Netflix Platform Engineering — we’re just getting started
“Aren’t you done with every interesting challenge already?†I get this question in various forms a lot. During interviews. At conferences, after we present on some of our technologies and practices. At meetups and social events. You have fully migrated to the Cloud, you must be done… You created multi-regional resiliency framework, you must be done… You launched globally, you must be done… You dep
Netflixã¯218å°ã®Cassandraノードå†èµ·å‹•ã«ã©ã†å¯¾å‡¦ã—ãŸã®ã‹
Spring Bootã«ã‚ˆã‚‹APIãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰æ§‹ç¯‰å®Ÿè·µã‚¬ã‚¤ãƒ‰ 第2版 何åƒäººã‚‚ã®é–‹ç™ºè€…ãŒã€InfoQã®ãƒŸãƒ‹ãƒ–ック「Practical Guide to Building an API Back End with Spring Bootã€ã‹ã‚‰ã€Spring Bootを使ã£ãŸREST API構築ã®åŸºç¤Žã‚’å¦ã‚“ã 。ã“ã®æœ¬ã§ã¯ã€å‡ºç‰ˆæ™‚ã«æ–°ã—ãリリースã•ã‚ŒãŸãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚ã‚‹ Spring Boot 2 を使用ã—ã¦ã„る。ã—ã‹ã—ã€Spring Boot3ãŒæœ€è¿‘リリースã•ã‚Œã€é‡è¦ãªå¤‰...
A Microscope on Microservices
by Coburn Watson, Scott Emmons, and Brendan Gregg At Netflix we pioneer new cloud architectures and technologies to operate at massive scale — a scale which breaks most monitoring and analysis tools. The challenge is not just handling a massive instance count but to also provide quick, actionable insight for a large-scale, microservice-based architecture. Out of necessity we’ve developed our own t
Evolution of the Netflix Data Pipeline
Our new Keystone data pipeline went live in December of 2015. In this article, we talk about the evolution of Netflix’s data pipeline over the years. This is the first of a series of articles about the new Keystone data pipeline. Netflix is a data-driven company. Many business and product decisions are based on insights derived from data analysis. The charter of the data pipeline is to collect, ag
Netflix and Containers - Titus
Slides from the 2016/01/28 Advanced Amazon Web Services (AWS) Meetup. Netflix overviewed the usage of containers at Netflix. We covered technologies we are working on in the runtime (Titus) and developer experience (Newt). We talked about how the Titus container management system was different from others and our journey with Docker, Mesos, Netflix Fenzo and eventually Amazon Elastic Container Ser
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
Netflixã®ãƒ¢ãƒ€ãƒ³ãªã‚¯ãƒ©ã‚¦ãƒ‰ãƒ™ãƒ¼ã‚¹ã®ãƒ—ラットフォーム- kuenishi's blog
