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Ensuring the Successful Launch of Ads on Netflix

In November 2022, we introduced a brand new tier — Basic with ads. This tier extended existing infrastructure by adding new backend components and a new remote call to our ads partner on the playback path. As we were gearing up for launch, we wanted to ensure it would go as smoothly as possible. To do this, we devised a novel way to simulate the projected traffic weeks ahead of launch by building upon the traffic migration framework described here. We used this simulation to help us surface problems of scale and validate our Ads algorithms.

Native Frame Rate Playback

This article talks about a novel HDMI technology and how it is used within the Netflix Application to improve a user’s experience.

ABAC on SpiceDB: Enabling Netflix’s Complex Identity Types

The authorization team at Netflix recently sponsored work to add Attribute Based Access Control (ABAC) support to AuthZed’s open source Google Zanzibar inspired authorization system, SpiceDB. Netflix required attribute support in SpiceDB to support core Netflix application identity constructs. This post discusses why Netflix wanted ABAC support in SpiceDB, how Netflix collaborated with AuthZed, the end result–SpiceDB Caveats, and how Netflix may leverage this new feature.

Debugging a FUSE deadlock in the Linux kernel

The Compute team at Netflix is charged with managing all AWS and containerized workloads at Netflix, including autoscaling, deployment of containers, issue remediation, etc. As part of this team, I work on fixing strange things that users report.

This particular issue involved a custom internal FUSE filesystem: ndrive. It had been festering for some time, but needed someone to sit down and look at it in anger. This blog post describes how I poked at /procto get a sense of what was going on, before posting the issue to the kernel mailing list and getting schooled on how the kernel’s wait code actually works!

Migrating Critical Traffic At Scale with No Downtime — Part 1

Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience. Behind the scenes, a myriad of systems and services are involved in orchestrating the product experience. These backend systems are consistently being evolved and optimized to meet and exceed customer and product expectations.

When undertaking system migrations, one of the main challenges is establishing confidence and seamlessly transitioning the traffic to the upgraded architecture without adversely impacting the customer experience. This blog series will examine the tools, techniques, and strategies we have utilized to achieve this goal.

Improved Alerting with Atlas Streaming Eval


Building a Media Understanding Platform for ML Innovations

The media understanding platform serves as an abstraction layer between Machine Learning (ML) algos and various applications.

NTS: Reliable Device Testing at Scale

At Netflix, we test hundreds of different device types every day, ranging from streaming sticks to smart TVs, to ensure that new version releases of the Netflix SDK continue to provide the exceptional Netflix experience that our customers expect. We also collaborate with our Partners to integrate the Netflix SDK onto their upcoming new devices, such as TVs and set top boxes. This program, known as Partner Certification, is particularly important for the business because device expansion historically has been crucial for new Netflix subscription acquisitions. The Netflix Test Studio (NTS) platform was created to support Netflix SDK testing and Partner Certification by providing a consistent automation solution for both Netflix and Partner developers to deploy and execute tests on “Netflix Ready” devices.

Over the years, both Netflix SDK testing and Partner Certification have gradually transitioned upstream towards a shift-left testing strategy. This requires the automation infrastructure to support large-scale CI, which NTS was not originally designed for. NTS 2.0 addresses this very limitation of NTS, as it has been built by taking the learnings from NTS 1.0 to re-architect the system into a platform that significantly improves reliable device testing at scale while maintaining the NTS user experience.

Data Reprocessing Pipeline in Asset Management Platform @Netflix

At Netflix, we built the asset management platform (AMP) as a centralized service to organize, store and discover the digital media assets created during the movie production. Studio applications use this service to store their media assets, which then goes through an asset cycle of schema validation, versioning, access control, sharing, triggering configured workflows like inspection, proxy generation etc. This platform has evolved from supporting studio applications to data science applications, machine-learning applications to discover the assets metadata, and build various data facts.

During this evolution, quite often we receive requests to update the existing assets metadata or add new metadata for the new features added. This pattern grows over time when we need to access and update the existing assets metadata. Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case. This framework allowed us to evolve and adapt the application to any unpredictable inevitable changes requested by our platform clients without any downtime. Production assets operations are performed in parallel with older data reprocessing without any service downtime. Some of the common supported data reprocessing use cases are listed below.

Elasticsearch Indexing Strategy in Asset Management Platform (AMP)

At Netflix, all of our digital media assets (images, videos, text, etc.) are stored in secure storage layers. We built an asset management platform (AMP), codenamed Amsterdam, in order to easily organize and manage the metadata, schema, relations and permissions of these assets. It is also responsible for asset discovery, validation, sharing, and for triggering workflows.

Amsterdam service utilizes various solutions such as Cassandra, Kafka, Zookeeper, EvCache etc. In this blog, we will be focusing on how we utilize Elasticsearch for indexing and search the assets.

Data ingestion pipeline with Operation Management

At Netflix, to promote and recommend the content to users in the best possible way there are many Media Algorithm teams which work hand in hand with content creators and editors. Several of these algorithms aim to improve different manual workflows so that we show the personalized promotional image, trailer or the show to the user.

These media focused machine learning algorithms as well as other teams generate a lot of data from the media files, which we described in our previous blog, are stored as annotations in Marken. We designed a unique concept called Annotation Operations which allows teams to create data pipelines and easily write annotations without worrying about access patterns of their data from different applications.

Scaling Media Machine Learning at Netflix

We tackle some of the unique challenges of scaling multimodal machine learning models that operate on media assets (video, audio, and text).

Discovering Creative Insights in Promotional Artwork

When members are shown a title on Netflix, the displayed artwork, trailers, and synopses are personalized. That means members see the assets that are most likely to help them make an informed choice. These assets are a critical source of information for the member to make a decision to watch, or not watch, a title. The stories on Netflix are multidimensional and there are many ways that a single story could appeal to different members. We want to show members the images, trailers, and synopses that are most helpful to them for making a watch decision.

Scalable Annotation Service — Marken

In Marken (Scalable Annotation Service at Netflix), an annotation is a piece of metadata which can be attached to an object from any domain.

Ready-to-go sample data pipelines with Dataflow

This post is for all data practitioners, who are interested in learning about bootstrapping, standardization and automation of batch data pipelines at Netflix.

You may remember Dataflow from the post we wrote last year titled Data pipeline asset management with Dataflow. That article was a deep dive into one of the more technical aspects of Dataflow and didn’t properly introduce this tool in the first place. This time we’ll try to give justice to the intro and then we will focus on one of the very first features Dataflow came with. That feature is called sample workflows, but before we start in let’s have a quick look at Dataflow in general.

For your eyes only: improving Netflix video quality with neural networks

When you are binge-watching the latest season of Stranger Things or Ozark, we strive to deliver the best possible video quality to your eyes. To do so, we continuously push the boundaries of streaming video quality and leverage the best video technologies. For example, we invest in next-generation, royalty-free codecs and sophisticated video encoding optimizations. Recently, we added another powerful tool to our arsenal: neural networks for video downscaling. In this tech blog, we describe how we improved Netflix video quality with neural networks, the challenges we faced and what lies ahead.

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