Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Decomposing applications for deployability and scalability (cfopentour india)

1 245 vues

Publié le

30 minute version of the talk given in Bangalore and India in Sept 2012

Publié dans : Technologie, Business
  • Soyez le premier à commenter

Decomposing applications for deployability and scalability (cfopentour india)

  1. 1. Decomposing applications fordeployability and scalability Chris Richardson Author of POJOs in Action Founder of the original CloudFoundry.com @crichardson crichardson@vmware.com http://plainoldobjects.com/ 1
  2. 2. Presentation goal How decomposing applications improves deployability and scalability 2
  3. 3. vmc push About-Chris Developer Advocate for CloudFoundry.comSignup at http://cloudfoundry.com cfopentour2012 3
  4. 4. Agenda§The (sometimes evil) monolith§Decomposing applications into services§How do services communicate? 4
  5. 5. Let’s imagine you are building an e-commerce application 5
  6. 6. Traditional web application architecture WAR StoreFrontUI Accounting Service MySQL Browser Apache InventoryService Database Shipping ServiceSimple to Tomcat develop test deploy scale 6
  7. 7. But there are problems with a monolithic architecture 7
  8. 8. Users expect a rich, dynamicand interactive experience h oug en ood ’tg HTTP Request isn ure ect Java Web Browser it HTML/Javascript Application Ia rch ty le U s Old Real-time web ≅ NodeJS 8
  9. 9. Obstacle to frequent deployments§Need to redeploy everything to change one component§Interrupts long running background (e.g. Quartz) jobs§Increases risk of failure Fear of change§Updates will happen less often§e.g. Makes A/B testing UI really difficult 9
  10. 10. Overloads your IDE and container Slows down development 10
  11. 11. Obstacle to scaling development Accounting team E-commerce Engineering application Shipping team 11
  12. 12. Obstacle to scaling development WAR UI team StoreFrontUI Accounting team Accounting Inventory team InventoryService Shipping team Shipping 12
  13. 13. Obstacle to scaling development Lots of coordination and communication required 13
  14. 14. Requires long-term commitment to a technology stack 14
  15. 15. Agenda§The (sometimes evil) monolith§Decomposing applications into services§How do services communicate? 15
  16. 16. 16
  17. 17. The scale cubeY axis -functionaldecompositionScale by im ingsplitting g s on r ila tin itidifferent things lit art p gs y ata th ale - d sp i s ax in b Z X axis - horizontal Sc duplication 17
  18. 18. Y-axis scaling - application level WAR StoreFrontUI Accounting Service InventoryService Shipping Service 18
  19. 19. Y-axis scaling - application level accounting web application Accounting Service Store front web application inventory web application InventoryService StoreFrontUI shipping web application Shipping Service Apply X axis cloning and/or Z axis partitioning to each service 19
  20. 20. Partitioning strategies§Partition by verb, e.g. shipping service§Partition by noun, e.g. inventory service§Single Responsibility Principle§Unix utilities - do one focussed thing well Something of an art 20
  21. 21. Real world examples http://techblog.netflix.com/ Between 100-150 services are accessed to build a page. http://highscalability.com/amazon-architecture http://www.addsimplicity.com/downloads/ eBaySDForum2006-11-29.pdf http://queue.acm.org/detail.cfm?id=1394128 21
  22. 22. There are drawbacks 22
  23. 23. Complexity See Steve Yegge’s GooglePlatforms Rant re Amazon.com 23
  24. 24. Multiple databases =Transaction management challenges 24
  25. 25. When to use it? In the beginning: •You don’t need it •It will slow you down Later on: •You need it •Refactoring is painful 25
  26. 26. But there are many benefits§Scales development: develop, deploy and scale each service independently§Update UI independently§Improves fault isolation§Eliminates long-term commitment to a single technology stack Modular, polyglot, multi- framework applications 26