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マイクãƒã‚µãƒ¼ãƒ“スã«ãŠã‘る障害㨠Failurewall - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? マイクãƒã‚µãƒ¼ãƒ“スã«ãŠã‘る障害㨠Failurewall ã“ã¡ã‚‰ã¯ Scala Advent Calendar 2015 25日目ã®è¨˜äº‹ã§ã™ã€‚ Scalist ã®çš†ã•ã‚“ã€ä¸€å¹´é–“ãŠç–²ã‚Œã•ã¾ã§ã—ãŸã€‚ 当記事ã®ç›®çš„ã¯ãƒžã‚¤ã‚¯ãƒã‚µãƒ¼ãƒ“ス(ã«é™ã‚‰ãªã„ã§ã™ãŒ)ã«ãŠã‘る失敗ã®æ€–ã•ã«ã¤ã„ã¦èª¬æ˜Žã™ã‚‹ã“ã¨ã¨ã€ãれを克æœã™ã‚‹ãŸã‚ã®ãƒ©ã‚¤ãƒ–ラリã€Failurewall を紹介ã™ã‚‹ã“ã¨ã§ã™ã€‚ 障害ã¯æ€–ㄠ障害ã®é€£éŽ– ç¾ä»£çš„㪠Web アプリケーションã¯å¤šãã®ä¾å˜ã‚’抱ãˆã¦ã„ã¾ã™ã€‚MySQL ã®ã‚ˆã†ãªãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã«ä¾å˜ã™ã‚‹ã“ã¨ã‚‚ã‚ã‚Œã°ã€Twitter API ã®ã‚ˆã†ãªå¤–部
Netflixã®githubリãƒã‚¸ãƒˆãƒªã¯Web技術ã®ç™¾è²¨åº—ã ã£ãŸ - DiaryException
検索ã—ã¦ã„ã‚‹ã¨ãªã«ã‹ã¨Netflixã®githubリãƒã‚¸ãƒˆãƒªãŒãƒ’ットã™ã‚‹ã®ã§ã€å…¨éƒ¨ï¼ˆ2015/07/18ç¾åœ¨åˆ†ï¼‰èª¿æŸ»ã—ã¦ã¿ãŸã€‚ github API㧠https://github.com/Netflix ã®ãƒªã‚¹ãƒˆã‚’全部å–å¾—ã—ã¦ã€åå‰ãƒ»æ¦‚è¦ãƒ»URL・最終更新日時 (ãªã‚“ã®æ›´æ–°ã ?) を抽出。 AWS用ã®ãƒ—ãƒãƒ€ã‚¯ãƒˆãŒå¤šã‹ã£ãŸã®ã§ã¾ãšãれらã¨ã€ãã®ä»–ã¨ã„ã†åˆ†é¡žã«ã—ãŸã€‚ãã®ä»–ã¯ã»ã¨ã‚“ã©ãŒJavaライブラリ・システムã ãŒã€ä¸€éƒ¨WebアプリケーションやPythonライブラリãŒã‚る。 日本語ã§ã®èª¬æ˜Žã¯Readmeã‚„Wikiを見ã¦æ›¸ã„ã¦ã„ã‚‹ãŒã€ç†è§£ãŒæ£ã—ããªã„ã‹ã‚‚ã—ã‚Œãªã„。 AWS用 aws-autoscaling Tools and Documentation about using Auto Scaling URL: https://github.com/Netflix/aws-autoscalin
Linux Profiling at Netflix
This talk discusses Linux profiling using perf_events (also called "perf") based on Netflix's use of it. It covers how to use perf to get CPU profiling working and overcome common issues. The speaker will give a tour of perf_events features and show how Netflix uses it to analyze performance across their massive Amazon EC2 Linux cloud. They rely on tools like perf for customer satisfaction, cost o
From Clouds to Roots: Performance Analysis at Netflix
Recent posts: 29 Oct 2024 » AI Flame Graphs 22 Jul 2024 » No More Blue Fridays 24 Mar 2024 » Linux Crisis Tools 17 Mar 2024 » The Return of the Frame Pointers 10 Mar 2024 » eBPF Documentary 28 Apr 2023 » eBPF Observability Tools Are Not Security Tools 01 Mar 2023 » USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon 17 Feb 2023 » USENIX SREcon APAC 2023: CFP 02 May 2022 » Brendan
NetflixãŒExpress.jsã«åµŒã¾ã‚‹
Spring Bootã«ã‚ˆã‚‹APIãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰æ§‹ç¯‰å®Ÿè·µã‚¬ã‚¤ãƒ‰ 第2版 何åƒäººã‚‚ã®é–‹ç™ºè€…ãŒã€InfoQã®ãƒŸãƒ‹ãƒ–ック「Practical Guide to Building an API Back End with Spring Bootã€ã‹ã‚‰ã€Spring Bootを使ã£ãŸREST API構築ã®åŸºç¤Žã‚’å¦ã‚“ã 。ã“ã®æœ¬ã§ã¯ã€å‡ºç‰ˆæ™‚ã«æ–°ã—ãリリースã•ã‚ŒãŸãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚ã‚‹ Spring Boot 2 を使用ã—ã¦ã„る。ã—ã‹ã—ã€Spring Boot3ãŒæœ€è¿‘リリースã•ã‚Œã€é‡è¦ãªå¤‰...
