By Rajiv Shringi , Kaidan Fullerton , Oleksii Tkachuk and Kartik Sathyanarayanan Introduction Netflix’s TimeSeries Abstraction is a scalable system for ingesting and querying petabytes of temporal event data with millisecond latency. We use Apache Cassandra 4.x as the underlying storage for these main reasons: Throughput, latency, and cost : Cassandra can handle millions of low‑latency reads and writes in…
Netflix Technology Blog
https://netflixtechblog.com/ · 10 posts · history since 2026 · failing
3 Jun
29 May
By Oleksii Tkachuk , Kartik Sathyanarayanan , Rajiv Shringi Introduction Netflix has a diverse range of graph use cases, each serving specific business needs with unique functionality and performance requirements. These use cases fall into two broad categories: OLAP : These use cases typically involve open-ended and algorithmic exploration of large graph datasets. They often utilize industry-standard models and languages…
By Parth Jain , Rakesh Sukumar , Yingwu Zhao , Renzo Sanchez & Nathan Fisher How we built a living map of our distributed infrastructure to help engineers understand dependencies, troubleshoot faster, and keep Netflix running smoothly for our members around the world. The Puzzle with a Thousand Pieces Picture this: It’s 3am, and an engineer gets paged. One of…
8 May
By John Burns and Emily Yuan Introduction At Netflix, we operate using a polyrepo strategy with tens of thousands of Java repositories. This means that we need to have ways of sharing common build logic across these repositories. On the JVM Ecosystem team within Java Platform, we build tooling such as the Nebula suite of Gradle plugins to provide standard…
4 May
Saish Sali , Nipun Kumar , Sura Elamurugu Introduction As Netflix has grown, machine learning continues to support our ability to deliver value to members and drive excellence across multiple areas of our business. When Netflix began investing in machine learning over a decade ago, it was primarily focused on a single domain: personalization. Scala was the industry standard, our…
1 May
By Nipun Kumar , Rajat Shah , Peter Chng Introduction This is the first blog post in a multi-part series that shares technical insights into how our ML model serving infrastructure powers several personalized experiences at scale across various domains (e.g., title recommendations, commerce). In this introductory blog post, we will dive into our domain-independent API abstraction and its traffic…
24 Apr
Orchestrating Media Workflows Through Strategic Collaboration Authors: Eric Reinecke , Bhanu Srikanth Introduction to Content Hub’s Media Production Suite At Netflix, we want to provide filmmakers with the tools they need to produce content at a global scale, with quick turnaround and choice from an extraordinary variety of cameras, formats, workflows, and collaborators. Every series or film arrives with its…
17 Apr
By: Brett Axler , Casper Choffat , and Alo Lowry In the three years since our first Live show, Chris Rock: Selective Outrage , we have witnessed an incredible expansion of our live content slate and the live operations that support it. From modest beginnings of streaming just one show per month, we are now capable of streaming over nine…
10 Apr
by Gabriela Alessio , Cameron Taylor , and Cameron R. Wolfe Introduction When members log into Netflix, one of the hardest choices is what to watch. The challenge isn’t a lack of options — there are thousands of titles — but finding the most intriguing one is complex and deeply personal. To help, we surface personalized promotional assets , especially…
6 Apr
By Ben Sykes In a previous post , we described how Netflix uses Apache Druid to ingest millions of events per second and query trillions of rows, providing the real-time insights needed to ensure a high-quality experience for our members. Since that post, our scale has grown considerably. With our database holding over 10 trillion rows and regularly ingesting up…