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Design fast query-to-listing retrieval over tens of millions of listings

eBayApplied Scientist · Technical Screen
EasyApplied ScientistTechnical ScreenML System Design

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An ML system design question on retrieving relevant listings for free-text search queries across a catalog of tens of millions of items with low latency. It tests candidate generation, indexing, embedding training, freshness, filtering and scaling, plus the offline and A/B test metrics used to evaluate retrieval.

Users type free-text search queries into a marketplace, and the catalog holds tens of millions of listings. Design how the system quickly retrieves the listings relevant to a query. Expect the discussion to drill into each component you propose.

Constraints and Clarifications

  • The catalog holds tens of millions of listings. No latency budget, query volume, or freshness requirement was specified; ask for them.
  • Assume listings have at least a title, description, category, structured attributes, price and availability.

Clarifying Questions

  • What latency budget does retrieval get within the full search request, and what peak query rate must it sustain?
  • How quickly must a new, edited, sold or removed listing be reflected in results?
  • Does retrieval produce a candidate set for a downstream ranker, or the final ordered results?
  • Which hard filters must be honored (category, price range, location, condition, availability)?
  • What interaction logs exist (queries, impressions, clicks, purchases) for training and evaluation?

Hints

Hint 1

Precompute: Think about what can be computed for every listing before any query arrives.

Hint 2

Complementary signals: Exact term matching and semantic matching each fail on different kinds of queries.

Follow-up questions

  • How do you train the query and listing encoders, and where do the negative examples come from?
  • A listing sells out. How quickly, and through what path, does it disappear from both the lexical and the vector index?
  • How do you apply a price or category filter with approximate nearest-neighbor search without losing recall?
  • Which offline metrics and which A/B test metrics would decide whether a new retrieval model ships?