Start by clarifying functional and non-functional requirements, then sketch a high-level architecture covering ingestion, processing, storage, and serving. Dive into key components like publisher onboarding, deduplication, ranking, and personalization, discussing trade-offs and scalability at each stage.
Pro tip: Emphasize how you would handle the cold-start problem for new publishers and users, and discuss the trade-offs between freshness and relevance in the ranking algorithm.
Ask questions to understand scope: expected scale (publishers, articles, users), latency requirements, personalization depth, and content types (text, images, video).
Outline the main components: publisher onboarding, ingestion pipeline, content processing (deduplication, categorization), storage (raw and processed), indexing, ranking, and feed delivery.
Detail critical parts: how to ingest content (push vs. pull), deduplication strategies (simhash, embeddings), ranking signals (freshness, authority, user engagement), and personalization (collaborative filtering, content-based).
Discuss data models for articles, publishers, users, and interactions; choose appropriate databases (e.g., Cassandra for articles, Redis for caching, Elasticsearch for search).
Address scaling ingestion (Kafka, partitioning), serving (CDN, caching), and trade-offs like consistency vs. availability, latency vs. freshness, and cost vs. performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They pushed on this after I mentioned retry strategies.
Start by clarifying the pipeline stages and failure modes, then explain how you would use idempotency keys and deduplication to make retries safe. Discuss the trade-offs between exactly-once and at-least-once with idempotent consumers, and how you would implement it with a message queue and database transactions.
Pro tip: Emphasize that exactly-once delivery is often a myth in distributed systems; instead, aim for effectively-once processing through idempotent operations and deduplication. Mention that you would measure and monitor duplicate rates to validate the approach.
Ask about the pipeline architecture, sources of retries (network, timeouts, crashes), and the cost of duplicates. Identify where exactly-once is needed versus where at-least-once with idempotency suffices.
Use unique idempotency keys (e.g., article URL + hash) for each ingestion request. Ensure that processing the same key multiple times produces the same result without side effects, such as upserts in a database.
Store processed keys in a durable store with a TTL or use a database unique constraint. Combine with transactions or two-phase commits to atomically update state and mark messages as processed.
Configure retries with exponential backoff and jitter. Route persistently failing messages to a dead-letter queue for manual inspection, ensuring they don't block the pipeline.
Track metrics like duplicate processing rate, retry counts, and DLQ size. Set up alerts and periodically audit for consistency to ensure the system behaves as expected.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly a detail I hadn't prepped deeply.
Start by outlining a layered architecture that separates policy enforcement (robots.txt, copyright) from crawling mechanics. Then, detail how you would implement each layer: fetching and caching robots.txt, respecting directives, and applying copyright filters. Finally, discuss scalability considerations and trade-offs.
Pro tip: Emphasize that robots.txt is a crawl-time directive, not a legal document, and that copyright compliance requires a separate, often manual, review process. Mention that you would log all decisions for auditability.
Design a system to fetch robots.txt for each domain, cache it with appropriate TTL, and handle errors gracefully. Ensure that the crawler respects the most recent version.
Implement a parser that extracts user-agent specific rules, disallow/allow paths, and crawl-delay. Integrate this into the crawler's URL frontier to filter URLs before fetching.
Incorporate checks for copyright notices, license metadata, and content types. Use heuristics or third-party services to identify copyrighted material and exclude it from crawling or storing.
Distribute robots.txt fetching and caching across crawler nodes, use a centralized policy service, and monitor compliance metrics. Implement rate limiting and backoff to avoid overloading servers.
Log all compliance decisions and provide audit trails. Periodically review and update policies to adapt to changing legal landscapes and website practices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I fumbled the math a little on article storage.
Start by clarifying the system's scope and key assumptions (e.g., number of users, data size per user, read/write ratio). Then break down capacity into storage, ingestion throughput, and read traffic, using simple math and round numbers to estimate each component. Finally, discuss how these estimates influence design decisions like sharding, caching, and replication.
Pro tip: Always state your assumptions explicitly and round aggressively—interviewers care more about your reasoning process than precise numbers. Also, relate estimates to real-world constraints (e.g., 'This would require 100 servers, which is costly, so we might optimize by...').
Ask questions to understand the system's scale: number of users, data generated per user per day, read/write ratio, and growth projections. State any assumptions you make.
Calculate total data volume over time (e.g., 1 year) by multiplying daily data generation by retention period. Consider replication and overhead.
Compute average and peak write requests per second (RPS) based on daily active users and actions per user. Convert to data size per second if needed.
Determine read RPS using the read/write ratio or by estimating queries per user per day. Consider peak multipliers (e.g., 2-3x average).
Recap the estimates and explain how they affect architecture choices (e.g., sharding, caching, CDN, database selection).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I kept this high-level: user interest vectors updated async, a candidate generation step, then a ranking model pass before serving.
Start by clarifying requirements and scale, then propose a modular architecture separating candidate generation, ranking, and personalization. Discuss trade-offs between consistency models (e.g., strong vs. eventual) and how they impact user experience and system complexity. Tie your design to experimentation and metrics to show business impact.
Pro tip: Emphasize that consistency guarantees should be driven by product requirements—e.g., for a feed, eventual consistency with bounded staleness is often acceptable, but for actions like 'undo' you might need stronger guarantees. This shows you balance technical rigor with pragmatism.
Ask about user base size, feed update frequency, latency SLAs, and personalization goals (e.g., engagement vs. diversity). This ensures your design is grounded in real constraints.
Outline how user features (interactions, profile) and item features are computed and stored. Propose a feature store and model serving architecture (e.g., online/offline consistency).
Describe a multi-stage ranking system: candidate generation (e.g., collaborative filtering), light ranking (e.g., logistic regression), and heavy ranking (e.g., deep neural network). Discuss how to handle cold start and exploration.
Propose consistency levels for different data types: user actions (strong), feed updates (eventual with bounded staleness), and model updates (versioned). Explain how you'd implement them (e.g., read-your-writes, monotonic reads).
Explain how you'd A/B test ranking changes, measure online metrics (CTR, dwell time), and guard against regressions. Mention logging and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partitioned article store by source domain hash, index sharded by topic/category with replicas per region.
Start by clarifying requirements like read/write patterns, consistency needs, and latency goals. Then propose a multi-region architecture that partitions data by region or tenant, with separate strategies for the article store (e.g., geo-sharded databases) and indexing layer (e.g., regional search clusters with cross-region replication). Discuss trade-offs between consistency, availability, and cost, and how you'd handle failover and data synchronization.
Pro tip: Emphasize that multi-region scalability is not just about technology but also about operational complexity—highlight how you'd automate failover, monitor cross-region replication lag, and design for graceful degradation. Mention that you'd start with a single-region MVP and evolve, showing pragmatism.
Ask about expected scale, read/write ratios, latency SLAs, consistency requirements, and budget. This ensures your design aligns with business needs.
Propose partitioning the article store by region or tenant to keep data close to users. For the indexing layer, consider sharding by region with replication for global search.
Select databases that support multi-region (e.g., CockroachDB, DynamoDB Global Tables) and search engines with cross-cluster replication (e.g., Elasticsearch CCR). Explain why they fit.
Discuss trade-offs between strong and eventual consistency. For the article store, consider quorum reads/writes; for indexing, use change data capture (CDC) to sync updates asynchronously.
Outline how to detect region failures, route traffic, and recover. Include monitoring for replication lag, error rates, and latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.