When you enjoy the latest season of Stranger Things or Casa de Papel (Money Heist), have you ever wondered about the secrets to fantastic story-telling, besides the stunning visual presentation? From the violin melody accompanying a pivotal scene to the soaring orchestral arrangement and thunderous sound-effects propelling an edge-of-your-seat action sequence, the various components of the audio soundtrack combine to evoke the very essence of story-telling. To uncover the magic of audio soundtracks and further improve the sonic experience, we need a way to systematically examine the interaction of these components, typically categorized as dialogue, music and effects.

In this blog post, we will introduce speech and music detection as an enabling technology for a variety of audio applications in Film & TV, as well as introduce our speech and music activity detection (SMAD) system which we recently published as a journal article in EURASIP Journal on Audio, Speech, and Music Processing.

At Netflix, we strive to give our members an excellent personalized experience, helping them make the most successful and satisfying selections from our thousands of titles. We already personalize artwork and trailers, but we hadn’t yet personalized sizzle reels — until now.

A sizzle reel is a montage of video clips from different titles strung together into a seamless A/V asset that gets members excited about upcoming launches (for example, our Emmys nominations or holiday collections). Now Netflix can create a personalized sizzle reel dynamically in real time and on demand. The order of the clips and included titles are personalized per member, giving each a unique and effective experience. These new personalized reels are called Dynamic Sizzles.

In this post, we will dive into the exciting details of how we create Dynamic Sizzles with minimal human intervention, including the challenges we faced and the solutions we developed.

Empowering video editors with multimodal machine learning to discover perfect moments across the entire Netflix catalog.

Data powers much of what we do at Netflix. On the Data Platform team, we build the infrastructure used across the company to process data at scale.

In our last blog post, we introduced “Data Mesh” — A Data Movement and Processing Platform. When a user wants to leverage Data Mesh to move and transform data, they start by creating a new Data Mesh pipeline. The pipeline is composed of individual “Processors” that are connected by Kafka topics. The Processors themselves are implemented as Flink jobs that use the DataStream API.

Since then, we have seen many use cases (including Netflix Graph Search) adopt Data Mesh for stream processing. We were able to onboard many of these use cases by offering some commonly used Processors out of the box, such as Projection, Filtering, Unioning, and Field Renaming.

Netflix’s service mesh adoption: history, motivations, and how we worked with the Envoy community on a feature to streamline mesh adoption.

At Netflix, we have created millions of artwork to represent our titles. Each artwork tells a story about the title it represents. From our testing on promotional assets, we know which of these assets have performed well and which ones haven’t. Through this, our teams have developed an intuition of what visual and thematic artwork characteristics work well for what genres of titles. A piece of promotional artwork may resonate more in certain regions, for certain genres, or for fans of particular talent. The complexity of these factors makes it difficult to determine the best creative strategy for upcoming titles.

Our assets are often created by selecting static image frames directly from our source videos. To improve it, we decided to invest in creating a Media Understanding Platform, which enables us to extract meaningful insights from media that we can then surface in our creative tools. In this post, we will take a deeper look into one of these tools, AVA Discovery View.

When Zuul was designed and developed, there was an inherent assumption that connections were effectively free, given we weren’t using mutual TLS (mTLS). It’s built on top of Netty, using event loops for non-blocking execution of requests, one loop per core. To reduce contention among event loops, we created connection pools for each, keeping them completely independent. The result is that the entire request-response cycle happens on the same thread, significantly reducing context switching.

There is also a significant downside. It means that if each event loop has a connection pool that connects to every origin (our name for backend) server, there would be a multiplication of event loops by servers by Zuul instances. For example, a 16-core box connecting to an 800-server origin would have 12,800 connections. If the Zuul cluster has 100 instances, that’s 1,280,000 connections. That’s a significant amount and certainly more than is necessary relative to the traffic on most clusters.

As streaming has grown over the years, these numbers multiplied with bigger Zuul and origin clusters. More acutely, if a traffic spike occurs and Zuul instances scale up, it exponentially increases connections open to origins. Although this has been a known issue for a long time, it has never been a critical pain point until we moved large streaming applications to mTLS and our Envoy-based service mesh.

本文提出了两种基于场景边界检测的方法，利用场景文本、音频和视频等多种模态信息。第一种方法使用预训练的句子级嵌入和动态时间规整方法在场景文本与时间戳文本的对齐信息上确定场景边界；第二种方法使用已标注的场景变化数据训练双向GRU序列模型，并利用预训练的多模态镜头嵌入来丰富模型。实验结果表明，这些方法在视频摘要、内容检索、配音质量评估和视频编辑等任务上的效果与最先进方法相当甚至更好。作者还提出了将场景边界检测与场景分类和关键时刻识别等任务相结合，进一步提高多模态机器学习模型在长形式内容中的表现。

Picture yourself enthralled by the latest episode of your beloved Netflix series, delighting in an uninterrupted, high-definition streaming experience. Behind these perfect moments of entertainment is a complex mechanism, with numerous gears and cogs working in harmony. But what happens when this machinery needs a transformation? This is where large-scale system migrations come into play. Our previous blog post presented replay traffic testing — a crucial instrument in our toolkit that allows us to implement these transformations with precision and reliability.

In 2022, a major change was made to Netflix’s iOS and Android applications. We migrated Netflix’s mobile apps to GraphQL with zero downtime, which involved a total overhaul from the client to the API layer.

Until recently, an internal API framework, Falcor, powered our mobile apps. They are now backed by Federated GraphQL, a distributed approach to APIs where domain teams can independently manage and own specific sections of the API.

Doing this safely for 100s of millions of customers without disruption is exceptionally challenging, especially considering the many dimensions of change involved. This blog post will share broadly-applicable techniques (beyond GraphQL) we used to perform this migration. The three strategies we will discuss today are AB Testing, Replay Testing, and Sticky Canaries.

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.

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

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.

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!

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.

Netflix采用基于实时流式度量的告警方式，用于处理高维度/基数限制的时间序列数据库。这种方法可以支持更多的用例，提高系统的可靠性，避免误报和漏报。另外，流式评估可以放松高基数限制，监控并警报一些高基数用例，如从自由形式日志数据中派生的指标。Netflix还将其应用健康监控系统Telltale从轮询指标缓存转换为实时Atlas流式处理，以检测异常并计算相关性，减少故障恢复平均时间（MTTR）。此外，Netflix发现流式度量方法不受监视查询数量的限制，且支持更高的度量维度/基数，这为更精确的警报和更好的可观察性提供了机会。