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Scaling with Spring & Cloud Foundry

Josh Long
October 10, 2014

Scaling with Spring & Cloud Foundry

this talk looks at how to scale applications. It looks at horizontal duplication, data partitioning and microservices, all set against the backdrop of Spring Integration, Spring AMQP, Spring Session, Spring Data, Spring MVC, Spring Cloud and the Netflix OSS stack.

Josh Long

October 10, 2014

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  1. S C A L I N G 
 W I

    T H Josh Long @starbuxman jlong@pivotal.io github.com/joshlong (⻰龙之春) S P R I N G
  2. Spring Developer Advocate @Starbuxman Josh Long (⻰龙之春) @starbuxman jlong@pivotal.io |

    Jean Claude van Damme! Java mascot Duke some thing’s I’ve authored...
  3. @Starbuxman


    code will be open sourced. W H Y S C A L E ?
  5. Moore’s Law no longer works @Starbuxman § processing can’t scale

    up § concurrent, horizontal architectures are easier to scale § “process”-style concurrency is easy to scale still Moore's law is the observation that, over the history of computing hardware, the number of transistors in a dense integrated circuit doubles approximately every two years. The law is named after Gordon E. Moore, co-founder of the Intel Corporation, who described the trend in his 1965 paper.! ! http://en.wikipedia.org/wiki/Moore's_law
  6. data @Starbuxman 44000% larger in 2020 than 2009 data production

    is expected to be :
  7. systems are increasingly complex @Starbuxman § a complex system today

    has a lot of moving parts § security § multiple clients (iOS, Android, Windows Mobile, etc.) § multiple (business) domains § integration between systems § requires more people working on the same problem
  8. mobile More than 
 1.5 MILLION activations daily * @Starbuxman

    * http://www.androidcentral.com/larry-page-15-million-android-devices-activated-every-day
  9. social: a connected world in 60 seconds @Starbuxman 3125 photos

    uploaded 7630 messages sent 7610 searches 2MM videos viewed 2000 checkins 175k tweets 1090 visitors 700k messages sent * source: visual.ly/60-seconds-social-media
  10. the internet of things @Starbuxman “In five years, by 2018,

    Earth will be home to 7.6 billion people, says the United Nations. By contrast, some 25 billion devices will be connected by 2015, and 50 billion by 2020, says Cisco.”! ! http://www.businessinsider.com/what-you-need-to-know-about-the- internet-of-things-2013-3?op=1#ixzz3FxCafwWe § IPv6 gives us more addresses § devices are getting smaller, more ubiquitous § “devices” include homes appliances (refrigerators, washers, coffee machines, dryers), roads, air pollution monitors, (human) body monitors, etc
  11. how to think about scale? @Starbuxman Chris Richardson (http://microservices.io/articles/scalecube.html) introduced

    me to this “scale cube” 
 from The Art of Scaling Software

    code will be open sourced. X - A X I S H O R I Z O N TA L D U P L I C AT I O N

  14. no state and lots of gain @Starbuxman § obvious: no

    state means no sharing § no sharing means that applications can be scaled horizontally easily § requires very little: § HTTP load balancers are ubiquitous. § message queues (like RabbitMQ) make effective load balancers
  15. D E M O R A B B I T

    M Q P I N G P O N G

  17. http sessions? @Starbuxman § Spring Session § useful in a

    PaaS § useful when you need state § useful when you need durable, replicated state § pluggable: Redis out-of-the-box, but feel free to bring your own
  18. D E M O R E D I S -

    B A C K E D H T T P S E S S I O N S
  19. PAAS: 
 P L AT F O R M -

    A S - A - S E RV I C E
  20. why PaaS? @Starbuxman Imagine if architects had to be the

    janitor for every building they designed. This is how the development team felt prior to moving to Windows Azure. Duncan Mackenzie Nov 07, 2011 http://www.infoq.com/articles/Channel-9-Azure “ ”
  21. The Impact of the Cloud @Starbuxman § Spring Boot makes

    it dead simple to stand up services.
 (Where do they live? Who runs them?) § Things get Distributed REALLY quickly! CF provides a way to simplify ! ! § Manifests are are the ultimate installer. 
 (cf push an entire distributed system!) § Spring Cloud PaaS connectors simplify service-consumption > cf push hystrix.jar > cf push …
  22. D E M O S I M P L E

    S C A L I N G O N T H E C L O U D

    code will be open sourced. Z - A X I S D ATA PA RT I T I O N I N G
  24. C A P & N O S Q L

