by Zhi Li, Kyle Swanson, Christos Bampis, Lukáš Krasula and Anne Aaron

Over the past few years, we have been striving to make VMAF a more usable tool not just for Netflix, but for the video community at large. This tech blog highlights our recent progress toward this goal.

VMAF is a video quality metric that Netflix jointly developed with a number of university collaborators and open-sourced on Github. VMAF was originally designed with Netflix’s streaming use case in mind, in particular, to capture the video quality of professionally generated movies and TV shows in the presence of encoding and scaling artifacts. Since its open-sourcing, we have started seeing VMAF being applied in a wider scope within the open-source community. To give a few examples, VMAF has been applied to live sports, video chat, gaming, 360 videos, and user generated content. …


By Fabio Kung, Sargun Dhillon, Andrew Spyker, Kyle Anderson, Rob Gulewich, Nabil Schear, Andrew Leung, Daniel Muino, and Manas Alekar

As previously discussed on the Netflix Tech Blog, Titus is the Netflix container orchestration system. It runs a wide variety of workloads from various parts of the company — everything from the frontend API for netflix.com, to machine learning training workloads, to video encoders. In Titus, the hosts that workloads run on are abstracted from our users. The Titus platform maintains large pools of homogenous node capacity to run user workloads, and the Titus scheduler places workloads. This abstraction allows the compute team to influence the reliability, efficiency, and operability of the fleet via the scheduler. The hosts that run workloads are called Titus “agents.” …


By Anupom Syam

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) as the data lands into our warehouse.

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. …


Part of our series on who works in Analytics at Netflix — and what the role entails

by Alex Diamond

This Q&A aims to mythbust some common misconceptions about succeeding in analytics at a big tech company.

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This isn’t your typical recruiting story. I wasn’t actively looking for a new job and Netflix was the only place I applied. I didn’t know anyone who worked there and just submitted my resume through the Jobs page 🤷🏼‍♀️ . I wasn’t even entirely sure what the right role fit would be and originally applied for a different position, before being redirected to the Analytics Engineer role. So if you find yourself in a similar situation, don’t be discouraged!

How did you come to Netflix?

Movies and TV have always been one of my primary sources of joy. I distinctly remember being a teenager, perching my laptop on the edge of the kitchen table to “borrow” my neighbor’s WiFi (back in the days before passwords 👵🏻), and streaming my favorite Netflix show. I felt a little bit of ✨magic✨ come through the screen each time, and that always stuck with me. So when I saw the opportunity to actually contribute in some way to making the content I loved, I jumped at it. …


Martin Tingley

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In November, Netflix was a proud sponsor of the 2020 Conference on Digital Experimentation (CODE), hosted by the MIT Initiative on the Digital Economy. As well as providing sponsorship, Netflix data scientists were active participants, with three contributions.

Eskil Forsell and colleagues presented a poster describing Success stories from a democratized experimentation platform. Over the last few years, we’ve been Reimagining Experimentation Analysis at Netflix with an open platform that supports contributions of metrics, methods and visualizations. This poster, reproduced below, highlights some of the success stories we are now seeing, as data scientists across Netflix partner with our platform team to broaden the suite of methodologies we can support at scale. …


By: John Blair, Netflix Partner Engineering

The Netflix application runs on hundreds of smart TVs, streaming sticks and pay TV set top boxes. The role of a Partner Engineer at Netflix is to help device manufacturers launch the Netflix application on their devices. In this article we talk about one particularly difficult issue that blocked the launch of a device in Europe.

The mystery begins

Towards the end of 2017, I was on a conference call to discuss an issue with the Netflix application on a new set top box. The box was a new Android TV device with 4k playback, based on Android Open Source Project (AOSP) version 5.0, aka “Lollipop”. …


In our previous post and QConPlus talk, we discussed GraphQL Federation as a solution for distributing our GraphQL schema and implementation. In this post, we shift our attention to what is needed to run a federated GraphQL platform successfully — from our journey implementing it to lessons learned.

Netflix GraphQL Federation
Netflix GraphQL Federation

Our Journey so Far

Over the past year, we’ve implemented the core infrastructure pieces necessary for a federated GraphQL architecture as described in our previous post:


by Melody Dye*, Chaitanya Ekanadham*, Avneesh Saluja*, Ashish Rastogi
* contributed equally

Netflix is pioneering content creation at an unprecedented scale. Our catalog of thousands of films and series caters to 195M+ members in over 190 countries who span a broad and diverse range of tastes. Content, marketing, and studio production executives make the key decisions that aspire to maximize each series’ or film’s potential to bring joy to our subscribers as it progresses from pitch to play on our service. Our job is to support them.

The commissioning of a series or film, which we refer to as a title, is a creative decision. Executives consider many factors including narrative quality, relation to the current societal context or zeitgeist, creative talent relationships, and audience composition and size, to name a few. The stakes are high (content is expensive!) as is the uncertainty of the outcome (it is difficult to predict which shows or films will become hits). To mitigate this uncertainty, executives throughout the entertainment industry have always consulted historical data to help characterize the potential audience of a title using comparable titles, if they exist. …


Introducing gnmi-gateway: a modular, distributed, and highly available service for modern network telemetry via OpenConfig and gNMI

By: Colin McIntosh, Michael Costello

Netflix runs its own content delivery network, Open Connect, which delivers all streaming traffic to our members. A backbone network underlies a large portion of the CDN, and we also run the high capacity networks that support our studios and corporate offices. In order to design, operate, and measure these networks, we must collect metrics and state data from the thousands of devices that compose them.

Towards this end, we created gnmi-gateway, which we have released as an open source project. …


Netflix is known for its loosely coupled and highly scalable microservice architecture. Independent services allow for evolving at different paces and scaling independently. Yet they add complexity for use cases that span multiple services. Rather than exposing 100s of microservices to UI developers, Netflix offers a unified API aggregation layer at the edge.

UI developers love the simplicity of working with one conceptual API for a large domain. Back-end developers love the decoupling and resilience offered by the API layer. But as our business has scaled, our ability to innovate rapidly has approached an invisible asymptote. …

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