GraphQL Search Indexing

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…

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…

How Netflix uses eBPF flow logs at scale for network insight

Netflix has developed a network observability sidecar called Flow Exporter that uses eBPF tracepoints to capture TCP flows at near real time. At much less than 1% of CPU and memory on the instance, this highly performant sidecar provides…

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…

Netflix Studio Hack Day — May 2019

Hack Days are a big deal at Netflix. They’re a chance to bring together employees from all our different disciplines to explore new ideas and experiment with emerging technologies. For the most recent hack day, we channeled our creative energy towards…

Netflix Cloud Security: Detecting Credential Compromise in AWS

Credential compromise is an important concern for anyone operating in the cloud. The problem becomes more obvious over time, as organizations continue to adopt cloud resources as part of their infrastructure without maintaining an accompanying capability to observe and react to these compromises…

Lerner — using RL agents for test case scheduling

Introduction Netflix brings delightful customer experiences to homes on a variety of devices that continues to grow each day. Partners across the globe leverage Netflix device certification process on a continual basis to ensure that quality products and experiences are…

Streaming Video Experimentation at Netflix: Visualizing Practical and Statistical Significance

Streaming video experimentation at Netflix seeks to optimize the Quality of Experience (QoE for short) of the videos we stream to our 130 million members around the world. To measure QoE, we look at a wide variety of metrics for each playback session, including play delay; the…

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…

Evolution of Application Data Caching : From RAM to SSD

Building a Cross-platform In-app Messaging Orchestration Service

Thoughtful, relevant, and timely messaging is an integral part of a customer’s Netflix experience. The Netflix Messaging Engineering team builds the platform and the messages to communicate with Netflix customers. Messages in the Netflix App In-app messages at Netflix fall broadly into two channels — Notifications and Alerts. Notifications are content recommendations that show up…

Keeping Customers Streaming — The Centralized Site Reliability Practice at Netflix

We’re privileged to be in the business of bringing joy to our customers at Netflix. Whether it’s a compelling new series or an innovative product feature, we strive to provide a best-in-class service that people love and can enjoy anytime, anywhere…

Netflix Now Streaming AV1 on Android

Today we are excited to announce that Netflix has started streaming AV1 to our Android mobile app. AV1 is a high performance, royalty-free video codec that provides 20% improved compression efficiency over our VP9† encodes. AV1 is made…

Metacat: Making Big Data Discoverable and Meaningful at Netflix

Most large companies have numerous data sources with different data formats and large data volumes. These data stores are accessed and analyzed by many people throughout the enterprise. At Netflix, our data warehouse consists of a large number of data sets stored in Amazon S3…

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…

The End of Video Coding?

In the IEEE Signal Processing Magazine issue November 2006 article “Future of Video Coding and Transmission” Prof. Edward Delp started by asking the panelists “Is video coding dead? Some feel that, with the higher coding efficiency of the H.264/MPEG-4 . . . perhaps there is not much more to do…

My (Seemingly) Random Walk to Netflix

By Sean Barnes, Studio Production Data Science & Engineering I am going to tell you a story about a person that works for Netflix. That person grew up dreaming of working in the entertainment industry. They attended the University of Southern California, double majored in data science and television &…

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 Samuel Setegne

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. Samuel Setegne is a Senior Software Engineer on the Core Data Science and Engineering team. Samuel and his team build tools and frameworks…

Re-Architecting the Video Gatekeeper

This is the story about how the Content Setup Engineering team used Hollow, a Netflix OSS technology, to re-architect and simplify an essential component in our content pipeline — delivering a large amount of business value in the process. The Context Each movie and show on the Netflix service is carefully curated…

Part 2: Scheduling Notebooks at Netflix

At Netflix we’ve put substantial effort into adopting notebooks as an integrated development platform. The idea started as a discussion of what development and collaboration interfaces might look like in the future. It evolved into a strategic bet on notebooks, both as…

Applying Netflix DevOps Patterns to Windows

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…

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…

Page Simulator

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 many aspects of our service, including…

Simplifying Media Innovation at Netflix with Archer

Imagine you are developing a quick prototype to sift through all the frames of the movie Bright to find the best shots of Will Smith with an action-packed background. Your goal is to get the computer vision…