Matt Zimmer discusses architectural patterns -service decomposition, stateless application tiers, and polyglot persistence- and migration strategies used by Netflix.
Diptanu Choudhury discusses the design of Netflix’ distributed scheduler based on Mesos and Titan, focusing on bin packing algorithms, scaling in and out of clusters, fault tolerance, and redundancy.
Roy Rapoport shares some of the lessons Netflix learned building a monitoring system, the challenges, pitfalls and opportunities encountered along the way.
Sudhir Tonse discusses about the robust interprocess communications (IPC) framework that Netflix built (Ribbon).
Ben Christensen summarizes why the Rx programming model was chosen and demonstrates how it is applied to a variety of use cases.
Sangeeta Narayanan goes over how Netfix got to the current continuous delivery state, the lessons they learnt and the successes they enjoyed along the way.
Husain shows the Reactive Extensions (Rx) library which allows one to treat events as collections, how Netflix uses Rx on the client and the server, allowing it to build end-to-end reactive systems.
The authors discuss Netflix's new stream processing system that supports a reactive programming model, allows auto scaling, and is capable of processing millions of messages per second.
The authors present basic concepts about Spring Boot and Netflix OSS software and how to integrate Netflix OSS technologies into Spring Boot.
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
Ruslan Meshenberg discusses Netflix's challenges, operational tools and best practices needed to provide high availability through multiple regions.
Dianne Marsh describes how Netflix' tooling, especially the continuous delivery system, allows developers to push the button for production deployment, and helps them to recover if necessary.