Start by clarifying functional and non-functional requirements, then estimate scale (e.g., 10M users, 100M reviews). Design a high-level architecture with separate services for reviews, media, and search, and dive into data modeling, storage choices, and trade-offs for sorting, pagination, and upvoting.
Pro tip: Emphasize trade-offs between consistency and availability for upvotes and owner responses, and discuss how to handle media storage efficiently with CDNs and object stores. Also, mention the importance of denormalization for read-heavy review browsing.
Ask questions to understand functional needs (e.g., review posting, sorting, upvoting) and non-functional needs (e.g., latency, consistency). Estimate scale: number of users, reviews, photos, and read/write ratios.
Outline main components: API gateway, review service, media service, search service, and databases. Consider using a CDN for photos and a message queue for asynchronous tasks like photo processing.
Design schemas for reviews, users, restaurants, and upvotes. Choose databases: e.g., SQL for transactional data, NoSQL for scalability, and search engine (Elasticsearch) for browsing/sorting. Discuss indexing for sorting by rating, date, helpfulness.
Detail how to implement pagination (cursor-based vs offset), sorting (by rating, date, helpfulness), owner responses (linking to reviews), and upvoting (idempotency, counters, eventual consistency).
Discuss trade-offs: consistency vs availability for upvotes, denormalization for read performance, media storage costs, and sharding strategies. Address bottlenecks and how to scale (e.g., caching, read replicas).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Read-heavy systems I'm comfortable with so caching felt obvious, but the abuse resistance part tripped me up a bit.
Start by clarifying the scale (e.g., read QPS, data volume) and the types of abuse (spam, fake reviews, scraping). Then propose a layered architecture: caching, read replicas, and CDN for reads; rate limiting, anomaly detection, and moderation pipelines for abuse. Discuss trade-offs between consistency, latency, and cost, and how you would monitor and iterate.
Pro tip: Emphasize that abuse resistance is not just about blocking bad actors but also about designing for graceful degradation and fast recovery, and that you would use a feedback loop from user reports to continuously improve detection.
Ask about expected read throughput (e.g., QPS), data size, latency SLAs, and the nature of abuse (spam, fake reviews, scraping). This ensures your design targets the right problems.
Propose a multi-layer caching strategy (CDN, application cache, database cache), read replicas, and possibly denormalization or materialized views. Consider using a NoSQL store for horizontal scaling if needed.
Outline rate limiting per user/IP, CAPTCHA for suspicious activity, anomaly detection using ML, and a moderation pipeline (automated + human). Also consider write-time validation and post-hoc analysis.
Discuss trade-offs: caching vs. freshness, strict moderation vs. user experience, cost vs. performance. Explain how you would handle eventual consistency and conflict resolution.
Describe monitoring metrics (latency, error rates, abuse detection rates), alerting, and a feedback loop to improve abuse detection. Mention auto-scaling and capacity planning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went rules-first (profanity filters, spam patterns) then described an async ML scoring queue.
Start by clarifying the requirements and scale (e.g., review volume, latency, and moderation goals), then outline a multi-stage pipeline that combines fast rule-based filtering with ML models for nuanced decisions. Emphasize trade-offs between precision/recall, latency, and cost, and explain how human review fits in for edge cases.
Pro tip: Highlight the importance of a feedback loop where human moderation decisions are used to continuously retrain and improve the ML models, and discuss how to handle adversarial users who try to evade filters.
Ask about scale (reviews per second), latency requirements, moderation goals (e.g., spam, profanity, fake reviews), and acceptable false positive/negative rates. This ensures your design aligns with business needs.
Propose a fast, deterministic layer using regex, blocklists, and heuristics to catch obvious violations (e.g., profanity, spam links) and reduce load on ML models. Discuss how to maintain and update rules.
Describe using ML models (e.g., text classification, sentiment analysis) to handle ambiguous cases, such as sarcasm, context-dependent toxicity, or fake reviews. Mention model selection, feature engineering, and threshold tuning.
Explain how low-confidence predictions or appeals are routed to human moderators, and how their decisions are fed back to retrain models, improving accuracy over time.
Discuss scaling the pipeline (e.g., async processing, caching), monitoring for drift and adversarial attacks, and trade-offs between latency, cost, and accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This felt like the most interesting part to me.
Start by clarifying the goal: produce a robust, fair restaurant ranking that balances average rating with confidence and helpfulness signals. Then outline a pipeline: collect and clean review data, compute a Bayesian-adjusted rating, incorporate helpfulness-weighted signals, and aggregate to restaurant level with time decay. Finally, discuss how to combine these into a single ranking score and validate it.
Pro tip: Emphasize that raw average ratings are misleading for restaurants with few reviews; using a Bayesian prior or Wilson score interval shows statistical maturity. Also mention that helpfulness signals should be weighted by reviewer credibility to avoid gaming.
Ask about the ranking's purpose (e.g., search, recommendations) and available data: review ratings, helpfulness votes, timestamps, reviewer history. Confirm if real-time or batch processing is needed.
Use a Bayesian average (e.g., (C * m + sum(ratings)) / (C + n)) to shrink low-volume restaurants toward the global mean. Alternatively, use a Wilson score interval for binary ratings.
Weight each review's rating by its helpfulness score (e.g., upvotes minus downvotes) and reviewer credibility (e.g., historical helpfulness). Aggregate weighted ratings to get a helpfulness-adjusted rating.
Apply exponential time decay to older reviews so recent feedback matters more. Combine the adjusted rating and helpfulness signals into a single score, possibly with a weighted sum or machine learning model.
Validate the ranking with offline metrics (e.g., correlation with user engagement) and A/B tests. Monitor for bias and gaming, and iterate on weights and decay parameters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.