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关联话题: 奈飞、网飞

Netflix(/ˈnɛtflɪks/)(官方中文译名网飞,非官方中文译名奈飞)是起源于美国、在世界各地提供网络视频点播的OTT服务公司,并同时在美国经营单一费率邮寄影像光盘出租服务,后者是使用回邮信封寄送DVD和Blu-ray出租光盘至消费者指定的收件地址。公司由里德·哈斯廷斯和马克·兰多夫在1997年8月29日成立,总部位于加利福尼亚州的洛斯盖图,1999年开始推出订阅制的服务。2009年,Netflix已可提供超过10万部电影DVD,订阅者数超过1000万人。另一方面,截至2022年6月的数据,Netflix的流服务已经在全球拥有2.20亿个订阅用户,在美国的订户已达到7330万。其主要的竞争对手有Disney+、Hulu、HBO Max、Amazon Prime Video、YouTube Premium及Apple TV+等。


Safe Updates of Client Applications at Netflix

Quality of a client application is of paramount importance to global digital products, as it is the primary way customers interact with a brand. At Netflix, we have significant investments in ensuring new versions of our applications are well tested. However, Netflix is available for streaming on thousands of types of devices and it is powered by hundreds of micro-services which are deployed independently, making it extremely challenging to comprehensively test internally. Hence, it became important to supplement our release decisions with strong evidence received from the field during the update process.

Our team was formed to mine health signals from the field to quickly evaluate new versions of the client applications. As we invested in systems to enable this vision, it led to increased development velocity, which arguably led to better development practices and quality of the applications. The goal of this blog post is to highlight the investment areas for this vision and the challenges we are facing today.

How We Build Micro Frontends With Lattice

As we continue to grow here at Netflix, the needs of Revenue and Growth Engineering are rapidly evolving; and our tools must also evolve just as rapidly. The Revenue and Growth Tools (RGT) team decided to set off on a journey to build tools in an abstract manner to have solutions readily available within our organization. We identified common design patterns and architectures scattered across various tools which were all duplicating efforts in some way or another.

We needed to consolidate these tools in a way that scaled with the teams we served. It needed to have the agility of a micro frontend and the extensibility of a framework to empower our stakeholders to extend our tools. We would abstract parts of which anyone can then customize, or extend, to meet their specific business or technical requirements. The end result is Lattice: RGT’s pluggable framework for micro frontends.

Netflix Cloud Packaging in the Terabyte Era

Behind the scenes of the beloved Netflix streaming service and content, there are many technology innovations in media processing. Packaging has always been an important step in media processing. After content ingestion, inspection and encoding, the packaging step encapsulates encoded video and audio in codec agnostic container formats and provides features such as audio video synchronization, random access and DRM protection. Our previous tech blog Packaging award-winning shows with award-winning technology detailed our packaging technology deployed on the streaming side.

Decision Making at Netflix

This introduction is the first in a multi-part series on how Netflix uses A/B tests to make decisions that continuously improve our products, so we can deliver more joy and satisfaction to our members. Subsequent posts will cover the basic statistical concepts underpinning A/B tests, the role of experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix.

What is an A/B Test?

This is the second post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. See here for Part 1: Decision Making at Netflix. Subsequent posts will go into more details on the statistics of A/B tests, experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix.

Practical API Design at Netflix, Part 2: Protobuf FieldMask for Mutation Operations

In our previous post, we discussed how we utilize FieldMask as a solution when designing our APIs so that consumers can request the data they need when fetched via gRPC. In this blog post we will continue to cover how Netflix Studio Engineering uses FieldMask for mutation operations such as update and remove.

Securing Netflix Studios At Scale Netflix TechBlog

A Journey About Productizing Security.

Practical API Design at Netflix, Part 1: Using Protobuf FieldMask

At Netflix, we heavily use gRPC for the purpose of backend to backend communication. When we process a request it is often beneficial to know which fields the caller is interested in and which ones they ignore. Some response fields can be expensive to compute, some fields can require remote calls to other services. Remote calls are never free; they impose extra latency, increase probability of an error, and consume network bandwidth. How can we understand which fields the caller doesn’t need to be supplied in the response, so we can avoid making unnecessary computations and remove calls? With GraphQL this comes out of the box through the use of field selectors. In the JSON:API standard a similar technique is known as Sparse Fieldsets. How can we achieve a similar functionality when designing our gRPC APIs? The solution we use within the Netflix Studio Engineering is protobuf FieldMask.

Towards a Reliable Device Management Platform

At Netflix, hundreds of different device types, from streaming sticks to smart TVs, are tested every day through automation to ensure that new software releases continue to deliver the quality of the Netflix experience that our customers enjoy. In addition, Netflix continuously works with its partners (such as Roku, Samsung, LG, Amazon) to port the Netflix SDK to their new and upcoming devices (TVs, smart boxes, etc), to ensure the quality bar is reached before allowing the Netflix application on the device to go out into the world. The Partner Infrastructure team at Netflix provides solutions to support these two significant efforts by enabling device management at scale.





Data Movement in Netflix Studio via Data Mesh

Data Mesh is a fully managed, streaming data pipeline product used for enabling Change Data Capture (CDC) use cases. In Data Mesh, users create sources and construct pipelines. Sources mimic the state of an externally managed source — as changes occur in the external source, corresponding CDC messages are produced to the Data Mesh source. Pipelines can be configured to transform and store data to externally managed sinks.

Data Mesh provides a drag-and-drop, self-service user interface for exploring sources and creating pipelines so that users can focus on delivering business value without having to worry about managing and scaling complex data streaming infrastructure.



(1)直接销售模式(Direct Sales)

(2)广告支持模式(Advertising Supported)

(3)价值分享模式(Shared Value)

Achieving observability in async workflows


Netflix Drive

在这篇文章中,我们将介绍Netflix Drive,一个用于媒体资产的云驱动器,并对它的一些功能和界面做一个高度概括。我们打算把这篇文章作为介绍Netflix Drive的系列文章中的第一篇文章。在未来的文章中,我们将对Netflix Drive的几个组件进行架构上的深入研究。

Production Media Management: Transforming Media Workflows by leveraging the Cloud

在这篇文章中,我们将分享Netflix如何提供技术和基础设施,帮助制作人员在制作和后期制作阶段创建和交换媒体的幕后故事。我们还将介绍我们的 Studio Engineering 工作如何通过利用我们基于云的服务,帮助 Netflix 制作人员在媒体物流上花费更少的时间。

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