--- title: "Behind the Streams: Real-Time Recommendations for Live Events Part 3" url: https://daily.dev/posts/behind-the-streams-real-time-recommendations-for-live-events-part-3-5u7i82cck source_url: https://netflixtechblog.com/behind-the-streams-real-time-recommendations-for-live-events-e027cb313f8f type: article source: "Netflix TechBlog" published: 2025-10-21T10:24:03.826Z updated: 2026-03-30T02:15:30.487Z tags: ["distributed-systems", "graphql", "kafka", "real-time-systems"] reading_time: 9 upvotes: 46 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Behind the Streams: Real-Time Recommendations for Live Events Part 3 **[Netflix TechBlog](https://daily.dev/sources/netflix)** · 9 min read · 46 upvotes · 0 comments ## Summary 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. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How Netflix Scaled Its Real-Time Service Map](https://daily.dev/posts/how-netflix-scaled-its-real-time-service-map-u7xt8lptw) · InfoQ · 2 upvotes · 0 comments - [From On-Demand to Live : Netflix Streaming to 100 Million Devices in Under 1 Minute](https://daily.dev/posts/from-on-demand-to-live-netflix-streaming-to-100-million-devices-in-under-1-minute-q5lb0w4hn) · InfoQ · 0 upvotes · 0 comments - [How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data Streams at Internet Scale](https://daily.dev/posts/how-and-why-netflix-built-a-real-time-distributed-graph-part-1-ingesting-and-processing-data-stre-ho3v4j4yn) · Netflix TechBlog · 82 upvotes · 0 comments --- Tags: [#distributed-systems](https://daily.dev/tags/distributed-systems), [#graphql](https://daily.dev/tags/graphql), [#kafka](https://daily.dev/tags/kafka), [#real-time-systems](https://daily.dev/tags/real-time-systems) [View this post on daily.dev](https://daily.dev/posts/behind-the-streams-real-time-recommendations-for-live-events-part-3-5u7i82cck)