Notes about Cutting-Edge Technologies and Everything

Netflix has more than 195 million subscribers that generate petabytes of data everyday. Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. Usually Data scientists and engineers write Extract-Transform-Load (ETL) jobs and pipelines using big data compute technologies, like Spark or Presto, to process this data and periodically compute key information for a member or a video. The processed data is typically stored as data warehouse tables in AWS S3. Iceberg is widely adopted in Netflix as a data warehouse table format that addresses many of the usability and performance problems with Hive tables. At Netflix, we also heavily embrace a microservice architecture that emphasizes separation of concerns. Many of these services often have the requirement to do a fast lookup for this fine-grained data which is generated periodically. For example, in order to enha...

As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there. Open Connect Open Connect is Netflix’s content delivery network (CDN). An easy, though imprecise, way of thinking about Netflix infrastructure is that everything that happens before you press Play on your remote control (e.g., are you logged in? what plan do you have? what have you watched so we can recommend new titles to you? what do you want to watch?) takes place in Amazon Web Services (AWS), whereas everything that happens after...

Netflix Data Landscape Freedom & Responsibility (F&R) is the lynchpin of Netflix’s culture empowering teams to move fast to deliver on innovation and operate with freedom to satisfy their mission. Central engineering teams provide paved paths (secure, vetted and supported options) and guard rails to help reduce variance in choices available for tools and technologies to support the development of scalable technical architectures. Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management. Therefore, building a complete and accurate data lineage system to map out all the data-artifacts (including in-motion and at-rest data repositories, Kafka topics, apps, reports and dashboards, interactive and ad-hoc analysis queries, ML and experimentation models) is a monumental task and requires a scalable architecture, robust design, a strong engineering team and above all, amazing cross-f...

For Netflix to be successful, we have to be vigilant in supporting the tens of millions of connected devices that are used by our 40+ million members throughout 40+ countries. These members consume more than one billion hours of content every month and account for nearly a third of the downstream Internet traffic in North America during peak hour From an operational perspective, our system environments at Netflix are large, complex, and highly distributed. And at our scale, humans cannot continuously monitor the status of all of our systems. To maintain high availability across such a complicated system, and to help us continuously improve the experience for our customers, it is critical for us to have exceptional tools coupled with intelligent analysis to proactively detect and communicate system faults and identify areas of improvement. In this post, we will talk about our plans to build a new set of insight tools and systems that create greater visibility int...

Netflix has open-sourced its visualization tool for exploring variance, perturbations, single-threaded execution, application startup, and other time-based data as flame graphs. Details >>

In some recent blog posts, we’ve explained why we believe it’s crucial to adopt a four‑tier application architecture in which applications are developed and deployed as sets of microservices . It’s becoming increasingly clear that if you keep using development processes and application architectures that worked just fine ten years ago, you simply can’t move fast enough to capture and hold the interest of mobile users who can choose from an ever‑growing number of apps. Switching to a microservices architecture creates exciting opportunities in the marketplace for companies. For system architects and developers, it promises an unprecedented level of control and speed as they deliver innovative new web experiences to customers. But at such a breathless pace, it can feel like there’s not a lot of room for error. In the real world, you can’t stop developing and deploying your apps as you retool the processes for doing so. You know that your future success depends on transitio...