Bringing Rich Experiences to Memory-constrained TV Devices

Netflix has over 148M paid members streaming on more than half a billion devices spanning over 1,900 different types. In the TV space alone, there are hundreds of device types that run the Netflix app. We need to support the same rich Netflix…

The End of Video Coding?

In the IEEE Signal Processing Magazine issue November 2006 article “Future of Video Coding and Transmission” Prof. Edward Delp started by asking the panelists “Is video coding dead? Some feel that, with the higher coding efficiency of the H.264/MPEG-4 . . . perhaps there is not much more to do…

Re-Architecting the Video Gatekeeper

This is the story about how the Content Setup Engineering team used Hollow, a Netflix OSS technology, to re-architect and simplify an essential component in our content pipeline — delivering a large amount of business value in the process. The Context Each movie and show on the Netflix service is carefully curated…

Netflix at Spark+AI Summit 2018

A glimpse at Spark usage for Netflix Recommendations Apache Spark has been an immensely popular big data platform for distributed computing. Netflix has been using Spark extensively for various batch and stream-processed workloads. A substantial list of use cases for Spark computation come from the various applications in the domain of content recommendations and personalization…

Hawkins: Diving into the Reasoning Behind our Design System

by Hawkins team member Joshua Godi; with cover art from Martin Bekerman and additional imagery from Wiki Chaves Hawkins may be the name of a fictional town in Indiana, most widely known as the backdrop for one of Netflix’s most popular TV series “Stranger Things,” but the name is so…

Netflix OSS and Spring Boot — Coming Full Circle

In 2007, Netflix started on a long road towards fully operating in the cloud. Much of Netflix’s backend and mid-tier applications are built using Java, and as part of this effort Netflix engineering built several cloud infrastructure libraries and systems — Ribbon for load balancing, Eureka for service discovery, and…

Part 2: Scheduling Notebooks at Netflix

At Netflix we’ve put substantial effort into adopting notebooks as an integrated development platform. The idea started as a discussion of what development and collaboration interfaces might look like in the future. It evolved into a strategic bet on notebooks, both as…

Enhancing the Netflix UI Experience with HDR

Some of you have probably heard about HDR, or High Dynamic Range, which can produce a higher level of brightness, a wider range of colors, and an increased level of detail and accuracy that empowers visual storytellers to deliver more impactful experiences for…

Byte Down: Making Netflix’s Data Infrastructure Cost-Effective

Background on data efficiency At Netflix, we invest heavily in our data infrastructure which is composed of dozens of data platforms, hundreds of data producers and consumers, and petabytes of data. At many other organizations, an effective way to manage data infrastructure costs is to…

MezzFS — Mounting object storage in Netflix’s media processing platform

MezzFS (short for “Mezzanine File System”) is a tool we’ve developed at Netflix that mounts cloud objects as local files via FUSE. It’s used extensively in our media processing platform, which includes services like Archer and runs features like…

Optimized shot-based encodes for 4K: Now streaming!

Netflix has an ever-expanding collection of titles which customers can enjoy in 4K resolution with a suitable device and subscription plan. Netflix creates premium bitstreams for those titles in addition to the catalog-wide 8-bit stream profiles¹. Premium features comprise a…

Keystone Real-time Stream Processing Platform

Computational Causal Inference at Netflix

Every Netflix data scientist, whether their background is from biology, psychology, physics, economics, math, statistics, or biostatistics, has made meaningful contributions to the way Netflix analyzes causal effects. Scientists from these fields have made many advancements in causal effects research in the past few decades, spanning…

Achieving observability in async workflows

Managing and operating asynchronous workflows can be difficult without the proper tools and architecture that puts observability, debugging, and tracing at the forefront. Imagine getting paged outside normal work hours — users are having trouble with the application you’re responsible for…

Open-Sourcing riskquant, a library for quantifying risk

Cache warming: Agility for a stateful service

EVCache has been a fundamental part of the Netflix platform (we call it Tier-1), holding Petabytes of data. Our caching layer serves multiple use cases from signup, personalization, searching, playback, and more. It is comprised of thousands of nodes in…

Android Rx onError Guidelines

“Creating a good API is hard.” — anyone who has created an API used by others As with any API, wrapping your data stream in a Rx observable requires consideration for reasonable error handling and intuitive behavior. The following guidelines are intended to help developers create consistent…

Data Engineers of Netflix — Interview with Samuel Setegne

This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Samuel Setegne is a Senior Software Engineer on the Core Data Science and Engineering team. Samuel and his team build tools and frameworks…

Auto Scaling Production Services on Titus

Over the past three years, Netflix has been investing in container technology. A large part of this investment has been around Titus, Netflix’s container management platform that was open sourced in April of 2018. Titus schedules application containers…

Evolution of Netflix Conductor:

By Anoop Panicker and Kishore Banala Conductor is a workflow orchestration engine developed and open-sourced by Netflix. If you’re new to Conductor, this earlier blogpost and the documentation should help you get started and acclimatized to Conductor. Netflix Conductor: A microservices orchestrator visibility into distributed workflowsmedium.com In the last two years since inception, Conductor has seen wide adoption and…

Streaming Video Experimentation at Netflix: Visualizing Practical and Statistical Significance

Streaming video experimentation at Netflix seeks to optimize the Quality of Experience (QoE for short) of the videos we stream to our 130 million members around the world. To measure QoE, we look at a wide variety of metrics for each playback session, including play delay; the…

Empowering the Visual Effects Community with the NetFX Platform

At Netflix, we want to entertain our global membership with series and films from around the world. In line with that, we’re excited to announce NetFX, a cloud-based platform that will make it easier for…

ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

Infrastructure for Contextual Bandits and Reinforcement Learning — theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches…

Building a Rule-Based Platform to Manage Netflix Membership SKUs at Scale

At Netflix, we aspire to entertain the world. From mailing DVDs in the US to a global streaming service with over 200 million subscribers across 190 countries, we have come a long way. For the longest time, Netflix had three plans (basic/standard/premium)…

Predictive CPU isolation of containers at Netflix

Noisy Neighbors We’ve all had noisy neighbors at one point in our life. Whether it’s at a cafe or through a wall of an apartment, it is always disruptive. The need for good manners in shared spaces turns out to be important not just for people, but for your Docker containers too. …