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By: Rajiv Shringi, Oleksii Tkachuk, Kartik Sathyanarayanan IntroductionIn our previous blog post, we introduced Netflix’s TimeSeries Abstraction, a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction. This counting service, built on top of the TimeSeries Abstraction,
By Rajiv Shringi, Vinay Chella, Kaidan Fullerton, Oleksii Tkachuk, Joey Lynch IntroductionAs Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming, the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital. In previous blog posts, we introduced the Key-Value Data
By Jose Fernandez, Sebastien Dabdoub, Jason Koch, Artem Tkachuk The Compute and Performance Engineering teams at Netflix regularly investigate performance issues in our multi-tenant environment. The first step is determining whether the problem originates from the application or the underlying infrastructure. One issue that often complicates this process is the "noisy neighbor" problem. On Titus,
By Karthik Yagna, Baskar Odayarkoil, and Alex Ellis Pushy is Netflix’s WebSocket server that maintains persistent WebSocket connections with devices running the Netflix application. This allows data to be sent to the device from backend services on demand, without the need for continually polling requests from the device. Over the last few years, Pushy has seen tremendous growth, evolving from its
By Vadim Filanovsky, Mike Huang, Danny Thomas and Martin Chalupa IntroNetflix has an extensive history of using Java as our primary programming language across our vast fleet of microservices. As we pick up newer versions of Java, our JVM Ecosystem team seeks out new language features that can improve the ergonomics and performance of our systems. In a recent article, we detailed how our workloads
By Jun He, Natallia Dzenisenka, Praneeth Yenugutala, Yingyi Zhang, and Anjali Norwood TL;DRWe are thrilled to announce that the Maestro source code is now open to the public! Please visit the Maestro GitHub repository to get started. If you find it useful, please give us a star. What is MaestroMaestro is a horizontally scalable workflow orchestrator designed to manage large-scale Data/ML workflows
Anirudh Mendiratta, Kevin Wang, Joey Lynch, Javier Fernandez-Ivern, Benjamin Fedorka IntroductionIn November 2020, we introduced the concept of prioritized load shedding at the API gateway level in our blog post, Keeping Netflix Reliable Using Prioritized Load Shedding. Today, we’re excited to dive deeper into how we’ve extended this strategy to the individual service level, focusing on the video
Hechao Li, Roger Cruz Cloud Networking TopologyNetflix operates a highly efficient cloud computing infrastructure that supports a wide array of applications essential for our SVOD (Subscription Video on Demand), live streaming and gaming services. Utilizing Amazon AWS, our infrastructure is hosted across multiple geographic regions worldwide. This global distribution allows our applications to del
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
Democratizing Stream Processing @ Netflix By Guil Pires, Mark Cho, Mingliang Liu, Sujay Jain Data powers much of what we do at Netflix. On the Data Platform team, we build the infrastructure used across the company to process data at scale. In our last blog post, we introduced “Data Mesh” — A Data Movement and Processing Platform. When a user wants to leverage Data Mesh to move and transform data,
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
By Bo Lei, Guilherme Pires, James Shao, Kasturi Chatterjee, Sujay Jain, Vlad Sydorenko BackgroundRealtime processing technologies (A.K.A stream processing) is one of the key factors that enable Netflix to maintain its leading position in the competition of entertaining our users. Our previous generation of streaming pipeline solution Keystone has a proven track record of serving multiple of our ke
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
In 2017, Netflix Studios was hitting an inflection point from a period of merely rapid growth to the sort of explosive growth that throws “how do we scale?” into every conversation. The vision was to create a “Studio in the Cloud”, with applications supporting every part of the business from pitch to play. The security team was working diligently to support this effort, faced with two apparently c
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
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