Decomposing applications for deployability and scalability(SpringSource webinar)
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Today, there are several trends that are forcing application architectures to evolve. Users expect a rich, interactive and dynamic user experience on a wide variety of clients including mobile devices. Applications must be highly scalable, highly available and run on cloud environments. Organizations often want to frequently roll out updates, even multiple times a day. Consequently, it’s no longer adequate to develop simple, monolithic web applications that serve up HTML to desktop browsers. In this talk we describe the limitations of a monolithic architecture. You will learn how to use the scale cube to decompose your application into a set of narrowly focused, independently deployable back-end services and an HTML 5 client. We will also discuss the role of technologies such as Spring and AMQP brokers. You will learn how a modern PaaS such as Cloud Foundry simplifies the development and deployment of this style of application.
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Decomposing applications for deployability and scalability(SpringSource webinar)
- 1. © 2013 Spring, by Pivotal Chris Richardson Author of POJOs in Action Founder of the original CloudFoundry.com @crichardson chris.richardson@springsource.com http://plainoldobjects.com Decomposing applications for deployability and scalability
- 2. @crichardson Presentation goal How decomposing applications improves deployability and scalability and simplifies the adoption of new technologies
- 8. @crichardson vmc push About-Chris Developer Advocate
- 9. @crichardson Agenda The (sometimes evil) monolith Decomposing applications into services Developing and deploying services How do services communicate?
- 10. @crichardson Let’s imagine you are building an e-commerce application
- 12. @crichardson But there are problems with a monolithic architecture
- 14. @crichardson 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
- 15. @crichardson Overloads your IDE and container Slows down development
- 16. @crichardson Shipping team Accounting Engineering Obstacle to scaling development E-commerce application
- 17. @crichardson WAR Shipping Accounting InventoryService StoreFront UI Shipping team Accounting team Inventory team UI Team Obstacle to scaling development
- 18. @crichardson Lots of coordination and communication required Obstacle to scaling development I want to update the UI But the backend is not working yet!
- 19. @crichardson Requires long-term commitment to a technology stack
- 20. @crichardson Agenda The (sometimes evil) monolith Decomposing applications into services Developing and deploying services How do services communicate?
- 21. @crichardson
- 22. @crichardson The scale cube X axis - horizontal duplication Z axis -data partitioning Y axis - functional decomposition Scale by splitting sim ilar things Scale by splitting different things
- 23. @crichardson Y-axis scaling - application level WAR Shipping Service Accounting Service Inventory Service StoreFrontUI
- 24. @crichardson Y-axis scaling - application level Store front web application shipping web application inventory web application Apply X axis cloning and/or Z axis partitioning to each service Accounting Service StoreFrontUI accounting web application Shipping Service Inventory Service
- 25. @crichardson 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
- 26. @crichardson Partitioning strategies Too few Drawbacks of the monolithic architecture Too many - a.k.a. Nano-service anti-pattern Runtime overhead Potential risk of excessive network hops Potentially difficult to understand system Something of an art
- 31. @crichardson Implementing and deploying features that span multiple services
- 32. @crichardson 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
- 33. @crichardson But there are many benefits Scales development: develop, deploy and scale each service independently, e.g. update UI independently Simplifies distributed development and outsourcing Improves fault isolation Eliminates long-term commitment to a single technology stack Modular, polyglot, multi- framework applications
- 34. @crichardson Two levels of architecture System-level Services Inter-service glue: interfaces and communication mechanisms Slow changing Service-level Internal architecture of each service Each service could use a different technology stack Pick the best tool for the job Rapidly evolving
- 35. @crichardson 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
- 36. @crichardson The human body as a system
- 37. @crichardson 50 to 70 billion of your cells die each day
- 38. @crichardson Yet you (the system) remain you
- 39. @crichardson Can we build software systems with these characteristics? http://dreamsongs.com/Files/WhitherSoftware.pdf http://dreamsongs.com/Files/ DesignBeyondHumanAbilitiesSimp.pdf
- 40. @crichardson Agenda The (sometimes evil) monolith Decomposing applications into services Developing and deploying services How do services communicate?
