Start by clarifying requirements and scale, then design a high-level architecture covering crawling, extraction, normalization, and storage. Dive into key components like deduplication, data quality, and scalability, discussing trade-offs at each step.
Pro tip: Emphasize the importance of data freshness and accuracy, and propose a feedback loop to continuously improve extraction quality. Also, consider legal and ethical aspects of crawling, showing maturity.
Ask questions to understand the scope: number of sites, update frequency, data volume, and required data fields. This ensures the design meets actual needs.
Outline the main components: crawler, parser/extractor, normalizer, deduplicator, storage, and serving layer. Explain how data flows through the system.
Discuss crawling strategy (scheduling, politeness, scalability), extraction techniques (ML-based, rule-based), and data normalization (mapping to a standard schema).
Explain how to handle duplicates, conflicting information, and stale data. Propose validation and quality scoring mechanisms.
Discuss scaling the system (distributed crawling, storage choices) and trade-offs between consistency, latency, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the importance of ethical crawling and legal compliance, then outline a system that dynamically fetches and respects robots.txt for each partner site, and finally describe a rate-limiting strategy that adapts per site based on their tolerance and your crawling needs. Emphasize scalability, fault tolerance, and monitoring to ensure politeness across many sites.
Pro tip: Mention that you would cache robots.txt with appropriate TTL and handle changes gracefully, and that you'd implement exponential backoff with jitter on 429/503 responses to avoid hammering sites during issues.
Clarify the scale (number of partner sites, crawl frequency), legal/ethical considerations, and any existing agreements with partners. Identify if there are different politeness policies per partner.
Implement a robots.txt parser and cache with per-site TTL. Before crawling any URL, check the cached rules; if expired, re-fetch. Respect disallow rules, crawl-delay, and sitemaps. Handle fetch failures gracefully (e.g., default to disallow or conservative crawl).
Use a per-site rate limiter (e.g., token bucket) with configurable rates. Start conservative, then adjust based on response headers (Retry-After), error rates, and partner feedback. Apply exponential backoff with jitter on 429/503 errors.
Distribute crawling across workers with a centralized coordination service (e.g., Redis) for rate limits and robots.txt cache. Monitor compliance, error rates, and latency; alert on violations or anomalies.
Address scenarios like robots.txt changes, site downtime, and rate limit overrides. Implement circuit breakers to pause crawling a site if it becomes unresponsive or returns repeated errors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My instinct was to go ML-first because it scales better across new sites.
Start by defining the core trade-off between precision/control and scalability/generalization. Then walk through dimensions like maintenance, accuracy, cost, and adaptability, and conclude with a decision framework based on scale, volatility, and data availability.
Pro tip: Emphasize that the best solution is often a hybrid: use ML for broad coverage and fall back to templates for high-value sites, and mention that you'd measure ROI by tracking extraction accuracy and engineering hours saved.
Briefly explain what per-site HTML parsing templates are (hand-crafted rules for specific sites) and what ML-based extraction entails (models trained to generalize across sites).
Discuss dimensions such as accuracy, development speed, maintenance overhead, scalability, cost, and robustness to site changes.
Identify factors that influence the choice: number of sites, frequency of layout changes, availability of labeled data, and required precision.
Outline when to use each: templates for few, stable, high-value sites; ML for many, dynamic sites or when generalization is key.
Mention that a hybrid approach (ML with template fallback) or starting with templates and transitioning to ML as scale grows can be effective.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business goal and constraints (e.g., scale, latency, accuracy). Then propose a multi-stage deduplication pipeline: first normalize and enrich product data, then generate candidate pairs using blocking or hashing, and finally apply similarity scoring and clustering to identify duplicates. Emphasize trade-offs between precision and recall, and suggest a feedback loop for continuous improvement.
Pro tip: Mention that you would start with a rule-based approach for quick wins and high precision, then layer in machine learning for fuzzy matching to improve recall. Also highlight the importance of measuring business impact (e.g., reduced duplicate listings) to prioritize efforts.
Ask about scale (number of products, partners), latency requirements, acceptable error rates, and available data (e.g., titles, descriptions, images). This ensures the solution aligns with business needs.
Standardize text (lowercase, remove punctuation), extract attributes (brand, model, color), and enrich with external data (e.g., GTIN, UPC) where possible. This reduces noise and improves matching accuracy.
Use blocking techniques (e.g., MinHash LSH, phonetic algorithms) to group similar products into candidate pairs, reducing the comparison space from O(n^2) to manageable sizes.
Compute similarity scores using string metrics (Jaccard, cosine) and/or ML models (e.g., Siamese networks). Then cluster products into duplicate groups using threshold-based or graph-based methods.
Measure precision/recall on a labeled dataset, monitor performance in production, and incorporate user feedback to refine rules and models. Consider active learning for ambiguous cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about perceptual hashing to avoid storing duplicate images, tiered storage for less-accessed media, and lazy transcoding for video.
Start by clarifying the scale and access patterns of the media assets, then propose a multi-layered optimization strategy covering storage tiers, compression, deduplication, and lifecycle policies. Emphasize trade-offs between cost, latency, and quality, and how you would measure and iterate on the solution.
Pro tip: Quantify the impact: estimate potential savings (e.g., 'moving infrequently accessed media to cold storage could cut costs by 60%') and mention monitoring tools to track cost per GB and access patterns. This shows business acumen and a data-driven approach.
Ask about the volume of media, access frequency, latency requirements, and budget constraints to tailor your optimization strategy.
Discuss how you would instrument the system to understand data access patterns, hot vs. cold data, and current cost breakdown.
Suggest techniques like compression, transcoding to efficient formats, deduplication, and using tiered storage (hot, warm, cold) based on access patterns.
Describe how to automate data movement between tiers, set expiration policies, and leverage CDNs to reduce origin storage and egress costs.
Explain how you would track cost savings, performance impact, and adjust strategies based on metrics and feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the question I was least prepared for in a system design context.
Start by acknowledging that crawling partner sites involves both legal and relationship risks, and that the engineering solution must align with business and legal constraints. Then structure your answer around identifying risks, proposing technical and procedural mitigations, and emphasizing collaboration with legal and partner teams.
Pro tip: Show that you understand the difference between public data and partner data—partner agreements often impose stricter limits than robots.txt or copyright law. Mention that you would involve legal early and design for auditability, which demonstrates maturity beyond pure engineering.
Enumerate legal risks (copyright, database rights, CFAA, GDPR/CCPA) and ToS risks (rate limits, prohibited scraping, account termination). Also consider partner relationship damage and reputational harm.
Examine the partner contract, API terms, robots.txt, and site ToS to understand explicit permissions and restrictions. Clarify ambiguous terms with legal counsel.
Implement rate limiting, respect robots.txt and crawl-delay, use caching, identify your crawler with a clear user-agent, and avoid scraping personal data. Consider using official APIs if available.
Set up logging, alerting, and periodic audits to ensure compliance. Define escalation paths for when partners raise concerns or when terms change.
Work with legal, business, and partner teams to get explicit permission when needed and to maintain transparency. Document decisions and keep partners informed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.