ã¯ã¦ãªäº¬éƒ½ã‚ªãƒ•ã‚£ã‚¹ã§ã€Netflix Meetupを開催ã—ã¾ã—㟠- Hatena Developer Blog
ã“ã‚“ã«ã¡ã¯ã€‚アプリケーションエンジニアã®id:daiksyã§ã™ã€‚ ã“ã®è¨˜äº‹ã¯ã¯ã¦ãªã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã‚¢ãƒ‰ãƒ™ãƒ³ãƒˆã‚«ãƒ¬ãƒ³ãƒ€ãƒ¼ã®2日目ã§ã™ã€‚ 今日ã¯ã€12月1æ—¥ã«ã¯ã¦ãªäº¬éƒ½ã‚ªãƒ•ã‚£ã‚¹ã§é–‹å‚¬ã•ã‚ŒãŸã€Netflix Meetup in Kyotoã®æ¨¡æ§˜ã‚’ã”紹介ã—よã†ã¨æ€ã„ã¾ã™ã€‚ 経緯 ã‚ã‚‹æ—¥ã€Netflix社ã®Jason Swartz(@swartzrock)ã•ã‚“ã‹ã‚‰ã€æ—¥æœ¬ã®Scalaコミュニティ宛ã«ç›¸è«‡ãŒã‚ã‚Šã¾ã—ãŸã€‚ãã®å†…容ã¯ã€ã€Œä»Šåº¦æ—¥æœ¬ã«éŠã³ã«è¡Œã予定ã§ã€æ±äº¬ã¨äº¬éƒ½ã«æ»žåœ¨ã™ã‚‹ã‚“ã ã‘ã©ã€ã‚ˆã„機会ã ã‹ã‚‰æ—¥æœ¬ã®ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã¨Meetupãªã©ã‚„ã‚Œãªã„ã‹ã€ã¨ã„ã†ã‚‚ã®ã§ã—ãŸã€‚ ã“ã®ç›¸è«‡ã‚’å—ã‘ãŸScalaコミュニティã‹ã‚‰ã€ScalaMatsuriã®ã‚¹ã‚¿ãƒƒãƒ•ã‚’ã—ã¦ã„ãŸé–¢è¥¿ã®ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã¨ã®ç¸ã‚‚ã‚ã£ã¦ã€Œäº¬éƒ½ã§èª°ã‹æ‰‹ã‚’挙ã’ã¾ã›ã‚“？ã€ã¨ã®æ‰“診ãŒã‚ã‚Šã€ã¾ãŸã¨ãªã„機会ãªã®ã§ã€ã¯ã¦ãªã®äº¬éƒ½ã‚ªãƒ•ã‚£ã‚¹ã®ã‚»ãƒŸãƒŠãƒ¼ãƒ«ãƒ¼ãƒ ã‚’æä¾›ã™ã‚‹ã“ã¨ã«ãªã‚Šã¾ã—
ZeroToDocker
Motivation:The NetflixOSS platform and related ecosystem services are extensive. While we make every attempt to document each project, being able to quickly evaluate NetflixOSS is a large challenge due to the breadth for most users. This becomes a very large challenge to anyone trying to understand individual parts of the platform. Another part of the challenge relates to how NetflixOSS was design
[セッションレãƒãƒ¼ãƒˆ]Netflixã«ãŠã‘ã‚‹Microservicesアーã‚テクãƒãƒ£ #reinvent | DevelopersIO
ã“ã®è¨˜äº‹ã¯ AWS re:Invent 2014ã€PFC304-JT - Effective Interprocess Communications in the Cloud: The Pros and Cons of Micro Services Architectures - Japanese Trackã®ãƒ¬ãƒãƒ¼ãƒˆã§ã™ã€‚ スピーカーã¯Netflixã®Sudhir Tonse。 レãƒãƒ¼ãƒˆ ã©ã†ã‚„ã£ã¦Microservicesã«å¤‰åŒ–ã—ã¦ã„ã£ãŸã®ã‹ã‚’話ã—ãŸã„。 ã“ã‚Œã¾ã§ä½•åº¦ã‹æœ¬ç•ªç’°å¢ƒãŒåœæ¢ã—ã€ãã“ã‹ã‚‰ãŸãã•ã‚“ã®ã“ã¨ã‚’å¦ã‚“ã 。ãれを共有ã—ãŸã„。 Netflixã«ã¤ã„ã¦ã€‚æ˜ ç”»ã®ã‚¹ãƒˆãƒªãƒ¼ãƒŸãƒ³ã‚°ã‚µãƒ¼ãƒ“ス。 PCã‚„PS4ãªã©ã§å†ç”Ÿã§ãる。 ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®1/3ã®ãƒˆãƒ©ãƒ•ã‚£ãƒƒã‚¯ã‚’NetflixãŒå ã‚ã‚‹ã“ã¨ãŒã‚る。 20億以上ã®ã‚¨ãƒƒãƒ‚APIリクエストãŒã‚ã£ã¦ã€500以上ã®MicroservicesãŒå‹•ã„ã¦ã„
