Optimizing the Aural Experience on Android Devices with xHE-AAC

Introduction At Netflix, we are passionate about delivering great audio to our members. We began streaming 5.1 channel surround sound in 2010, Dolby Atmos in 2017, and adaptive bitrate audio in 2019. Continuing in this tradition, we are proud to announce that Netflix now streams…

MezzFS — Mounting object storage in Netflix’s media processing platform

MezzFS (short for “Mezzanine File System”) is a tool we’ve developed at Netflix that mounts cloud objects as local files via FUSE. It’s used extensively in our media processing platform, which includes services like Archer and runs features like…

Byte Down: Making Netflix’s Data Infrastructure Cost-Effective

Background on data efficiency At Netflix, we invest heavily in our data infrastructure which is composed of dozens of data platforms, hundreds of data producers and consumers, and petabytes of data. At many other organizations, an effective way to manage data infrastructure costs is to…

Ready for changes with Hexagonal Architecture

As the production of Netflix Originals grows each year, so does our need to build apps that enable efficiency throughout the entire creative process. Our wider Studio Engineering Organization has built numerous apps that help content progress from pitch (aka screenplay) to playback: ranging…

Netflix OSS and Spring Boot — Coming Full Circle

In 2007, Netflix started on a long road towards fully operating in the cloud. Much of Netflix’s backend and mid-tier applications are built using Java, and as part of this effort Netflix engineering built several cloud infrastructure libraries and systems — Ribbon for load balancing, Eureka for service discovery, and…

Netflix at the Spinnaker Summit 2018

Since releasing Spinnaker as an open source project in late 2015, the Spinnaker user and contributor community has grown by leaps and bounds. Our strong partnership with Google and other companies has led to a plethora of innovations and features that enable rapid, safe, and reliable delivery…

Netflix at Spark+AI Summit 2018

A glimpse at Spark usage for Netflix Recommendations Apache Spark has been an immensely popular big data platform for distributed computing. Netflix has been using Spark extensively for various batch and stream-processed workloads. A substantial list of use cases for Spark computation come from the various applications in the domain of content recommendations and personalization…

Seamlessly Swapping the API backend of the Netflix Android app

by Rohan Dhruva, Ed Ballot As Android developers, we usually have the luxury of treating our backends as magic boxes running in the cloud, faithfully returning us JSON. At Netflix, we have adopted the Backend for Frontend (BFF) pattern: instead of having one general purpose “backend API”, we have one…

Full Cycle Developers at Netflix

Trace Event, Chrome and More Profile Formats on FlameScope

Less than a year ago, FlameScope was released as a proof of concept for a new profile visualization. Since then, it helped us, and many other users, to easily find and fix performance issues, and allowed us to see patterns that we had never noticed before in our profiles. As…

Packaging award-winning shows with award-winning technology

Introduction In previous blog posts, our colleagues at Netflix have explained how 4K video streams are optimized, how even legacy video streams are improved and more recently how new audio codecs can provide better aural experiences to our members. In all these cases, prior to being delivered through…

Engineering to Improve Marketing Effectiveness (Part 2) — Scaling Ad Creation and Management

In the first part of this series of blogs, we described our philosophy, motivations, and approach to blending ad technology into our marketing. In addition, we laid out some of the engineering undertakings to solve creative development and localization at scale. In this Part-2…

Open-sourcing Polynote: an IDE-inspired polyglot notebook

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…

Python at Netflix

As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all…

Scaling Revenue & Growth Tooling

This post shares how the Revenue & Growth Tools (RGT) team approaches creating full-stack tools for the teams that are the financial backbone of Netflix. Our primary partners are the teams of Revenue and Growth Engineering (RGE): Growth, Membership, Billing, Payments…

Netflix Studio Engineering Overview

Netflix is revolutionizing the way a modern studio operates. Our mission in Studio Engineering is to build a unified, global, and digital studio that powers the effective production of amazing content. Studio Engineering at Netflix …

Netflix Hack Day — November 2019

Hack Day at Netflix is an opportunity to build and show off a feature, tool, or quirky app. The goal is simple: experiment with new ideas/technologies, engage with colleagues across different disciplines, and have fun! We know even the silliest idea…

Netflixにおける日本語字幕の導入

Netflixでは、2015年9月の日本における配信サービス開始時から日本語字幕を提供して …

Introducing SVT-AV1: a scalable open-source AV1 framework

Netflix Headquarters, Winchester Circle. Netflix headquarters circa 2014. It’s a nice building with good architecture! This was the primary home of Netflix for a number of years during the company’s growth, but at some point Netflix had outgrown its home and needed more space. One approach to solve this problem would have been to…

Data Engineers of Netflix — Interview with Dhevi Rajendran

This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Dhevi Rajendran is a Data Engineer on the Growth Data Science and Engineering team. Dhevi joined Netflix in July 2020 and is one…

Bringing Rich Experiences to Memory-constrained TV Devices

Netflix has over 148M paid members streaming on more than half a billion devices spanning over 1,900 different types. In the TV space alone, there are hundreds of device types that run the Netflix app. We need to support the same rich Netflix…

Modernizing the Web Playback UI

Since 2013, the user experience of playing videos on the Netflix website has changed very little. During this period, teams at Netflix have rolled out amazing video playback features, but the visual design and user controls of the playback UI have remained the same. The visual design and user controls…

Performance comparison of video coding standards: an adaptive streaming perspective

“This is my advice to people: Learn how to cook, try new recipes, learn from your mistakes, be fearless, and above all have fun” — Julia Child (American chef, author, and television personality) At Netflix, we are continually refining the recipes we use to serve your favorite shows and movies…

Unbundling Data Science Workflows with Metaflow and AWS Step Functions

tl;dr Today, we are releasing a new job scheduler integration with AWS Step Functions. This integration allows the users of Metaflow to schedule their production workflows using a highly available, scalable, maintenance-free service, without…

Keystone Real-time Stream Processing Platform