Intelligent integration with WSO2 ESB & WSO2 CEP
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Intelligent integration with WSO2 ESB & WSO2 CEP
- 1. Intelligent Integration with WSO2 ESB and WSO2 CEP Sriskandarajah Suhothayan (Suho) Technical Lead WSO2 Inc.
- 5. Connected Business ! To identity threats & opportunities
- 6. What to integrate ? In the line of Connected Business there will be many software systems • Enterprise Resource Planning systems (ERP) • Management Information Systems (MIS) • Decision Support Systems (DSS) • Data stores • Legacy systems
- 7. Issues in integration But integrating a multitude of complex software applications is not an easy task … • Disparate Systems, Services, Protocols • Diverse and dynamic business requirements • No single vendor/solution • On premise/Cloud solutions • Different QoS requirements • Not everyone adhere to standards
- 8. How could we integrate ?
- 9. How could we integrate ? Point to point connections ?
- 10. How could we integrate ?
- 11. How could we integrate ?
- 12. • Scalability, maintainability, troubleshooting nightmares. How could we integrate ?
- 13. Solution … Bus !
- 14. Solution … Bus !
- 15. Integration Platform Contains … • WSO2 ESB – Connects anything with every thing • WSO2 DSS, WSO2 GReg – Expose data as a service, Repository and governance • WSO2 MB – Guaranteed messaging • WSO2 IS – Security, SSO, Role based access control
- 17. Job Done :)
- 19. The Prevention … ? • Monitor ! • Monitor ! • Monitor !
- 20. The next mile ... ! Monitor and … • Take reactive action • Predict future & take preventive measures • Auto recovery at failure • Optimum resource utilisation • Ensure security & QoS • Alerts
- 21. Is it working as expected ? • The load of the system
- 22. ! It can be The solution should be • A realtime system • Capable of processing data on the fly • Notify alerts in various forms • Enable enterprise integration
- 23. What is ?
- 24. How ESB integrates with CEP?
- 25. How CEP integrates with ESB?
- 26. Patterns • Monitoring Proxy service/API : QoS (E.g Response time) • In Message Store & forward : Managing the Queue • Monitoring Routing : Scaling, Throttling, Blacklisting
- 27. Monitoring QoS on Proxy Services & APIs Monitoring response time
- 28. define stream ResponseTimeStream ( ! ! arrivalTime long, departureTime long, ! ! correlationID string, serviceID string, … ) ;! ! from ResponseTimeStream! ! [departureTime - arrivalTime > 2 min]! select correlationID, serviceID! insert into NotificationStream ; Monitoring QoS on Proxy Services & APIs
- 29. Tuning Message Store and Forward • Monitoring the Message Queue
- 30. Tuning Message Store and Forward define stream QueueStatStream ( queueID string, size long ) ;! ! define partition QueuePartition QueueStatStream.queueID;! ! from a1 = QueueStatStream, ! a2 = QueueStatStream[size >a1. size]+! within 2 min! select a1. queueID as queueID, ! ! a1. size as firstSize,! ! a2[last]. size as lastSize! insert into AdjustForwardingProcessStream! partition by QueuePartition;
- 31. Monitoring ESB routing and Scaling
- 32. Monitoring ESB routing and Scaling define stream RequestStream ( ! ! ! correlationID string, serviceID string, ! ! endpointID string, … ) ;! ! from RequestStream! select count(correlationID) as requestCount, ! ! serviceID, true as scaleUp! group by endpointID! having up requestCount > 90! insert into ScaleUpStream ;
- 34. Throttling & Blacklisting users
- 35. Throttling & Blacklisting users define stream RequestStream ( correlationID string, serviceID string, ! ! ! userID string, tear string, requestTime long, … ) ;! ! define table BlacklistedUserTable(userID string,! ! ! ! ! time long,requestCount long); !
- 36. Throttling & Blacklisting users define stream RequestStream ( correlationID string, serviceID string, ! ! ! userID string, tear string, requestTime long, … ) ;! ! define table BlacklistedUserTable(userID string,! ! ! ! ! time long,requestCount long); ! ! from RequestStream[tear==‘BRONZE’]#window.time(1 min)! select userID, requestTime as time, ! ! count(correlationID) as requestCount! group by userID! having up requestCount > 5! insert into BlacklistedUserTable ;! !
- 37. Throttling & Blacklisting users define stream RequestStream ( correlationID string, serviceID string, ! ! ! userID string, tear string, requestTime long, … ) ;! ! define table BlacklistedUserTable(userID string,! ! ! ! ! time long,requestCount long); ! ! from RequestStream[tear==‘BRONZE’]#window.time(1 min)! select userID, requestTime as time, ! ! count(correlationID) as requestCount! group by userID! having up requestCount > 5! insert into BlacklistedUserTable ;! ! from RequestStream[not(userID in BlacklistedUserTable. userID)] ! select *! insert into ApprovedRequestStream ;
- 38. Patterns • Monitoring Proxy service/API : QoS (E.g Response time) • In Message Store & forward : Managing the Queue • Monitoring Routing : Scaling, Throttling, Blacklisting ! • In Load balancing : Fix out of order arrivals • Rules : Temporal
- 39. Patterns • Monitoring Proxy service/API : QoS (E.g Response time) • In Message Store & forward : Managing the Queue • Monitoring Routing : Scaling, Throttling, Blacklisting ! • In Load balancing : Fix out of order arrivals • Rules : Temporal ! • For Business Decisions : KPIs, Usage patterns, Waiting for the right time.. to trigger • Monitoring for Security : Detect Attacks, Credit card fraud, Login patterns, Blacklisting
- 40. Waiting for the right time … to pull trigger
- 41. Waiting for the right time … to pull trigger define stream StockOrderRequest ( symbol string, ! ! ! ! ! action string ) ;! define stream StockStream ( symbol string, price double, … ) ;! ! define partition Stocks StockStream.symbol, ! ! ! ! ! ! StockOrderRequest.symbol ;! ! from a1 = StockOrderRequest[action==‘buy’],! ! b1 = StockStream[price<a1.price]+,! ! b2 = StockStream[price>b1.price]! select a1.symbol as symbol, ! ! b1[0].price as firstPrice, ! ! b2.price as orderPrice! insert into PurchaseOrder;! partition by Stocks
- 42. Fixing Security Vulnerabilities • Identify Dos attacks • Analyse login patterns • Detect credit card fraud • Blacklisting
- 44. Fixing Security Vulnerabilities define stream PasswordChangeStream (userId string, time long); ! ! define stream TransactionStream (userId string, ! amount double, … ); ! ! from every (a1 = PasswordChangeStream) -> ! a2 = TransactionStream[amount >10000] ! within 1 hour! select a1.userId as userId, a2. amount as amount! insert into PotentialFraud ;
- 45. ! Better three hours too soon, than one minute too late. ! ! ! William Shakespeare
- 46. Thank You Questions ?