Download as PDF, PPTX
Uploaded byYusaku Watanabe
QCon SF-feedback
QCon is an annual engineering conference held in San Francisco that brings together team leads, architects, and project managers. This year's 8th conference featured over 100 speakers across 15 tracks covering topics like microservices, mobile development, Java, and data science. Attendees can learn about cutting-edge technologies from companies like Google, Netflix, and catch up on trends in web development. The conference runs from 9am to 6pm each day and includes meals and social events. Keynote speakers include security expert Bruce Schneier and Leslie Lamport, winner of the 2013 Turing Award. Tracks explore managing microservices at scale, containers and deployment automation, and reactive system architectures.
![[Devopsdays2021] Roll Your Product with Kaizen Culture](https://cdn.slidesharecdn.com/ss_thumbnails/devopsdays2021rollyourproduct-210415064545-thumbnail.jpg?width=640&height=640&fit=bounds)
QCon SF-feedback
- 1.
- 2. QCon Conferenceとは QCon SanFrancisco 2014は今回で8回目を迎える、チームリード、 アーキテクト、プロジェクトマネージャーのために開催されるエンジ ニアによるエンジニアのためのカンファレンス。 100名以上の講演者が登壇し、15のトラックが行われ、オープンスペー スなどさまざまな人脈作りの機会を提供する。
- 3. なぜ参加したか • Google, Twitter,Facebook, Netflixなど様々なWEB企業が参加し、 WEB技術における最前線をキャッチアップするため。 • 海外における技術へのスタンス、カルチャーを理解し組織のあり方を 学ぶため • 日本でも多く開催されているカンファレンス(QCon Tokyoなど)との情 報格差を確認するため
- 4.
- 5. Managing Microservices @Scale 無数のきめ細かなWebサービスを持つ企業(たとえばNetflix、LinkedIn)はその複雑性を どのように管理しているのか The Future of Mobile モバイルとパフォーマンス改善の将来 Java at the Cutting Edge Javaエコシステムの最先端 Continuous Delivery 継続的デリバリーの哲学、カルチャー、課題、ベストプラクティス Engineering Culture エンジニアリングカルチャー作りの成功と失敗 Applied Machine Learning and Data Science あなたの巨大なビッグデータを理解する Unleashing the Power of Streaming Data ビジネスおよびエンジニアリング問題を解決するため、企業はどのように新しいデータ ストリーム処理パラダイムを活用しているか
- 6. Next gen HTML5and JS Webは変化している、次に来るのは何か？ Containers, Dockers, Puppets 大規模デプロイメント現場におけるデプロイメント自動化 Measuring Engineering and Product Success 進 を計測、トラックするための戦略 Real World Functional 関数プログラミングのコンセプトを現場に活かす Modern Computer Science In the Real World 現在の問題に立ち向かうために、最新のコンピュータサイエンスが役に立てること Reactive Service Architecture リアクティブ、レスポンシブ、フォールトトレラントなど The Hottest Tech in the World of Big Data ビッグデータ技術領域における最新情報 Architectures You've Always Wondered about 最新、最大のインターネットアーキテクチャ
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
- 13. Bruce Schneier Security Guru BruceSchneier is a cryptographer, computer security and privacy specialist, and writer. Called a "security guru" by The Economist, he is the author of 12 books as well as the famous ‘Schneier on Security’ blog. He keynoted at QCon NY weeks after the Snowden leak about prism in 2013 and was one of the top rated keynotes. @schneierblog
- 14. Leslie Lamport Turing AwardWinner of Paxos and LaTeX 2013 Turing Award Winner for "fundamental contributions to the theory and practice of distributed and concurrent systems,” much of the infrastructure implemented in places like Amazon.com is based on his research.
- 15. Melody Meckfessel Google EngineeringDirector Melody Meckfessel leads a team of 200+ engineers responsible for Developer Infrastructure for Google and its Cloud platform. Her team builds highly scalable systems, from IDEs and automated test systems to deployment through Google’s massive production environment. She is a hands-on technical leader with 18 years of experience building and maintaining large-scale distributed systems.
- 16. •畑史彦 Functional programming &Reactive System •伊藤和宏 Starting Apache Spark training •渡邊雄作 MicroService Cutting Edge
- 17.
