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AWS Customer Presentation - How Runa uses AWS

AWS Customer Presentation - How Runa uses AWS



Robert Berger discusses how Runa uses AWS at the AWS Startup Tour - SV - 2010

Robert Berger discusses how Runa uses AWS at the AWS Startup Tour - SV - 2010



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AWS Customer Presentation - How Runa uses AWS AWS Customer Presentation - How Runa uses AWS Presentation Transcript

  • Runa on AWS
    Big Data & Machine Intelligence for a SaaS Startup
  • Runa
  • aSaaS
  • convertsShoppers to Buyers
  • forOnline Commerce Sites
  • by presentingDynamic Personalized Promotions
  • on theMerchant’s Website
  • inReal-Time
  • in the Shopping Flow
  • Tech Challenges
  • Big Data
  • JavaScript client collects activity on every Merchant page for every Shopper
  • One or more Ajax call & Event Store to Runa per Merchant page view
  • Step function increase of calls and stores as each new Merchant added
  • We capture everything we can and store it forever
  • Expecting to grow to thousands of merchants
  • That’s a lot of Data
  • Processing Data withMachine Intelligence
  • Batch Processing forStatistical Analysisand Reports
  • Real-Time Rule based inserts of Promotions
  • Tech Challenges Synopsis
    Big Data & Processing
    Step Function Growth
    Batch Processing
    Real-Time Promotions
  • Why AWS for Runa?
  • At First(a couple years ago)
  • Not Much Money in the Bank
  • Didn’t Know exactly what were making
  • Or exactly how we were going to do it
  • Prototyped with Ruby / Rails / MySQL
  • ThenPrototype became Production
  • EC2 & AWS let us scale the prototype to Beta Production
  • Flexibility to incrementally refine service & infrastructure
  • Confidence we could scale as we added Merchants
  • More RecentlyIncrementally added next-gen Tech & Full Production
  • Goal: Everything Horizontally Scalable
  • Batch Processing & Infinite StorageMap / Reduce& BigTable viaHadoop & HBase
  • Flexible Real-Timeparallel processingvia Clojure / Swarmiji
  • Deployment & Configuration ManagementviaOpscode Chef
  • Good Things
  • Able to Start Small
  • Having the flexibility to throw “Hardware” at our Prototype got us to market faster
  • Ability to launch test and staging environments almost at will
  • “Hardware” as “Software”
  • Living in “interesting” times
  • Managing Complexitylots of moving parts
  • Easy to launch a few instances
  • Impossible to manage horizontal stacks“by hand”
  • Must have tool like Opscode Chef
  • Chef automates deployment & puts it under Revision Control
  • There’s going to be some blood when using cutting edge tech
  • Lots of Learning Curves to climb
  • Useful Monitoring is hard but Critical
  • HBase on AWS may be dangerousbecause of Hadoop namenode SPOF
  • EC2 bill can surprise you if you cavalierly deploy multiple versions of horizontally scalable environments
  • Could not do our startup without AWS or lots more VC Funding