- 41. @crichardson Services come in all shapes and sizes
- 42. @crichardson Example service: Spring MVC @Controller class TwilioController { @Autowired var surveyManagementService: SurveyManagementService = _ @RequestMapping(value = Array("/begincall.html")) @ResponseBody def beginCall(@RequestParam("From") callerId: String) = { surveyManagementService.findSurveyByCallerId(callerId) match { ... case Some(survey) => <Response> <Say>{ survey.prompt }</Say> <Gather action="handleresponse.html" method="POST" numDigits="1"> { for ((choice, index) <- survey.choices zipWithIndex) yield <Say>Press { index } for { choice }</Say> } </Gather> <Say>We are sorry you could not decide</Say> <Hangup/> </Response> } }
- 44. @crichardson Example service: NodeJS var express = require('express') , http = require('http') , amqp = require(‘amqp’) ....; server.listen(8081); ... var amqpCon = amqp.createConnection(...); io.sockets.on('connection', function (socket) { function amqpMessageHandler(message, headers, deliveryInfo) { var m = JSON.parse(message.data.toString()); socket.emit(‘tick’, m); }; amqpCon.queue(“”, {}, function(queue) { queue.bind(“myExchange”, “”); queue.subscribe(amqpMessageHandler); }); }); Simple portable way to deliver AMQP messages to the browser
- 45. @crichardson Example service: Sinatra require 'sinatra' post '/' do phone_number = params[:From] registration_url = "#{ENV['REGISTRATION_URL']}?phoneNumber=#{URI.encode(phone_number, "+")}" <<-eof <Response> <Sms>To complete registration please go to #{registration_url}</Sms> </Response> eof end
- 46. @crichardson Service deployment options VM or Physical Machine Linux Container/LXC JVM JAR/WAR/OSGI bundle/... Isolation, manageability Density/efficiency You could do this yourself but ...
- 47. @crichardson PaaS dramatically simplifies deployment Applica'on Service Interface OSS community Private Clouds Public Clouds Micro Clouds Data Services Other Services Msg Services vFabric Postgres vFabric RabbitMQTM Additional partners services …
- 48. @crichardson Cloud Foundry features One step deployment: vmc push Single application Service-oriented application Easy platform service provisioning: vmc create-service Simple scaling: vmc instances app-name +/- N Health monitoring and automated recovery
- 49. @crichardson Benefits of PaaS Simplifies and automates deployment Eliminates barriers to adding new service Eliminates barriers to using a new platform service Imposes conventions: packaging, configuration and deployment Enforces consistency Eliminates accidental complexity
- 50. @crichardson Agenda The (sometimes evil) monolith Decomposing applications into services Developing and deploying services How do services communicate?
- 51. @crichardson Inter-service communication options Synchronous HTTP asynchronous AMQP Formats: JSON, XML, Protocol Buffers, Thrift, ... Asynchronous is preferred JSON is fashionable but binary format is more efficient
- 53. @crichardson Benefits Decouples client from server: client unaware of server’s coordinates (URL) Message broker buffers message when server is down/ slow Supports a variety of communication patterns, e.g. point- to-point, pub-sub, ...
- 54. @crichardson Drawbacks Additional complexity of message broker Request/reply-style communication is more complex
- 55. @crichardson Spring Integration 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
- 57. Pros and cons of REST Pros Simple and familiar Request/reply is easy Browser and firewall friendly No intermediate broker Cons Only supports request/ reply Server must be available Client needs to know URL(s) of server(s)
- 58. @crichardson Spring MVC makes REST easy @Controller public class AccountController { @Autowired private MoneyTransferService moneyTransferService; @RequestMapping(value = "/accounts/{accountId}", method = RequestMethod.GET) @ResponseBody public AccountInfo getAccount(@PathVariable String accountId) { Account account = moneyTransferService.findAccountByid(accountId); return makeAccountInfo(account); } @RequestMapping(value = "/accounts", method = RequestMethod.POST) @ResponseStatus( HttpStatus.CREATED ) public void createAccount(@RequestBody AccountInfo accountInfo, UriComponentsBuilder builder, HttpServletResponse response) { ... } URL matching & destructuring object XML/JSON XML/JSON object
- 59. @crichardson About Hypertext As The Engine Of Application State (HATEOAS) $ curl http://cf-auto-scaler.cloudfoundry.com {"links": [ {"rel":"autoscaledapps", "href":"http://cf-auto-scaler.cloudfoundry.com/autoscaledapps"} ]} The well known URL Linklink type $ curl http://cf-auto-scaler.cloudfoundry.com/autoscaledapps {"content":[ {"name":"vertx-clock", "links":[ {"rel":"self","href":"http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock"}, {"rel":"rules","href":"http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock/rules"} ] }], ... } Links to act on this app
- 60. @crichardson Spring HATEOAS @Controller @RequestMapping(value = "/autoscaledapps") public class AutoscaledAppController { @RequestMapping(value = "/{appName}", method = RequestMethod.GET) public HttpEntity<AutoscaledAppResource> get(@PathVariable String appName) { AutoscaledAppResource ar = new AutoscaledAppResource(appName); ar.add(linkTo(AutoscaledAppController.class) .slash(appName).withSelfRel()); ar.add(linkTo(AutoscaledAppController.class) .slash(appName).slash("rules").withRel("rules")); return new HttpEntity<AutoscaledAppResource>(ar); } ... } https://github.com/SpringSource/spring-hateoas public class AutoscaledAppResource extends ResourceSupport { private String name;
- 61. @crichardson Consuming RESTful WS RestTemplate restTemplate = new RestTemplate(); AccountInfo accountInfo = new AccountInfo(...); URI accountUrl = restTemplate.postForLocation("http://localhost/accounts", accountInfo); ResponseEntity<AccountInfo> accountInfoResponse = restTemplate.getForEntity(accountUrl, AccountInfo.class); Assert.assertEquals(HttpStatus.SC_OK, accountInfoResponse.getStatusCode()); AccountInfo accountInfo2 = accountInfoResponse.getBody(); ...