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NetflixMeetup in Kyoto (2014/12/01 19:30〜)
日本ã§ã¯huluãªã©ãŒé¦´æŸ“ã¿æ·±ã„ãƒãƒƒãƒˆã§ã®å‹•ç”»ã‚¹ãƒˆãƒªãƒ¼ãƒŸãƒ³ã‚°é…信サービスã§ã™ãŒã€ 米国ã®å‹•ç”»é…信サイト大手ã®Netflixã¨ã„ã†ä¼šç¤¾ãŒã‚ã‚Šã¾ã™ã€‚ Netflix社ã§ã¯Scalaã§ã®ãƒ—ãƒãƒ€ã‚¯ãƒˆé–‹ç™ºã‚„マイクãƒã‚µãƒ¼ãƒ“スã®å–り組ã¿ãªã©ã€æŠ€è¡“çš„ã«ã‚‚アグレッシブãªãƒãƒ£ãƒ¬ãƒ³ã‚¸ã‚’ã™ã‚‹ä¼šç¤¾ã¨ã—ã¦çŸ¥ã‚‰ã‚Œã¦ãŠã‚Šã€ãã‚“ãªNetflixã®ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã§ã‚ã‚‹Jason Swartz(@swartzrock)ã•ã‚“ã‚’ãŠè¿Žãˆã—ã¦ã€Netflixã§ã®Scala活用事例や開発ã®å–り組ã¿ãªã©ã‚’ã”è¬›æ¼”é ‚ã予定ã§ã™ã€‚ ※å‚考スライド 当日ã¯è‹±èªžã®ç™ºè¡¨ã¨ãªã‚Šã¾ã™ã€‚(翻訳ç‰ã«ã¤ã„ã¦ã¯ç¾åœ¨æ¤œè¨Žä¸ã§ã™) コンテンツ 講演者プãƒãƒ•ã‚£ãƒ¼ãƒ« Jason is a Software Engineer in the San Francisco Bay Area, developing applications and services in Scala
éžåˆ†æ•£ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‚’分散データベース化ã™ã‚‹ã€ŒDynomiteã€ã€NetflixãŒã‚ªãƒ¼ãƒ—ンソースã§å…¬é–‹
Redisã‚„Memcachedã¨ã„ã£ãŸã‚¤ãƒ³ãƒ¡ãƒ¢ãƒªãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã¯éžå¸¸ã«é«˜é€Ÿã«ãƒ¬ã‚¹ãƒãƒ³ã‚¹ã‚’è¿”ã—ã¦ãれるデータストアã§ã™ãŒã€ãã‚Œå˜ä½“ã§ã¯ã‚¹ã‚±ãƒ¼ãƒ©ãƒ“リティやå¯ç”¨æ€§ãªã©ã«é™ç•ŒãŒã‚ã‚Šã¾ã™ã€‚ The Netflix Tech Blog: Introducing Dynomite - Making Non-Distributed Databases, Distributed NetflixãŒã‚ªãƒ¼ãƒ—ンソースã§å…¬é–‹ã—ãŸã€ŒDynomiteã€ï¼ˆãƒ€ã‚¤ãƒŠãƒžã‚¤ãƒˆã¨ã¯ç¶´ã‚ŠãŒé•ã†ã®ã«æ³¨æ„）ã¯ã€ã“ã†ã—ãŸãƒ‡ãƒ¼ã‚¿ã‚¹ãƒˆã‚¢ã‚’分散データベース化ã—ã€é«˜é€Ÿãªãƒ‡ãƒ¼ã‚¿ã‚¹ãƒˆã‚¢ã®ç‰¹é•·ã‚’æ´»ã‹ã—ã¤ã¤é«˜ã„スケーラビリティやå¯ç”¨æ€§ã‚’実ç¾ã™ã‚‹ãŸã‚ã®ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã§ã™ã€‚ アプリケーションå´ã§ã‚·ãƒ£ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã®ã‚ˆã†ãªé¢å€’ãªãƒ‡ãƒ¼ã‚¿æ§‹é€ ã‚’è¨å®šã™ã‚‹ã“ã¨ãªãã€Redisã‚„Memcachedをノードã¨ã—ã€Cassandraã‚„Amazonクラウドã®DynamoDBã®ã‚ˆã†ãªå¤§è¦
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
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