[RakutenTechConf2013] [B-3_1] Challenge of Rakuten Ichiba
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[RakutenTechConf2013] [B-3_1] Challenge of Rakuten Ichiba

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Rakuten Technology Conference 2013

Rakuten Technology Conference 2013
"Best Practices of Scale-up and non-stop for Legacy System ~Challenge of Rakuten Ichiba"
Noboru Ochiai (Rakuten)

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[RakutenTechConf2013] [B-3_1] Challenge of Rakuten Ichiba [RakutenTechConf2013] [B-3_1] Challenge of Rakuten Ichiba Presentation Transcript

  • Best Practices of Scale-up and non-stop for Legacy System ~Challenge of Rakuten Ichiba Vol.01 Feb/26/2013 Noboru Ochiai Japan Ichiba Section Rakuten Ichiba Development Department, Rakuten Inc. http://www.rakuten.co.jp/
  • Who are we?  Noboru Ochiai Japan Ichiba Section Rakuten Ichiba Development Dept. Rakuten, Inc. https://www.facebook.com/imonikai →Let’s join it!! 11/16(Sun) 2
  • Challenge for out of Legacy System Challenging!! 3
  • Rakuten’s Business Eco-System Various Services can be used by one-stop place through the Internet ・Shopping Mall ・Auction Today’s target ・Advertising ・Streaming ・Downloading ・Recruitment ・Marriage E-Commerce Business Portal/Media Business Business Business Application Application Media ・Credit Card Business Business Credit and Application Application Payment Business Rakuten ID Membership Database Banking Business Business Business Application Application Business Business Travel Application Application Business ・Hotels and Facilities ・Air Tickets ・Dynamic Packages Business Business Telecommunications Application Application Business ・Payment ・Consumer Loan Securities Business Business Business Application Application ・Fixed IP Telephony ・Mobile IP Telephony ・Stocks ・Bonds ・Trust Founds ・FX 4
  • Scale of Rakuten Ichiba Merchants :42,000+ Items :1.7 billion + Registered Members Gross :78 mil + Merchant Sales : 1.1tri. JPY(2013) ≒12.5 bil. USD 5
  • Rakuten Ichiba Service Overview  RMS (Rakuten Merchandise System) – Applications for Merchant . – Applications for Consumer. Data analyze for internal R-backoffice R-storefront GMS(Gross Merchant merchant sales) Reporting DB R-Navigation Consumer R-Page html DWH R-checkout 6
  • Rakuten’s Issue Go to “become the No. 1 Internet services company in the world” But… SYSTEM STRUCTURE… 7
  • Problem of Our System Application having complicated dependence.. 8
  • Problem of Our System System stops with once a month 11times in a year Call “Regular maintenance” (added oyastu) 9
  • Detail of our problem R-backoffice R-storefront Order api Application APP synchronous process Inventory DB Application APP APP View LOGIC High load synchronous process LOGIC DAO DAO Dependence logic orderDB  Synchronous at DB  Heavy depend on application  High load DB R-checkout Checkout api APP Application APP View Dependence logic LOGIC LOGIC DAO DAO 10
  • Resolve an issue ~step 1 R-backoffice R-storefront Order api Application APP synchronous process Inventory DB Application APP APP View LOGIC LOGIC DAO DAO Dependence logic High load synchronous process orderDB R-checkout Checkout api Non stop Checkout APP Application APP View Dependence logic LOGIC LOGIC DAO DAO 11
  • Detail of Resolve an issue ~step1 After Before Application Application APP APP State management View View LOGIC DAO Dependence logic Checkout api synchronous process APP LOGIC APP APP LOGIC Checkout api loosely-coupled logic DAO APP DAO State management 2PhaseCommit Order DB UI UI Application Application Inventory DB kvs solution asynchronous process Inventory DB Order DB : Queue 12
  • Resolve an issue ~step 2 R-backoffice R-storefront Order api Application APP synchronous process Inventory DB Application APP APP View LOGIC synchronous process High load orderDB Easily Scale up at Backoffice kvs solution LOGIC DAO DAO Dependence logic R-checkout Checkout api APP Application APP LOGIC View DAO 13
  • Detail of Resolve an issue ~step2 Before After Complicated search conditions Complicated search conditions many update order status many update order status View View Application Application LOGIC DAO APP Order api Order api LOGIC DAO APP synchronous process APP Checkout api APP Order DB High load LOGIC DAO APP From checkout kvs solution loosely-coupled logic Data cache From checkout kvs solution kvs solution asynchronous process Checkout api APP Order DB Decrease load 14
  • Resolve an issue ~step 3 R-backoffice R-storefront Order api Application APP synchronous process Inventory DB APP APP synchronous process Application LOGIC View DAO kvs solution orderDB kvs solution Realize 24/7 R-checkout Checkout api APP Application APP LOGIC View DAO 15
  • Detail of Resolve an issue ~step3 After Before Application UI UI ApplicationApplication APP APP APP Application UI UI ApplicationApplication APP APP APP Checkout api Checkout api APP APP synchronous process Inventory created api for inventory update api APP invetory DB asynchronous process invetory DB kvs solution 16
  • After Resolving our Problem R-backoffice R-storefront Application Inventory DB kvs solution APP Application Order api APP APP View LOGIC inventory api DAO 5years From DB  Asynchronous at 2009 to 2013 APP kvs solution R-checkout  Loosely-coupled Logic  Decrease load at DB Checkout api APP orderDB kvs solution Application APP LOGIC View DAO : Queue 17
  • Summary  Our system make possible to scale up easily, so we can catch up with business growth rapidly.  we can do system maintenance easily and easily modify/delete application dependencies. Reduction To Improve a system.. We had chosen coherence. kvs solution = 18
  • Last message Challenging!! 19
  • Thank you for your listening Thank You For Listening!! 20