  25. Brewer’s Conjecture (CAP) @Starbuxman Many datastores provide some of the

    following three characteristics: ! ! § Consistency ! § Availability ! § Partitionability 
 ! clarification #1: in a system with no network partitions (such as a single- node RDBMS), then there's no need to sacrifice C & A.! 
 clarification #2: availability is a continuous value: 0-100%. there are many levels of consistency, and even partitions have nuances, including disagreement within the system about whether a partition exists.!
  26. choose the best store for the job @Starbuxman

  27. NoSQL @Starbuxman

  28. S P R I N G D ATA 

    E P O S I TO R I E S
  29. How it Works in Rails @Starbuxman class Car < ActiveRecord

    end car = Car.new cars = car.find_cars_by_id(232) 
 # where did this method come from? # and then magic happens
  30. Using Spring Data Repositories @Starbuxman •Spring Data Neo4J @EnableNeo4jRepositories •Spring

    Data JPA @EnableJpaRepositories •Spring Data MongoDB @EnableMongoRepositories •Spring Data GemFire @EnableGemfireRepositories @Configuration @EnableTransactionManagement @ComponentScan @EnableJpaRepositories( basePackageClasses = BlogRepository.class) public class ServiceConfiguration { ! @Bean public DataSource dataSource(){ .. } @Bean public PlatformTransactionManager transactionManager(){ .. } }
  31. Custom Repository @Starbuxman Keyword Sample Resulting MongoDB Query * GreaterThan

    findByAgeGreaterThan(int age) {"age" : {"$gt" : age}} LessThan findByAgeLessThan(int age) {"age" : {"$lt" : age}} Between findByAgeBetween(int from, int to) {"age" : {"$gt" : from, "$lt" : to}} NotNull findByFirstnameNotNull() {”firstname" : {"$ne" : null}} Null findByFirstnameNull() {”firstname" : null} Like findByFirstnameLike(String name) "firstname" : firstname} (regex)
  32. M O N G O D B

  33. Spring Data MongoDB @Starbuxman § GridFS integration § GIS integration

    § Document mapping
  34. who’s using MongoDB? @Starbuxman § Mailbox.app: https://tech.dropbox.com/2013/09/scaling-mongodb-at-mailbox/ § eHarmony: https://www.mongodb.com/presentations/big-dating-eharmony-0?

    _ga=1.259505294.567221685.1413121358 § Expedia: https://www.mongodb.com/presentations/building-expedia %E2%80%99s-travel-graph-using-mongodb? _ga=1.26276665.567221685.1413121358
  35. D E M O M O N G O D

    B G I S & 
  36. R E D I S

  37. Spring Data Redis @Starbuxman § key/value store § data structures

    § sets § queues § lists § maps § CacheManager implementation § memcached client
  38. who’s using Redis? @Starbuxman § Twitter: http://www.infoq.com/presentations/Real-Time-Delivery-Twitter § Sina Weibo

    http://www.xdata.me/?p=353 § GitHub https://github.com/blog/530-how-we-made-github-fast § Snapchat https://twitter.com/robustcloud/status/448503100056535040 § Pinterest http://engineering.pinterest.com/post/55272557617/building-a-follower- model-from-scratch
  39. C O U C H B A S E

  40. Spring Data Couchbase @Starbuxman § keyed document access § sort

    of like a mix of Redis and MongoDB § horizontally scalable ! @Configuration @EnableCouchbaseRepositories public class Application 
 extends AbstractCouchbaseConfiguration { ! @Override protected List<String> bootstrapHosts() { return Arrays.asList( “" ); } ! @Override protected String getBucketName() { return "default"; } ! @Override protected String getBucketPassword() { return ""; } ! }
  41. who’s using Couchbase? @Starbuxman § AOL: http://www.couchbase.com/ad_platforms § Playtika: http://www.couchbase.com/social-gaming

  42. N E O 4 J

  43. complexity vs performance @Starbuxman

  44. who’s using Neo4j? @Starbuxman

  45. the evolution of search Pre-1999 WWW Indexing Atomic Data 1999

    - 2012 Google Invents PageRank Simple Connected Data 2012-? Google Launches the
 Knowledge Graph Rich Connected Data @Starbuxman
  46. Recommenda)ons @Starbuxman

  47. Graph Search! @Starbuxman

  48. What the Cypher Query Looks Like: @Starbuxman MATCH (person:Person)-[:IS_FRIEND_OF]->(friend), (friend)-[:LIKES]->(restaurant),

    (restaurant)-[:LOCATED_IN]->(loc:Location), (restaurant)-[:SERVES]->(type:Cuisine) ! WHERE person.name = 'Philip' AND loc.location='New York' AND type.cuisine='Sushi' ! RETURN restaurant.name * Cypher query language example http://maxdemarzi.com/?s=facebook
  49. What the Search Looks Like: @Starbuxman

