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…

Optimizing data warehouse storage

Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3, and each day we ingest and create additional Petabytes. At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions)…

Essential Suite — Artwork Producer Assistant

Introduction Netflix continues to invest in content for a global audience with a diverse range of unique tastes and interests. Correspondingly, the member experience must also evolve to connect this global audience to the content that most appeals to each of them. Images that represent…

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…

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…

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…

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…

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…

Engineering to Improve Marketing Effectiveness (Part 3) — Scaling Paid Media campaigns

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 …

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…

Extending Vector with eBPF to inspect host and container performance

Improving the tools available to our engineers to help them diagnose, triage, and work through software performance challenges in the cloud is a key goal for the cloud performance engineering team at Netflix. Today we are excited to announce latency…

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 Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

Netflix’s engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. This…

Lessons from Building Observability Tools at Netflix

Protecting a Story’s Future with History and Science

Kylee’s parents after their wedding in 1978. I — Kylee — have two photos from my parents’ wedding. Just two. This year they celebrated 40 years of marriage, so both photos were shot on film. Both capture a joy and awkwardness that come with young weddings. They’re…

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 &…

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…

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…

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question — “Can I run a check myself to understand what data is behind…

Detecting Performance Anomalies in External Firmware Deployments

Netflix has over 139M members streaming on more than half a billion devices spanning over 1,700 different types of devices from hundreds of brands. This diverse device ecosystem results in a high dimensionality feature space, often with sparse data, and can make identifying device performance issues challenging…

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…

Predictive CPU isolation of containers at Netflix

Noisy Neighbors We’ve all had noisy neighbors at one point in our life. Whether it’s at a cafe or through a wall of an apartment, it is always disruptive. The need for good manners in shared spaces turns out to be important not just for people, but for your Docker containers too. …

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…