This is a lot to cover in one question and I definitely felt the scope creep in real time.
Start by clarifying functional and non-functional requirements (scale, latency, consistency) and then walk through the high-level architecture: catalog modeling, search indexing pipeline, query processing, ranking, and integration with order flow. Emphasize trade-offs (e.g., denormalization vs normalization, batch vs real-time indexing) and tie decisions back to Disney's content-heavy, high-traffic e-commerce context.
Pro tip: Show awareness of Disney's unique catalog (e.g., movies, merchandise, experiences) by discussing how to handle rich media and franchise-based navigation, and mention the importance of separating read and write paths for scalability.
Ask about scale (QPS, catalog size), latency SLAs, consistency needs, and whether search includes personalization or recommendations. Define the boundaries of the system (e.g., does it include inventory checks?).
Propose a schema for products, categories, and attributes, considering hierarchical categories and facets. Discuss storage choices (e.g., relational DB for transactions, NoSQL for flexible attributes) and how to handle updates.
Outline how product data flows from the source of truth to the search index (e.g., via CDC or batch jobs). Choose an indexing engine (e.g., Elasticsearch) and discuss index design, sharding, and replication for scalability.
Describe query parsing (tokenization, stemming), filtering, and ranking signals (relevance, popularity, personalization). Explain how to blend category browsing and search, and how to handle pagination and sorting.
Explain how search results link to product detail pages and cart/order services, ensuring consistency (e.g., price and inventory). Discuss caching, eventual consistency, and fallback strategies.
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