (multi-AZ, multi-region), graceful degradation (fallback content, lower bitrate), circuit breakers, retries with backoff, bulkheads, and chaos engineering. Explain how these address the failure.
Acknowledge trade-offs (cost, complexity, latency) and explain how you'd monitor and test these failures (observability, SLOs, game days). Show that resilience is an ongoing process.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.