Automated Failure Testing
At Netflix, we have found that proactive failure testing is a great way to ensure that we have a reliable product for our members by helping us prepare our systems, and our teams, for the problems that arise in our production environment. Our various efforts in this space, some of which are manual, have helped us make it through the holiday season without incident (which is great if you’re on-call
Global Continuous Delivery with Spinnaker
After over a year of development and production use at Netflix, we’re excited to announce that our Continuous Delivery platform, Spinnaker, is available on GitHub. Spinnaker is an open source multi-cloud Continuous Delivery platform for releasing software changes with high velocity and confidence. Spinnaker is designed with pluggability in mind; the platform aims to make it easy to extend and enha
Netflix at Velocity 2015: Linux Performance Tools
There are many performance tools nowadays for Linux, but how do they all fit together, and when do we use them? At Velocity 2015, I gave a 90 minute tutorial on Linux performance tools. I’ve spoken on this topic before, but given a 90 minute time slot I was able to include more methodologies, tools, and live demonstrations, making it the most complete tour of the topic I’ve done. The video and sli
NetflixのgithubリポジトリはWeb技術の百貨店だった - DiaryException
検索しているとなにかとNetflixのgithubリポジトリがヒットするので、全部（2015/07/18現在分）調査してみた。 github APIで https://github.com/Netflix のリストを全部取得して、名前・概要・URL・最終更新日時 (なんの更新だ?) を抽出。 AWS用のプロダクトが多かったのでまずそれらと、その他という分類にした。その他はほとんどがJavaライブラリ・システムだが、一部WebアプリケーションやPythonライブラリがある。 日本語での説明はReadmeやWikiを見て書いているが、理解が正しくないかもしれない。 AWS用 aws-autoscaling Tools and Documentation about using Auto Scaling URL: https://github.com/Netflix/aws-autoscalin
Introducing Vector
Vector is an open source host-level performance monitoring framework, which exposes hand-picked, high-resolution system and application metrics to every engineer’s browser. Having the right metrics available on demand and at a high resolution is key to understanding how a system behaves and correctly troubleshooting performance issues. Previously, we’d login to instances as needed, run a variety o
Introducing Dynomite
Introduction & OverviewNetflix has long been a proponent of the microservices model. This model offers higher-availability, resiliency to failure and loose coupling. The downside to such an architecture is the potential for a latent user experience. Every time a customer loads up a homepage or starts to stream a movie, there are a number of microservices involved to complete that request. Most of
Big Data Pipeline and Analytics Platform Using NetflixOSS and Other Open Source Libraries
Big Data Pipeline and Analytics Platform Using NetflixOSS and Other Open Source Libraries Netflix collects over 100 billion events per day from over 1000 device types and 500 apps/services. They built a big data pipeline using open source tools like NetflixOSS, Hadoop, Druid, Elasticsearch, and RxJava to ingest, process, store, and query this data in real-time and perform tasks like intelligent al
S3mper
In previous posts, we discussed how the Hadoop platform at Netflix leverages AWS’s S3 offering (read more here). In short, Netflix considers S3 the “source of truth” for all data warehousing. There are many attractive features that draw us to this service including: 99.999999999% durability, 99.99% availability, effectively infinite storage, versioning (data recovery), and ubiquitous access. In co
Netflix running Presto in the AWS Cloud
Netflix runs Presto in its AWS cloud environment to enable low-latency ad-hoc queries on petabyte-scale data stored in S3. Some key things Netflix did include optimizing Presto to read from and write directly to S3, fixing bugs, integrating Presto with its EMR and Ganglia monitoring, and deploying a 100+ node Presto cluster that handles over 1000 queries per day. Performance testing showed Presto
Distributed Neural Networks with GPUs in the AWS Cloud
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
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