Page Simulator

Page Simulation for Better Offline Metrics at Netflix by David Gevorkyan, Mehmet Yilmaz, Ajinkya More, Gaurav Agrawal, Richard Wellington, Vivek Kaushal, Prasanna Padmanabhan, Justin Basilico At Netflix, we spend a lot of effort to make it easy for our members to find content they will love. To make this happen, we personalize Read more…

GraphQL Search Indexing

by Artem Shtatnov and Ravi Srinivas Ranganathan Almost a year ago we described our learnings from adopting GraphQL on the Netflix Marketing Tech team. We have a lot more to share since then! There are plenty of existing resources describing how to express a search query in GraphQL and paginate Read more…

Open-sourcing Polynote: an IDE-inspired polyglot notebook

Jeremy Smith, Jonathan Indig, Faisal Siddiqi We are pleased to announce the open-source launch of Polynote: a new, polyglot notebook with first-class Scala support, Apache Spark integration, multi-language interoperability including Scala, Python, and SQL, as-you-type autocomplete, and more. Polynote provides data scientists and machine learning researchers with a notebook environment that allows Read more…

ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

Faisal Siddiqi Infrastructure for Contextual Bandits and Reinforcement Learning — theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches can Read more…

How Netflix microservices tackle dataset pub-sub

By Ammar Khaku Introduction In a microservice architecture such as Netflix’s, propagating datasets from a single source to multiple downstream destinations can be challenging. These datasets can represent anything from service configuration to the results of a batch job, are often needed in-memory to optimize access and must be updated as Read more…

Evolving Regional Evacuation

Niosha Behnam | Demand Engineering @ Netflix At Netflix we prioritize innovation and velocity in pursuit of the best experience for our 150+ million global customers. This means that our microservices constantly evolve and change, but what doesn’t change is our responsibility to provide a highly available service that delivers 100+ Read more…

Applying Netflix DevOps Patterns to Windows

Baking Windows with Packer By Justin Phelps and Manuel Correa Customizing Windows images at Netflix was a manual, error-prone, and time consuming process. In this blog post, we describe how we improved the methodology, which technologies we leveraged, and how this has improved service deployment and consistency. Artisan Crafted Images In the Netflix Read more…