Design a real-time faceted search system for thousands of case studies

WHAT THIS TESTS: This tests your ability to select between search engines based on indexing latency, query performance, and operational complexity rather than brand familiarity. The interviewer wants to see that you understand inverted indices, how faceted counts are computed at query time, and why schema design matters for filtering speed.

A GOOD ANSWER COVERS: First, a technology comparison with concrete trade-offs. Algolia offers managed infrastructure with sub-100ms query latency and built-in disjunctive faceting but can become expensive at scale and requires sending data to a SaaS platform. Elasticsearch provides self-hosted flexibility, complex aggregation support, and lower per-query cost but demands more operational tuning for cluster sizing and shard allocation. Second, a schema-first indexing process. You should define attributes for faceting upfront such as Industry, Company Size, and Products Used, map them to typed fields, and denormalize case study records so each document contains all filterable metadata. Third, the ingestion pipeline. Use a change data capture stream or scheduled ETL job to push updates into the search index, using incremental updates rather than full reindexes to maintain real-time freshness. Fourth, query-time mechanics. Explain that the engine builds inverted indices per facet and intersects posting lists to compute conjunctive counts, returning contextual facet values with each result set.

COMMON WRONG ANSWERS: Suggesting a relational database with LIKE queries or B-tree indexes for real-time faceting because table scans and join overhead will fail at low latency with thousands of records and multiple simultaneous filters. Recommending a full reindex on every metadata change rather than incremental updates. Choosing a technology without mentioning operational costs, data residency, or team expertise. Describing faceting as a simple GROUP BY without acknowledging that facet counts must update contextually as other filters are applied.

LIKELY FOLLOW-UPS: How would you handle hierarchical facets such as Products Used nested by category? What is your strategy for facet count approximation when exact counts are too expensive? How do you keep the index synchronized with the primary database when case studies are updated? At what scale would you switch from Algolia to Elasticsearch or vice versa?

ONE CONCRETE EXAMPLE: Imagine a case study for a healthcare startup using your analytics product. The document would include Industry as Healthcare, Company Size as 11-50, and Products Used as Analytics Dashboard. When a user filters to Healthcare AND Analytics Dashboard, the engine intersects the posting lists for both facets and returns the matching case study along with updated counts showing that only two Company Size values remain available in that subset. Algolia computes these counts automatically while Elasticsearch would require aggregation queries.