Start by clarifying requirements and scale, then walk through the high-level architecture covering crawling, indexing, and serving. Dive into the feed serving path to meet the 100ms P99 latency, discussing trade-offs and optimizations.
Pro tip: Emphasize that the 100ms P99 latency is for the feed serving path only; crawling and indexing can be asynchronous. This shows you understand the system's critical path and can prioritize effectively.
Ask questions to understand the scale (e.g., number of sources, articles per day, user base), latency targets, and consistency requirements. Establish that the 100ms P99 applies to feed serving.
Outline the main components: web crawlers, content processing pipeline, indexing systems (search and recommendation), and feed serving layer. Explain data flow from crawl to serve.
Discuss crawling strategies (politeness, scheduling, deduplication), content extraction, and how to build search and recommendation indexes (e.g., inverted index, embedding-based). Mention batch vs. stream processing.
Focus on the serving path: how to achieve 100ms P99. Cover caching (CDN, in-memory), precomputation of feeds, sharding, replication, and load balancing. Discuss trade-offs between freshness and latency.
Identify potential bottlenecks (e.g., hot keys, index size) and discuss trade-offs (e.g., consistency vs. availability, cost vs. performance). Propose monitoring and scaling strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came up as a follow-up and I wasn't ready for it to get as technical as it did.
Start by clarifying requirements (scale, latency, accuracy) and then propose a multi-stage pipeline: ingest, normalize, generate fingerprints, and cluster. Discuss trade-offs between exact and near-duplicate detection, and how to handle evolving stories and scalability.
Pro tip: Mention that deduplication is not just about removing exact copies but also about grouping similar stories to provide a unified view, and that you'd evaluate precision/recall trade-offs based on product needs.
Ask about scale (articles per day), latency needs (real-time vs batch), definition of 'similar' (exact copy, paraphrased, same event), and desired output (remove duplicates or cluster).
Collect articles from sources, extract text, and normalize (lowercase, remove punctuation, stemming/lemmatization) to reduce superficial differences.
Generate fingerprints using techniques like MinHash, SimHash, or shingling to efficiently compare documents. For semantic similarity, consider embeddings and approximate nearest neighbor search.
Group similar articles using clustering algorithms (e.g., connected components, DBSCAN) based on similarity thresholds. Select a representative article per cluster or merge information.
Design for scale using distributed processing (e.g., Spark) and indexing (e.g., Elasticsearch). Continuously evaluate precision/recall and adjust thresholds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining crawl freshness and index update latency, then explain the tradeoff: fresher crawls reduce staleness but increase load and cost, while lower latency updates improve user experience but require more resources. Balance by segmenting content by importance and freshness requirements, using adaptive crawl schedules and incremental indexing with prioritization.
Pro tip: Tie the tradeoff to business impact: for a news system, breaking news needs low latency, while evergreen content can tolerate staleness. Propose a tiered SLA and monitor freshness/latency metrics to dynamically adjust.
Clarify what crawl freshness (time since last crawl) and index update latency (time from crawl to searchable) mean, and set target SLAs based on content type and user expectations.
Discuss how increasing crawl frequency improves freshness but raises bandwidth, processing, and cost; reducing index latency requires more frequent indexing and can strain resources.
Classify content (e.g., breaking news, regular articles, archives) and assign different crawl and indexing strategies to each tier to optimize resource use.
Propose dynamic crawl scheduling based on change rates, and incremental indexing with prioritization queues to balance freshness and latency.
Define metrics (e.g., freshness lag, indexing delay) and set up alerts; continuously tune parameters based on traffic patterns and feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I felt most out of my depth.
Start by clarifying the constraints: feed latency SLA, pull-based architecture details (e.g., clients polling), and model characteristics. Then propose a layered optimization strategy covering caching, precomputation, model efficiency, and infrastructure, while discussing trade-offs between latency, freshness, and cost.
Pro tip: Emphasize that in pull-based systems, the client controls the request timing, so you must optimize both the serving path and the data freshness strategy. Mention that you'd measure end-to-end latency and set up canary deployments to validate improvements without risking the feed experience.
Ask about the latency SLA (e.g., p99 < 100ms), feed size, model update frequency, and infrastructure (e.g., cloud, on-prem). Understand the pull-based mechanism: are clients polling an API, and how often?
Break down the request flow: client request -> load balancer -> service -> feature fetching -> model inference -> response. Measure each segment to find the dominant latency contributors.
Suggest caching (e.g., precomputed feeds, Redis), model optimizations (quantization, distillation, ONNX), hardware acceleration (GPU/TPU), and asynchronous precomputation. Consider trade-offs between freshness and latency.
Since clients pull, consider techniques like long polling, HTTP/2 server push, or client-side caching with ETags. Also, precompute feeds for active users and use incremental updates.
Propose A/B testing, canary releases, and monitoring (latency percentiles, cache hit rates). Discuss how to handle failures and fallbacks to maintain latency SLAs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.