The first thing I did was ask about read vs write ratio and freshness requirements, which felt right in hindsight.
Start by clarifying functional and non-functional requirements, then sketch a high-level architecture that separates ingestion, storage, ranking, and serving. Dive deep into the most challenging components like ranking and feed generation, discussing trade-offs and scalability.
Pro tip: Emphasize how you would handle the cold-start problem for personalized feeds and ensure diversity to avoid filter bubbles, showing product awareness beyond pure engineering.
Ask questions to understand scale (number of publishers, articles per day, users), latency requirements, feed types (homepage, topic, personalized), and ranking criteria (recency, popularity, personalization).
Outline the main components: ingestion pipeline (crawling, parsing, deduplication), storage (article store, user profiles, graph), ranking service, and feed serving API. Draw a block diagram.
Pick 2-3 critical areas to detail: e.g., how to ingest and process thousands of articles per second, how to compute and update rankings efficiently, and how to generate personalized feeds with low latency.
Discuss partitioning, caching, consistency vs. availability, batch vs. stream processing, and trade-offs between precomputed vs. on-the-fly ranking.
Mention monitoring (latency, throughput, ranking quality), A/B testing, and how the system can evolve (e.g., adding new feed types, handling viral content).
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I split it into two planes pretty quickly, an async ingestion API and a low-latency read API, and kept them decoupled with a durable queue in between.
Start by clarifying the requirements and constraints of the system, then present a high-level architecture that separates ingestion and feed reads. Dive into API design details for each side, covering endpoints, data models, and key considerations like scalability, reliability, and security.
Pro tip: Emphasize idempotency and rate limiting on the ingestion side, and caching and pagination on the read side—these show you understand real-world production concerns. Also, mention how you would monitor and evolve the APIs over time.
Ask questions to understand data volume, velocity, variety, read/write patterns, latency requirements, and consistency needs. This ensures your design is tailored to the problem.
Sketch a system diagram showing ingestion pipeline (e.g., API gateway, message queue, processing workers, storage) and feed read path (e.g., API gateway, cache, database, feed service). Explain data flow and component responsibilities.
Define endpoints (e.g., POST /events), request/response schemas, authentication, idempotency keys, rate limiting, and error handling. Discuss batching, validation, and async processing.
Define endpoints (e.g., GET /feed), query parameters (pagination, filters), response schemas, caching strategy, and consistency guarantees. Discuss how to handle large result sets and personalization.
Address scalability, reliability, security, monitoring, and versioning. Discuss trade-offs made (e.g., consistency vs. availability) and how you would iterate.
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This was the part I was least confident about.
Start by clarifying the requirements: what defines 'near-identical' and what is the acceptable false positive/negative rate? Then propose a multi-stage pipeline: first, generate candidate pairs using efficient blocking or locality-sensitive hashing (LSH), then compute similarity using embeddings and/or shingles, and finally apply a clustering or deduplication decision. Discuss trade-offs between precision and recall, scalability, and how to handle updates and incremental data.
Pro tip: Mention that deduplication is not just a technical problem but also a product decision: you need to define what 'same event' means and involve stakeholders to set thresholds. Also, consider using a hybrid approach combining lexical and semantic similarity to balance accuracy and cost.
Ask questions to understand scale, latency, and what constitutes a duplicate. Define a similarity metric (e.g., cosine similarity of embeddings, Jaccard similarity of shingles) and a threshold.
Use blocking or LSH (e.g., MinHash) to avoid comparing all pairs. This reduces the problem from O(n^2) to something manageable.
For candidate pairs, compute a more precise similarity score. Then use clustering (e.g., connected components, hierarchical clustering) to group near-identical stories.
Decide which story to keep (e.g., earliest, most authoritative) and how to merge metadata. Handle incremental updates and avoid re-processing entire dataset.
Set up metrics (precision, recall, F1) and a feedback loop. Monitor performance and adjust thresholds or algorithms as needed.
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Classic follow-up, and I was half-expecting it.
Start by clarifying the system context and assumptions, then trace the request flow from ingestion to storage to identify the first bottleneck. Discuss each component's scaling strategy and trade-offs, emphasizing how the system absorbs the spike without downtime.
Pro tip: Proactively mention that the first saturation point depends on the system's architecture (e.g., push vs. pull ingestion), and propose a hybrid approach with backpressure to prevent cascading failures. This shows you think about failure modes and resilience.
Ask about the system's scale, expected traffic patterns, and existing architecture. State assumptions about components like load balancers, message queues, and databases.
Walk through the path of an article from ingestion (API gateway, load balancer) to processing (message queue, workers) to storage (database, cache) and serving (CDN, search).
Analyze each component's capacity and explain which saturates first (e.g., database write throughput, message queue, or network I/O) and why.
For each component, describe how it handles the spike: horizontal scaling, buffering, rate limiting, caching, sharding, or backpressure.
Highlight trade-offs like consistency vs. availability, cost vs. scalability, and propose monitoring and auto-scaling to handle future spikes.
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Basically a freshness vs cost tradeoff question.
Start by acknowledging the trade-off: longer TTL reduces read costs but increases staleness and risks serving outdated content. Then propose a tiered caching strategy with a short TTL for breaking news and a longer TTL for evergreen content, and describe how to detect and propagate updates quickly.
Pro tip: Mention that you would monitor cache hit ratio and freshness metrics, and set up alerts for when breaking news latency exceeds a threshold. This shows you think about operational excellence, not just design.
List the negative impacts: stale content, delayed breaking news, potential user distrust, and increased load on origin when cache misses occur after TTL expiry.
Classify content into categories (e.g., breaking news, regular articles, evergreen) and assign different TTLs and cache invalidation strategies per category.
For breaking news, use a short TTL (e.g., 10-30 seconds) combined with active invalidation via pub/sub or webhooks when content updates, and consider stale-while-revalidate to serve stale content while fetching fresh.
Track cache hit ratio, origin load, and content freshness metrics; set up alerts and have a fallback to bypass cache for critical breaking news if needed.
Continuously measure the cost savings vs. freshness trade-off and adjust TTLs and invalidation strategies based on data and user feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two separate data structures, one for current vote state keyed by user+article pair, one for ordered action history per user.
Start by clarifying requirements and edge cases, then design a data model that tracks each user's current vote per article and a chronological history of vote actions. Implement the voting logic to handle no-ops and vote changes, and use a data structure that supports efficient retrieval of the last three votes per user. Analyze time and space complexity, and discuss potential optimizations or trade-offs.
Pro tip: Emphasize that changing a vote is a new action and should be recorded in the history, while a repeat vote is a no-op and should not create a new entry. This distinction is crucial for correctly implementing the 'last three votes' feature.
Ask questions to confirm details: Can users vote on their own articles? What defines a 'vote' (upvote/downvote)? Should the last three votes include only changes or also initial votes? How to handle concurrent votes?
Propose a data model with two structures: a map from (user, article) to current vote, and a per-user list (or deque) of recent vote actions. Ensure the history captures only actual changes (new votes or vote changes).
Write a function that checks the current vote: if same as new vote, do nothing; otherwise, update the current vote and append the new vote action to the user's history. Maintain the history to only keep the last three entries.
Provide a method that returns the last three vote actions for a given user, in reverse chronological order. If fewer than three exist, return all.
State time and space complexity for each operation. Discuss alternative designs (e.g., using a database, event sourcing) and their pros/cons, especially for scalability and persistence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.