--- title: Behind the Streams: Real-Time Recommendations for Live... description: Netflix engineered a real-time recommendation system to handle live event streaming at massive scale, serving over 100 million concurrent devices. The solution... canonical: https://daily.dev/posts/behind-the-streams-real-time-recommendations-for-live-events-part-3-5u7i82cck twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Behind the Streams: Real-Time Recommendations for Live Events Part 3 | daily.dev og:description: Netflix engineered a real-time recommendation system to handle live event streaming at massive scale, serving over 100 million concurrent devices. The solution... og:url: https://daily.dev/posts/behind-the-streams-real-time-recommendations-for-live-events-part-3-5u7i82cck og:image: https://api.daily.dev/og/posts/5U7I82cCk.png og:image:alt: Behind the Streams: Real-Time Recommendations for Live Events Part 3 og:image:width: 1200 og:image:height: 630 og:locale: 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 - [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 - [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 We Built a Real-Time Stock Pricing System at Scale](https://daily.dev/posts/how-we-built-a-real-time-stock-pricing-system-at-scale-enzk8krjp) · ITNEXT · 17 upvotes · 0 comments - [How Netflix Live Streams to 100 Million Devices in 60 Seconds](https://daily.dev/posts/how-netflix-live-streams-to-100-million-devices-in-60-seconds-yovkqxg1j) · ByteByteGo · 17 upvotes · 1 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) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"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","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/behind-the-streams-real-time-recommendations-for-live-events-part-3-5u7i82cck"},"datePublished":"2025-10-21T10:24:03.826Z","dateModified":"2026-03-30T02:15:30.487Z","description":"Netflix engineered a real-time recommendation system to handle live event streaming at massive scale, serving over 100 million concurrent devices. 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