  50. D E M O N E O 4 J T

    W I T T E R
  51. H A D O O P

  52. spring for Surviving the Big Data Wild-West with
 Spring for

    Hadoop @Starbuxman
  53. S P R I N G X D

  54. stream processing, data ingestion & integration But How Do You

    Process Data Realtime? @metamarkets founder Michael E. Driscoll: @Starbuxman
  55. stream processing, data ingestion & integration @Starbuxman Introducing Spring XD

    sources sinks
  56. D E M O S P R I N G

    X D A N D P I V O TA L H D
  57. None

    code will be open sourced. Y- A X I S B O U N D E D C O N T E X T S
  59. micro- vs. monolith… is not a new discussion @Starbuxman From:

    kt4@prism.gatech.EDU (Ken Thompson) Subject: Re: LINUX is obsolete Date: 3 Feb 92 23:07:54 GMT Organization: Georgia Institute of Technology I would generally agree that microkernels are probably the wave of the future. However, it is in my opinion easier to implement a monolithic kernel. It is also easier for it to turn into a mess in a hurry as it is modified. Regards, Ken
  60. hold on a tick.. …didn’t the monolith win? @Starbuxman

  61. so what’s so bad about a monolith? @Starbuxman (does your

    monolith drive you to drink?)
  62. boardroom agility pushes tech agility @Starbuxman § boardroom agility manifest

    in technology: • 2-pizza box teams are a result of eschewing organizational norms ! § easier to scale (in development teams, and at runtime) ! § shorter iterations: • small services > 
 continuous integration > 
 shorter release cycles > 
 deployment automation
  63. the elegant microservice @Starbuxman

  64. problems with microservices @Starbuxman § hard to deploy (devops!) §

    hard to tease into separate deployable modules (Boot!) § lots of moving parts introduces complexity (PaaS & Spring Cloud!)
  65. W H Y B O O T

  66. harder to tease into separate microservices? …No. @Starbuxman import org.springframework.boot.SpringApplication;

    import org.springframework.boot.autoconfigure.EnableAutoConfiguration; import org.springframework.context.annotation.Configuration; import org.springframework.web.bind.annotation.* ! // assumes org.springframework.boot:spring-boot-starter-web on CLASSPATH @Configuration @RestController @EnableAutoConfiguration public class GreetingsController { ! @RequestMapping("/hi/{name}") String hello(@PathVariable String name) { return "Hello, " + name + "!"; } ! public static void main(String[] args) { SpringApplication.run(GreetingsController.class, args); } }
  67. managing many processes with a PaaS @Starbuxman § services are

    explicit about what they bundle § services are attached resources (locally or remote, who cares) § configuration is external § scaling is easy § isolation is provided at the process level
  68. emergent patterns of microservices @Starbuxman § distributed / versioned configuration

    § service registration + discovery § client-side routing, service-to-service calls § load-balancing § minimizing failure cascades § proxies
  69. Standing on the Shoulders of Spring & @Starbuxman

  70. C O N F I G - S E RV

    E R
  71. R E F R E S H - A B

    L E C O N F I G U R AT I O N
  72. S E RV I C E R E G I

    S T R AT I O N & D I S C O V E RY W I T H E U R E K A http://techblog.netflix.com/2012/09/eureka.html
  73. M A N A G I N G FA I

    L U R E S W I T H H Y S T R I X http://techblog.netflix.com/2012/11/hystrix.html
  74. D Y N A M I C R O U

    T I N G W I T H Z U U L http://techblog.netflix.com/2012/11/hystrix.html
  75. Bookmark.. @Starbuxman § The Netflix Techblog http://techblog.netflix.com § Fred Georges

    on Programmer Anarchy 
 http://www.infoq.com/news/2012/02/programmer-anarchy § Matt Stine’s CF + Microservices: a Mutualistic Symbiotic Relationship 
 http://www.youtube.com/watch?v=RGZefc92tZs § Martin Fowler’s article - http://martinfowler.com/articles/microservices.html
  76. Bookmark.. @Starbuxman § Former Netflix DevOps Guru Adrian Cockroft on

    DevOps + MS
 http://www.infoq.com/interviews/adrian-cockcroft-microservices-devops § Bootiful Applications with Spring Boot
 http://http://www.youtube.com/watch?v=eCos5VTtZoI § Chris Richardson’s http://microservices.io site and his 
 Decomposing Applications for Scalability talks § github.com/joshlong/scaling-software-talk
  77. References spring.io/guides github.com/spring-cloud/ github.com/spring-cloud-samples/ github.com/joshlong/spring-doge github.com/joshlong/spring-doge-microservice docs.spring.io/spring-boot/ ! Questions? Josh

    Long @starbuxman jlong@pivotal.io github.com/joshlong (⻰龙之春)