- 62. @crichardson The Spring REST shell$ rest-shell http://localhost:8080:> baseUri http://cf-auto-scaler.cloudfoundry.com http://cf-auto-scaler.cloudfoundry.com:> discover rel href ======================================================================= autoscaledapps http://cf-auto-scaler.cloudfoundry.com/autoscaledapps http://cf-auto-scaler.cloudfoundry.com:> follow --rel autoscaledapps http://cf-auto-scaler.cloudfoundry.com/autoscaledapps:> post --from src/test/resources/examplejson/ createapp1.json --follow true 1 files uploaded to the server using POST http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock:> discover rel href ================================================================================ self http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock rules http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock/rules http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock:> follow --rel rules http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock/rules:> post --from src/test/resources/examplejson/createrule1.json --follow true 1 files uploaded to the server using POST http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock/rules/idle:> up http://cf-auto-scaler.cloudfoundry.com/autoscaledapps/vertx-clock/rules:> up
- 63. @crichardson Writing code that calls services
- 64. @crichardson The need for parallelism Product Details Controller Product Details Recommendations Reviews getProductDetails() getRecomendations() getReviews() Call in parallel Display Product
- 65. @crichardson Futures are a great concurrency abstraction An object that will contain the result of a concurrent computation http://en.wikipedia.org/wiki/Futures_and_promises Hides the underlying concurrency mechanism: threads or async Future<Integer> result = executorService.submit(new Callable<Integer>() {... }); Java has basic futures. We can do much better....
- 66. @crichardson Better: Futures with callbacks val f : Future[Int] = Future { ... } f onSuccess { case x : Int => println(x) } f onFailure { case e : Exception => println("exception thrown") } Guava ListenableFutures, Java 8 CompletableFuture, Scala Futures
- 67. @crichardson Even better: Composable Futures val f1 = Future { ... ; 1 } val f2 = Future { ... ; 2 } val f4 = f2.map(_ * 2) assertEquals(4, Await.result(f4, 1 second)) val fzip = f1 zip f2 assertEquals((1, 2), Await.result(fzip, 1 second)) def asyncOp(x : Int) = Future { x * x} val f = Future.sequence((1 to 5).map { x => asyncOp(x) }) assertEquals(List(1, 4, 9, 16, 25), Await.result(f, 1 second)) Scala Futures Transforms Future Combines two futures Transforms list of futures to a future containing a list
- 68. @crichardson Using Scala futures def callB() : Future[...] = ... def callC() : Future[...] = ... def callD() : Future[...] = ... val future = for { (b, c) <- callB() zip callC(); d <- callD(b, c) } yield d val result = Await.result(future, 1 second) Two calls execute in parallel And then invokes D Get the result of DScala Futures
- 69. @crichardson Handling partial failures Service A Service B Down? Slow? Down? Slow?
- 70. @crichardson 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
- 71. @crichardson How to run out of threads Tomcat Execute thread pool HTTP Request Thread 1 Thread 2 Thread 3 Thread n Service A Service B If service B is down then thread will be blocked XX X X X Eventually all threads will be blocked
- 72. @crichardson Their approach Network timeouts and retries Invoke remote services via a bounded thread pool Use the Circuit Breaker pattern On failure: return default/cached data return error to caller https://github.com/Netflix/Hystrix
- 74. @crichardson Monolithic applications are simple to develop and deploy BUT have significant drawbacks
- 75. @crichardson Apply the scale cube Modular, polyglot, and scalable applications Services developed, deployed and scaled independently
- 76. @crichardson Cloud Provider Interface Applica'on Service Interface Private Clouds Public Clouds Micro Clouds Data Services Other Services Msg Services .js Cloud Foundry helps
- 77. @crichardson Questions? @crichardson chris.richardson@springsource.com http://plainoldobjects.com - code and slides www.springsource.org
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