- 18. Speaker: Paul Osman Directorof Platform Engineering at 500px Paul Osman is the Director of Platform Engineering at 500px, the world’s premier photography community. He and his team concentrate on building a scalable and maintainable infrastructure that powers a growing community of photography enthusiasts. Prior to 500px, Paul worked on the Platform team at SoundCloud. His primary interests are distributed systems, APIs and scalable teams. @paulosman Find Paul Osman at Organizing your company to embrace Microservices
- 19. Organizations which designsystems are constrained to produce designs which are copies of the communication structures of these organisations :Melvin Conway システムを設計する組織は、その構造をそっくりまねた 構造の設計を生み出してしまう
- 20. 1. コードベース バージョン管理されている1つのコードベースと複数のデプロイ 2. 依存関係 依存関係を明示的に宣言し分離する 3.設定 設定を環境変数に格納する http://twelve-factor-ja.herokuapp.com/(日本語訳) Herokuの中の人 Adam Wiggins氏によって書かれた、モダン でスケーラブルかつ継続的デプロイ可能なWebアプリケー ションを構築するための方法論
- 21.
- 24.
- 26.
- 29. Scalable Microservices atNetflix. Challenges and Tools of the Trade Speaker: Sudhir Tonse Responsible for Netflix Cloud Platform Sudhir Tonse manages the Cloud Platform Infrastructure team at Netflix and is responsible for many of the services and components that form the Netflix Cloud Platform as a Service. Many of these components have been open sourced under the NetflixOSS umbrella. Open source contribution includes Archaius: a dynamic configuration/properties management library, Ribbon: an Inter Process Communications framework that includes Cloud friendly Software load balancers, Karyon: the nucleus of a PaaS service etc. Prior to Netflix, Sudhir was an Architect at Netscape/AOL delivering large-scale consumer and enterprise applications in the area of Personalization, Infrastructure and Advertising Solutions. Sudhir is a weekend golfer and tries to make the most of the wonderful California weather and public courses. @stonse Find Sudhir Tonse at https://twitter.com/stonse http://www.linkedin.com/in/sudhirtonse
- 30.
- 31. デメリット •Large Codebase •Many Components,no clear ownership •Long deployment cycles メリット •Single codebase Easy to develop/debug/deploy Good IDE support •Easy to scale horizontally •A Central Ops team can efficiently handle
- 32. User Accounts Shopping Cart ProductCatalog Customer Service
- 33. Monolithic Apps –Failure & Availability
- 34.
- 35. •Many smaller (finegrained), clearly scoped services Single Responsibility Principle Domain Driven Development Bounded Context Independently Managed •Clear ownership for each service Typically need/adopt the “DevOps” model MicroServiceの特徴
- 36. Faster and simplerdeployments and rollbacks Independent Speed of Delivery (by different teams) Right framework/tool/language for each domain Recommendation component using Python?, Catalog Service in Java .. Greater Resiliency Fault Isolation Better Availability If architected right … なぜMicroServiceなのか？
- 37. MicroServices – separatesingle purpose services
- 38. Load Balancer Account ServiceCatalog Service Recommendation Service Customer Service Service Catalog DB API Gateway Customer DB Microservices Architecture
- 39. Can lead tochaos if not designed right …
- 40. •Service Discovery •Operational Overhead(100s of services; DevOps model absolutely required) •Distributed Systems are inherently Complex •N/W Latency, Fault Tolerance, Serialization overhead .. •Service Interface Versioning, Mismatches? •Testing (Need the entire ecosystem to test) •Fan out of Requests -> Increases n/w traffic 数々の挑戦をしてきた
- 41.
- 42. Homogeneity in APolyglot Ecosystem
- 43. • Eureka –for Service Registry/Discovery • Karyon – for Server (Reactive or threaded/servlet container based) • Ribbon – for IPC Client And Fault Tolerant Smart LoadBalancer • Hystrix – for Fault Tolerance and Resiliency • Archaius – for distributed/dynamic Properties • Servo – unified Feature rich Metrics/Insight • EVCache – for distributed cache • Curator/Exhibitor – for zookeeper based operations
- 44.
- 45. QCon SF 2014まとめ
- 46.