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By Jose Fernandez Today, we are thrilled to announce the release of bpftop, a command-line tool designed to streamline the performance optimization and monitoring of eBPF programs. As Netflix increasingly adopts eBPF [1, 2], applying the same rigor to these applications as we do to other managed services is imperative. Striking a balance between eBPF’s benefits and system load is crucial, ensuring
by Moshe Kolodny In this post, we’re excited to introduce SafeTest, a revolutionary library that offers a fresh perspective on End-To-End (E2E) tests for web-based User Interface (UI) applications. The Challenges of Traditional UI TestingTraditionally, UI tests have been conducted through either unit testing or integration testing (also referred to as End-To-End (E2E) testing). However, each of th
Liwei Guo, Anush Moorthy, Li-Heng Chen, Vinicius Carvalho, Aditya Mavlankar, Agata Opalach, Adithya Prakash, Kyle Swanson, Jessica Tweneboah, Subbu Venkatrav, Lishan Zhu This is the first blog in a multi-part series on how Netflix rebuilt its video processing pipeline with microservices, so we can maintain our rapid pace of innovation and continuously improve the system for member streaming and st
IntroductionEarlier this summer Netflix held our first-ever Data Engineering Forum. Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community! You can find each of the talks below with a short descri
By Abhinaya Shetty, Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. Many metrics in Netflix’s financial reports are powered and reconciled with efforts from our team! Given our role on this critical pat
How Netflix’s Container Platform Connects Linux Kernel Panics to Kubernetes Pods By Kyle Anderson With a recent effort to reduce customer (engineers, not end users) pain on our container platform Titus, I started investigating “orphaned” pods. There are pods that never got to finish and had to be garbage collected with no real satisfactory final status. Our Service job (think ReplicatSet) owners d
By Jennifer Shin, Tejas Shikhare, Will Emmanuel 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 A
Shyam Gala, Javier Fernandez-Ivern, Anup Rokkam Pratap, Devang Shah 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?
By Vadim Filanovsky and Harshad Sane In one of our previous blogposts, A Microscope on Microservices we outlined three broad domains of observability (or “levels of magnification,” as we referred to them) — Fleet-wide, Microservice and Instance. We described the tools and techniques we use to gain insight within each domain. There is, however, a class of problems that requires an even stronger lev
At Netflix, we want to entertain the world through creating engaging content and helping members discover the titles they will love. Key to that is understanding causal effects that connect changes we make in the product to indicators of member joy. To measure causal effects we rely heavily on AB testing, but we also leverage quasi-experimentation in cases where AB testing is limited. Many scienti
By Alex Hutter, Falguni Jhaveri and Senthil Sayeebaba Over the past few years Content Engineering at Netflix has been transitioning many of its services to use a federated GraphQL platform. GraphQL federation enables domain teams to independently build and operate their own Domain Graph Services (DGS) and, at the same time, connect their domain with other domains in a unified GraphQL schema expose
By: Ankush Gulati, David Gevorkyan Additional credits: Michael Clark, Gokhan Ozer IntroNetflix has more than 220 million active members who perform a variety of actions throughout each session, ranging from renaming a profile to watching a title. Reacting to these actions in near real-time to keep the experience consistent across devices is critical for ensuring an optimal member experience. This
Netflix is used by 222 million members and runs on over 1700 device types ranging from state-of-the-art smart TVs to low-cost mobile devices. At Netflix we’re proud of our reliability and we want to keep it that way. To that end, it’s important that we prevent significant performance regressions from reaching the production app. Sluggish scrolling or late rendering is frustrating and triggers acci
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Michael Lindon, and Colin McFarland This is the fifth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a look at Part 1 (Decision Making at Netflix), Part 2 (What is an A/B Test?), Part 3 (False positives and statistical significance), and
tl;dr Today, we are open-sourcing a long-awaited GUI for Metaflow. The Metaflow GUI allows data scientists to monitor their workflows in real-time, track experiments, and see detailed logs and results for every executed task. The GUI can be extended with plugins, allowing the community to build integrations to other systems, custom visualizations, and embed upcoming features of Metaflow directly i
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 vari
By Xiaomei Liu, Rosanna Lee, Cyril Concolato IntroductionBehind 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 p
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarland 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 in
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarland 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 a
By Alex Borysov, Ricky Gardiner BackgroundAt 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
By Andrew Nguonly, Armando Magalhães, Obi-Ike Nwoke, Shervin Afshar, Sreyashi Das, Tongliang Liu, Wei Liu, Yucheng Zeng BackgroundOver the next few years, most content on Netflix will come from Netflix’s own Studio. From the moment a Netflix film or series is pitched and long before it becomes available on Netflix, it goes through many phases. This happens at an unprecedented scale and introduces
By Burak Bacioglu, Meenakshi Jindal Asset Management at NetflixAt Netflix, all of our digital media assets (images, videos, text, etc.) are stored in secure storage layers. We built an asset management platform (AMP), codenamed Amsterdam, in order to easily organize and manage the metadata, schema, relations and permissions of these assets. It is also responsible for asset discovery, validation, s
By Alok Tiagi, Hariharan Ananthakrishnan, Ivan Porto Carrero and Keerti Lakshminarayan Netflix has developed a network observability sidecar called Flow Exporter that uses eBPF tracepoints to capture TCP flows at near real time. At much less than 1% of CPU and memory on the instance, this highly performant sidecar provides flow data at scale for network insight. ChallengesThe cloud network infrast
Written by Vikram Krishnamurthy, Kishore Kasi, Abhishek Kapatkar, Tejas Chopra, Prudhviraj Karumanchi, Kelsey Francis, Shailesh Birari In this post, we are introducing Netflix Drive, a Cloud drive for media assets and providing a high level overview of some of its features and interfaces. We intend this to be a first post in a series of posts covering Netflix Drive. In the future posts, we will do
ConsoleMe: A Central Control Plane for AWS Permissions and Access By Curtis Castrapel, Patrick Sanders, and Hee Won Kim At AWS re:Invent 2020, we open sourced two new tools for managing multi-account AWS permissions and access. We’re very excited to bring you ConsoleMe (pronounced: kuhn-soul-mee), and its CLI utility, Weep (pun intended)! If you missed the talk, check it out here. MotivationGrowth
by Frank San Miguel on behalf of the Cosmos team IntroductionCosmos is a computing platform that combines the best aspects of microservices with asynchronous workflows and serverless functions. Its sweet spot is applications that involve resource-intensive algorithms coordinated via complex, hierarchical workflows that last anywhere from minutes to years. It supports both high throughput services
by Dane Avilla The entertainment industry has struggled with COVID-19 restrictions impacting productions around the globe. Since early 2020, Netflix has been iteratively developing systems to provide internal stakeholders and business leaders with up-to-date tools and dashboards with the latest information on the pandemic. These software solutions allow executive leadership to make the most inform
by AIM Team Members Karen Casella, Travis Nelson, Sunny Singh; with prior art and contributions by Justin Ryan, Satyajit Thadeshwar As most developers can attest, dealing with security protocols and identity tokens, as well as user and device authentication, can be challenging. Imagine having multiple protocols, multiple tokens, 200M+ users, and thousands of device types, and the problem can explo
Stranger Things imagery showcasing the inspiration for the Hawkins Design Systemby 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 much more. Hawkins is the namesake t
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