Netflix

Global streaming platform with 260M+ subscribers. Search powers both external content discovery and internal asset management.

Search Context

Netflix’s content catalog is a federated graph — different teams own different entity types (titles, talent, studios, rights). Making this graph searchable is an unusually complex distributed systems problem.

Key Engineering Work

External: Federated Graph Search

• Content catalog represented as distributed graph (movies, shows, people, studios)
• Entity embeddings capture semantic relationships from graph neighborhood
• Federated query routing: sub-queries sent to appropriate partition owners; merged at orchestration layer
• Enables similarity search across entity types (“movies similar to X”)

Internal: AMP Elasticsearch Indexing

• AMP (Asset Management Platform) indexes media assets with high-frequency metadata updates
• Alias-based blue-green indexing: build index_v2 → validate → atomically switch alias → delete index_v1
• Zero-downtime reindexing via alias decoupling
• Mapping design: field type choices (keyword vs. text, nested vs. flattened) affecting performance

Articles

• How Netflix Makes a Federated Graph Searchable — distributed graph search architecture
• Netflix Elasticsearch Indexing Strategy in AMP — indexing patterns for high-update corpus

Influence on Ranking Experimentation

Outside its own search stack, Netflix’s 2017 engineering write-up on interleaving for personalization algorithms is a frequently cited reference point for Interleaving as an online evaluation method. Booking.com cites those reported results as the reason it investigated interleaving for search ranking — see Beyond Algorithms - Ranking at Scale at Booking.com.

Related Concepts

• Knowledge Graph Search · Search Architecture · Dense Vector Retrieval · Retrieval Pipeline
• Interleaving · A-B Testing for Search — via the interleaving work cited above