They were explicit upfront that they wanted roughly equal time on each half, which I appreciated but also found stressful because I kept wanting to stay in the systems weeds.
Start by clarifying requirements (scale, latency, consistency, query types) and then present a high-level architecture that covers both algorithmic and systems aspects. Dive into the inverted index, query parsing, ranking, and relevance scoring, then discuss sharding, replication, query fan-out, result merging, hot shard handling, and the indexing pipeline. Emphasize trade-offs and how you would handle scale, failures, and updates.
Pro tip: Show that you understand the end-to-end data flow: from document ingestion to query serving, and how choices in the indexing pipeline (e.g., batch vs. incremental) affect query latency and relevance. Also, mention how you would monitor and tune relevance using A/B testing and user feedback.
Ask questions to understand scale (documents, QPS), latency requirements, consistency needs, query types (keyword, phrase, fuzzy), and update frequency. This shapes the design.
Describe the inverted index structure, tokenization, stemming, and query parsing. Explain ranking algorithms (e.g., TF-IDF, BM25) and relevance scoring, including how to incorporate signals like recency or popularity.
Cover sharding strategies (e.g., by document ID or term), replication for fault tolerance, query fan-out to shards, and merging results (e.g., using a heap for top-K). Discuss handling hot shards via dynamic rebalancing or caching.
Explain how documents are ingested, processed (parsing, enrichment), and indexed. Discuss batch vs. incremental indexing, handling updates/deletes, and ensuring consistency between replicas.
Discuss trade-offs like latency vs. consistency, sharding strategies, and caching. Mention optimizations like query caching, early termination, and relevance tuning via A/B testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.