NT/e's Presentation on Scalable Cloud Transaction & ORM at the GigaSpaces CloudCrowd Evening Gathering in London - November 2009.

1. 1 Matthew Fowler, NT/e CloudSave CloudTran Scalable Transactions in the Cloud

2. 2 ? And the answer is platform for mainstream Java developers to use IMDG for scalable, commercial applications without worry and minimal hassle for commercial advantage It's a lump of middleware built on, adding value to GigaSpaces

3. 3 2001 WebLogic/J2EE specialisation One week training course 4-point architecture for dummies Messed-up architecture revenue down Automating server-side applications J2EE/EJB Spring/Hibernate

4. 4 3-5...5-10...10-20...1,000,000 Tracy's story: the path of successful apps Database Caching In-memory Data Grid The 50,000 club Application scale drivers Mobile phone growth, iPhone Apps Micropayments e-commerce continued growth

5. 5 Get an edge with performance “Latency really matters ... 100ms of latency costs 1% in sales.” Amazon “An extra 0.5 seconds in search page generation time dropped traffic by 20%.” Google Please wait ..................... “... almost half of visitors will abandon a site if they perceive a page or feature takes longer than 2 seconds to load. ” GetElastic

6. 6 6.5m, x10yrs, $400bn/yr Mainstream Java developers 6.5m most have 5-10 years experience 50 million man-years experience Plain old application development market $400bn/year Can they build an IMDG application? How can IMDG go mainstream?

7. 7 Explaining it to your Mom / Boss IMDG - SOR Persistent Storage

9. sleeping at night.Catching the money: ACID transactions throughput, scalability, bullet-proof reliability distributed, data + messaging ORM - Object references, not foreign keys.Easy to program. Entity groups for performance.

10. 9 In-Memory Data Bases - Are You Crazy? What's it worth: Loss of sales, traffic - 5% vulnerable, saved by speed of IMDG For $100m/year co: $5m/year revenue for good behaviour Customer/order/product data - 2million * 16Kb 8 servers in grid for 32GB live data 8 servers isn't a lot Worth doing the numbers!

11. 10 Distributed Transactions Low Reliability Complicated Programming of Unknowing Unintended Consequences Fear and loathing ...

12. 11 , 1, 2, 3, ...  Other alternatives forget transactions, forget databases Dan's the Man GoogleApps on V2 last we heard

13. 12 How is it possible? Distributed Cloud Transactions Redefining the problem Grid connected Helland's get out clause System of Record is in the grid No voting - 1PC not 2PC Commit to backed-up memory Leverage the GigaSpaces platform SBA/Entity Groups, Transactions, SQL Queries, Backups

14. 13 200/ ... 2,000 ... 20,000/second

15. 14 Transactions you can count on

16. 15 Transactions you can count on

17. 16 Herding Cats - Java Style How to distribute data How to find it How to resolve references IMDG versus user view: FK ↔ OO Atomicity on failure Timeouts Scalability Consistency and isolation

18. 17 The 'T' Word GigaSpaces Local Transactions GigaSpaces Distributed Transactions Mirror service see Cat-Herding 101

19. 18 How CloudTran ORM works Partitioning (entity groups) Client Gridsearch OL Order Service Commit Data Data TxB Commit Commit Confirm Confirm Tx Messaging Datasources

20. 19 300 .. 700 .. 900 .. 2,100 Performance of transaction buffer Tiny Transactions per second

21. 20 Virgin Airways LastMinute.com IMDG In-Cloud Federated Applications IMDG CloudTran -FederatedTransaction Buffer

22. 21 Scalable transactions in the cloud? platform for mainstream Java developers to use IMDG for scalable, commercial applications without worry and minimal hassle for commercial advantage GigaSpaces CloudTran

23. 22 End