Behind the Streams: Real-Time Recommendations for Live Events Part 3 By: Kris Range, Ankush Gulati, Jim Isaacs, Jennifer Shin, Jeremy Kelly, Jason Tu This is part 3 in a series called “Behind the …

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Netflix engineered a real-time recommendation system to handle live event streaming at massive scale, serving over 100 million concurrent devices. The solution uses a two-phase approach: prefetching recommendations and metadata during natural browsing patterns before events, then broadcasting low-cardinality state updates via WebSocket when events start. This architecture solves the thundering herd problem by distributing load over time and minimizing real-time compute requirements. The system leverages GraphQL schemas, Apache Kafka, and a two-tier pub/sub architecture to deliver updates in under a minute during peak load, while adaptive traffic prioritization and cache jitter prevent unexpected traffic spikes.

Behind the Streams: Real-Time Recommendations for Live Events Part 3

Orchestrating the moment: Real-time Recommendations

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Balancing the Moment: Throughput Management