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. …


How viewers are able to watch their favorite show on Netflix while the infrastructure self-recovers from a system failure

By Manuel Correa, Arthur Gonigberg, and Daniel West

Getting stuck in traffic is one of the most frustrating experiences for drivers around the world. Everyone slows to a crawl, sometimes for a minor issue or sometimes for no reason at all. As engineers at Netflix, we are constantly reevaluating how to redesign traffic management. What if we knew the urgency of each traveler and could selectively route cars through, rather than making everyone wait?

In Netflix engineering, we’re driven by ensuring Netflix is there when you need it to be. Yet, as recent as last year, our systems were susceptible to metaphorical traffic jams; we had on/off circuit breakers, but no progressive way to shed load. …


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

by Rocio Ruelas

Back when we were all working in offices, my favorite days were Monday, Wednesday, and Friday. Those were the days with the best breakfast items on the cafeteria menu! I started the day by arriving at the LA office right before 8am and finding a parking spot close to the entrance. I would greet the familiar faces at the reception desk and take a moment to check out which Netflix Original was currently being projected across the lobby. Take the elevator uninterrupted up to the top floor. Grab myself a plate of scrambled eggs, salsa, and bacon. Pour myself some coffee. …


By David Henry & Mel Yahya

Over the last few years Netflix has been developing a mobile app called Prodicle to innovate in the physical production of TV shows and movies. The world of physical production is fast-paced, and needs vary significantly between the country, region, and even from one production to the next. The nature of the work means we’re developing write-heavy software, in a distributed environment, on devices where less than ⅓ of our users have very reliable connectivity whilst on set, and with a limited margin for error. …


By Tianlong Chen and Ioannis Papapanagiotou

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.


by Maulik Pandey

Our Team — Kevin Lew, Narayanan Arunachalam, Elizabeth Carretto, Dustin Haffner, Andrei Ushakov, Seth Katz, Greg Burrell, Ram Vaithilingam, Mike Smith and Maulik Pandey

Image for post
Image for post

@Netflixhelps Why doesn’t Tiger King play on my phone?” — a Netflix member via Twitter

This is an example of a question our on-call engineers need to answer to help resolve a member issue — which is difficult when troubleshooting distributed systems. Investigating a video streaming failure consists of inspecting all aspects of a member account. In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. …


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

by Julie Beckley & Chris Pham

This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.

Image for post
Image for post
Photo from a team curling offsite — There’s us to the right!

[Chris] Julie and I joined the Streaming DSE team at Netflix a few years ago and have been close colleagues and friends since then. At work, we regularly lean on each other for help based on our respective areas of expertise — I bring my breadth of big data tools and technologies while Julie has been building statistical models for the past decade. …


An Introduction to Analytics and Visualization Engineering at Netflix

by Molly Jackman & Meghana Reddy

Image for post
Image for post
Explained: Season 1 (Photo Credit: Netflix)

Across nearly every industry, there is recognition that data analytics is key to driving informed business decision-making. But there is far less agreement on what that term “data analytics” actually means — or what to call the people responsible for the work.

Even within Netflix, we have many groups that do some form of data analysis, including business strategy and consumer insights. But here we are talking about Netflix’s Data Science and Engineering group, which specializes in analytics at scale. The group has technical, engineering-oriented roles that fall under two broad category titles: “Analytics Engineers” and “Visualization Engineers.” In this post, we refer to these two titles collectively as the “analytics role.” …


How we migrated our Android endpoints out of a monolith into a new microservice

by Rohan Dhruva, Ed Ballot

As Android developers, we usually have the luxury of treating our backends as magic boxes running in the cloud, faithfully returning us JSON. At Netflix, we have adopted the Backend for Frontend (BFF) pattern: instead of having one general purpose “backend API”, we have one backend per client (Android/iOS/TV/web). On the Android team, while most of our time is spent working on the app, we are also responsible for maintaining this backend that our app communicates with, and its orchestration code.

Recently, we completed a year-long project rearchitecting and decoupling our backend from the centralized model used previously. We did this migration without slowing down the usual cadence of our releases, and with particular care to avoid any negative effects to the user experience. We went from an essentially serverless model in a monolithic service, to deploying and maintaining a new microservice that hosted our app backend endpoints. This allowed Android engineers to have much more control and observability over how we get our data. Over the course of this post, we will talk about our approach to this migration, the strategies that we employed, and the tools we built to support this. …

About

Netflix Technology Blog

Learn more about how Netflix designs, builds, and operates our systems and engineering organizations

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store