How does Netflix build code before it’s deployed to the cloud? While pieces of this story have been told in the past, we decided it was time we shared more details. In this post, we describe the tools and techniques used to go from source code to a deployed service serving movies and TV shows to more than 75 million global Netflix members. The above diagram expands on a previous post announcing Sp
The localization program at Netflix is centered around linguistic excellence, a great team environment, and cutting-edge technology. The program is only 4 years old, which for a company our size is unusual to find. We’ve built a team and toolset representative of the scope and scale that a localization team needs to operate at in 2015, not one that is bogged down with years of legacy process and t
私はここ1週間ほど、同僚の David の一言で Infrastructure as Code について頭が大混乱状態でした。 それは次の一言です。 Chef や Puppet は大体の部分は Infrastructure as Code じゃないよね。ARM (Azure Resource Manager) はそうだけど。 ただ、Chef-Provisioning は Infrastructure as Code だよね。 もう頭が大混乱です。なんとなく言わんとしていることはわかりますが、私は今まで Chef とか、Puppet とか、Ansible とかで やっているようなことが、Infrastructure as Code と思い込んでいましたが、何か間違っていたのでしょうか?そういえば、 Chef はConfiguration Management Toolと紹介されていたなとか頭
In the billing migration blog post published a few weeks ago, we explained the overall approach employed in migrating our billing system to the cloud: In this post, the database migration portion will be covered in detail. We hope that our experiences will help you as you undertake your own migrations. Have you ever wondered about the elements that need to come together and align to get a complica
Previously we wrote about our traffic intuition tool, Flux. We have some announcements and updates to share about this project. First, we have renamed the project to Vizceral. More importantly, Vizceral is now open source! Open SourceVizceral transformed the way we understand and digest information about the state of traffic flowing into the Netflix control plane. We wanted to be able to intuit de
Early Bird Registration Deadline: March 16, 2016 SREcon16 is SOLD OUT. No walkup registrations will be accepted. Venue: Hyatt Regency Santa Clara 5101 Great America Pkwy Santa Clara, CA 95054 Rooms at the Hyatt Regency Santa Clara are sold out. Rooms available at: Biltmore Hotel & Suites 2151 Laurelwood Road Santa Clara, CA 95054 Book your room for $225 single or double plus tax or call (800) 255-
Repeatable builds Going back to a previous commit of your code and building it from source doesn’t guarantee exactly the same result. Your transitive dependency graph can change in subtle ways, even if you are careful to pin your dependencies. Nebula can help you lock your resolved dependency graph into source control quickly and easily. Immutable deployments If you’ve built an app and need to ins
Who should use Spinnaker?Spinnaker provides application management and deployment to help you release software changes with high velocity and confidence. Spinnaker is an open-source, multi-cloud continuous delivery platform that combines a powerful and flexible pipeline management system with integrations to the major cloud providers. If you are looking to standardize your release processes and im
At Netflix, our goal is to predict what you want to watch before you watch it. To do this, we run a large number of machine learning (ML) workflows every day. In order to support the creation of these workflows and make efficient use of resources, we created Meson. Meson is a general purpose workflow orchestration and scheduling framework that we built to manage ML pipelines that execute workloads
(BDT303) Running Spark and Presto on the Netflix Big Data Platform In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage
by Alex Chen, Justin Basilico, and Xavier Amatriain As we have described previously on this blog, at Netflix we are constantly innovating by looking for better ways to find the best movies and TV shows for our members. When a new algorithmic technique such as Deep Learning shows promising results in other domains (e.g. Image Recognition, Neuro-imaging, Language Models, and Speech Recognition), it
Ever wonder how Netflix serves a great streaming experience with high-quality video and minimal playback interruptions? Thank the team of engineers and data scientists who constantly A/B test their innovations to our adaptive streaming and content delivery network algorithms. What about more obvious changes, such as the complete redesign of our UI layout or our new personalized homepage? Yes, all
The Netflix Way to deal with Big Data Problems 1. The way to deal with Big data problems Monal Daxini March 2016 2. Monal Daxini Real Time Data Infrastructure Senior Software Engineer, Netflix https://www.linkedin.com/in/monaldaxini @monaldax #Netflix #Keystone 3. We help Produce, Store, Process, Move Events @ scale 4. Tell me more... ● Big Data Ecosystem @ Netflix ● How we built a scalable event
Java mixed-mode flame graphs provide a complete visualization of CPU usage and have just been made possible by a new JDK option: -XX:+PreserveFramePointer. We’ve been developing these at Netflix for everyday Java performance analysis as they can identify all CPU consumers and issues, including those that are hidden from other profilers. ExampleThis shows CPU consumption by a Java process, both use
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