  27. 27. Two levels of architecture System-levelServicesInter-service glue: interfaces and communication mechanismsSlow changing Service-levelInternal architecture of each serviceEach service could use a different technology stackPick the best tool for the jobRapidly evolving 27
  28. 28. If services are small...§Regularly rewrite using a better technology stack§Adapt system to changing requirements and better technology without a total rewrite§Pick the best developers rather than best <pick a language> developers polyglot culture Fred George “Developer Anarchy” 28
  29. 29. The human body as a system 29
  30. 30. 50 to 70 billion of your cells die each day 30
  31. 31. Yet you (the system) remain you 31
  32. 32. Can we build software systems with these characteristics? http://dreamsongs.com/Files/ DesignBeyondHumanAbilitiesSimp.pdf http://dreamsongs.com/Files/WhitherSoftware.pdf 32
  33. 33. Agenda§The (sometimes evil) monolith§Decomposing applications into services§How do services communicate? 33
  34. 34. Inter-service communication options§Synchronous HTTP asynchronous AMQP§Formats: JSON, XML, Protocol Buffers, Thrift, ...§Even via the database Asynchronous is preferred JSON is fashionable but binary format is more efficient 34
  35. 35. Asynchronous message-based communication wgrus-billing.war Accounting Servicewgrus-store.war wgrus-inventory.war RabbitMQ StoreFrontUI (Message InventoryService MySQL Broker) wgrus-shipping.war ShippingService 35
  36. 36. Benefits§Decouples caller from server§Caller unaware of server’s coordinates (URL)§Message broker buffers message when server isdown/slow 36
  37. 37. Drawbacks§Additional complexity of message broker§RPC using messaging is more complex 37
  38. 38. Writing code that calls services 38
  39. 39. The need for parallelism Service B b = serviceB() Call in parallel c = serviceC() Service A Service C d = serviceD(b, c) Service D 39
  40. 40. Java Futures are a greatconcurrency abstraction http://en.wikipedia.org/wiki/Futures_and_promises 40
  41. 41. Akka’s composablefutures are even better 41
  42. 42. Using Akka futuresdef callB() : Future[...] = ...def callC() : Future[...] = ...def callD() : Future[...] = ... Two calls execute in parallelval future = for { (b, c) <- callB() zip callC(); d <- callD(b, c) And then invokes D } yield dval result = Await.result(future, 1 second) http://doc.akka.io/docs/akka/2.0.1/scala/futures.html Get the result of D 42
  43. 43. Spring Integration§Implements EAI patterns§Provides the building blocks for a pipes and filters architecture§Enables development of application components that are •loosely coupled •insulated from messaging infrastructure§Messaging defined declaratively 43
  44. 44. Handling failure Service A Service B Errors happen in distributed systems 44
  45. 45. About Netflix > 1B API calls/day 1 API call average 6 service calls Fault tolerance is essential http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html 45
  46. 46. Use timeouts and retries Never wait forever Errors can be transient retry http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html 46
  47. 47. Use per-dependency bounded thread pool Service A Runnable 1 Task 1 Runnable 2 Task 2 Service B Runnable ... Task ... bounded queue bounded thread pool Fails fast if Limits number ofservice is slow or down outstanding requests http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html 47
  48. 48. Use a circuit breakerHigh error rate stop calling temporarily Down wait for it to come back up Slow gives it a chance to recover http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html 48
  49. 49. On failure Return cached dataAvoidFailing Return default data Fail fast http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html 49
  50. 50. Summary 50
  51. 51. Monolithic applications aresimple to develop and deploy BUT have significant drawbacks 51
  52. 52. Apply the scale cube §Modular, polyglot, and scalable applications §Services developed,Y axis -functionaldecomposition ng deployed and scaled independently i on iti rt pa ta da is- ax Z X axis - horizontal duplication 52
  53. 53. @crichardson crichardson@vmware.com http://slideshare.net/chris.e.richardson/ Questions?www.cloudfoundry.com promo code: cfopentour2012