Reactive Applications
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The document discusses reactive applications and programming. Key points include: - Reactive applications are event-driven and can scale, be resilient, and responsive. They react to events, time, load, failures, and users. - Various technologies like RxJava, Rx.NET, and RxJS can be used to build reactive applications by translating reactive concepts like events and streams into code. - Reactive applications follow an asynchronous event-driven model with isolated blocks communicating via messages. They are location-transparent and focus on failures, state, and being always responsive.
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Reactive Applications
- 1. reactive applications new kids for your building blocks 09/2013 twitter.com/MikeBild
- 2. la, la, la, la, la, la tonight • quick • hard • dirty Reactive
- 3. developers • translating the reactive concepts into code • use Rx / RxJS for example • https://gist.github.com/MikeBild/6725003 yeah
- 4. why • unstructured information • real-time analytics and intelligence • high user interaction and collaboration • clusters vs. elasticity • big-data vs. high-frequency
- 5. why • historical analysis • triggered finite state machines • data synchronization • more and more connected devices • business domain complexity
- 6. working software • is useful information output that • fits business domain • fits non-functional requirements
- 7. new challenges • cause - effect • time correlation • information/data correlation
- 8. reactive manifesto • it‘s a concept • technology agnostic • pros and cons • event / message centric
- 9. 4 building blocks • Event-Driven • Scalable • Resilient • Responsive and Interactive
- 10. react to ... • events • time • load • failures • users
- 11. how • RxJava | Java, Clojure, Scala, Groovy, JRuby • Rx.NET | C#, F#,VB • RxJS | JavaScript | NodeJS • ReactiveCocoa | Objective-C • RxCpp | C++ • more and more
- 12. glorious • NetFlix • GitHub for Windows / Mac • more and more
- 13. events • things that have happened • events represent state changes • data structure • implicit and explicit information • payload
- 14. event-driven • events • sources • analytics & abstracting • handling
- 15. event-driven • highly decoupled • ever non-blocking • reactive from top to bottom • input - processing - output • asynchronous Event-Stream(s)
- 16. translate into code • event sources structure • create and compose • transform, filter, aggregate, group, combine
- 17. scalable • easy to expand or update on demand • elastic scale out and scale in • helps to manage risk
- 18. scalable • location transparent • message passing style • abstraction for opportunities • local context or distributed within same semantic
- 19. translate into code • composing • scheduling • tasks
- 20. resilient • no downtime - be stale or compensate • failures/errors as a first class construct • failures are business decisions • react to manage failures • monitor what possible
- 21. translate into code • state • resources • catch errors • retry • compensation
- 22. responsive • react to a stimulus • high user to system interaction • „always ready“
- 23. responsive • permanent connection between user and system • collaborative environment • real-time feedback
- 24. translate into code • throttle, delay time • analytics • filter • transform
- 25. design • isolated / autonomous blocks • async event-driven message passing • location transparent • ever responsive • explicit failure management @reactivemanifesto.org
- 26. conclusion What does that mean here? @reactivemanifesto.org
- 27. reactive applications • Observable Models • Event Streams • Stateful Clients
- 28. reactive applications • react from top to buttom to top • observe data changes • execute logic • store state • real-time update UI
- 29. thanks • http://www.reactivemanifesto.org/ • https://github.com/Reactive-Extensions/RxJS-DOM • https://github.com/Reactive-Extensions/RxJS • http://mywebbasedcomputer.com/users/johngslater/tech/rx/bubbleDiagrams.html • https://github.com/Netflix/RxJava • https://github.com/blog/1127-github-for-windows • https://netflix.com