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Migrating JEE Apps to GigaSpaces Case Analysis – Spring PetClinic

Migrating JEE Apps to GigaSpaces Case Analysis – Spring PetClinic. Uri Cohen, Product Manager Sept. 24 th , 2008. Agenda. Background & Motivation What’s In an App Server Migration in Practice Messaging Data Access Web front end Architectural Considerations Live Demo. Background.

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Migrating JEE Apps to GigaSpaces Case Analysis – Spring PetClinic

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  1. Migrating JEE Apps to GigaSpacesCase Analysis – Spring PetClinic Uri Cohen, Product Manager Sept. 24th, 2008

  2. Agenda • Background & Motivation • What’s In an App Server • Migration in Practice • Messaging • Data Access • Web front end • Architectural Considerations • Live Demo

  3. Background • GigaSpaces as an application server • Previous projects to map the migration efforts and gaps

  4. Motivation • Feasibility study, learn about: • User experience (migration complexity) • Gaps between JEE & GigaSpaces • Use that as input for next releases planning • Share outcome with user community • Provide a common basis for compatison • “Here’s how it should be done with GigaSpaces”

  5. What’s In an App Server? • Data access • Messaging / event processing • Remoting • TX management • Web

  6. Migration in Practice • Messaging / event processing • Replace MDBs with GigaSpaces event listeners • Remoting • Replace SLSBs with GigaSpaces SVF (Remoting/Executors) • Data access • Use GigaSpaces 2nd cache for Hibernate • Convert your DAOs to use GigaSpaces, use mirror to persist • TX management • Use Spring… • Web • Use GigaSpaces web processing unit • Use GS HTTP session replication

  7. Migration in Practice

  8. Converting Your Messaging Layer • Convert you MDBs to GigaSpaces event containers • You can use IMessageConverter on the client side • You can use annotations / XML • Considerable performance boost • Without affinity: up to 3 times faster • With affinity: up to 6 times faster

  9. Converting Your Messaging Layer

  10. Converting Your Messaging Layer

  11. Converting Your Remoting Layer • Usually implemented by SLSBs with JEE • No or minor code changes needed • You get: • Location transparency • Dynamic service discovery • Synchronous or asynchronous invocation • Cluster wide invocations (Map/Reduce) • Data affinity • Automatic failover

  12. Converting Your Remoting Layer • Service Interface • Client: • Server:

  13. Converting Your Data Access Layer • Currently this is not effortless • 1st step: Domain model • Dealing with relationships • Need to change domain model • Add foreign key fields, update setter, O/R mappings (if you use mirror) • No lazy loading • Load on a per case basis, might mean you should change the DAO • No object navigation and aggregations in queries • Need to query the space multiple times and aggregate

  14. Converting Your Data Access Layer • 2nd Step: Partitioning • You should determine how objects will be partitioned • May involve duplicating parent routing fields to nested objects • 3rd Step: Changing the DAO • Naive approach: Convert all JDBC/Hibernate calls to space calls • Better approach: Use Remoting/Executors, take advantage of data affinity and collocation

  15. Converting Your Data Access Layer • Other issues: • Non String IDs: need to create cluster-wide ID generator • No dirty checks: similar to lazy loading, need to persist on a case-by-case basis • Semantics: • When saving with Hibernate/JPA, generated ID is injected into the object • Solve at the DAO level

  16. Architectural Considerations • Web app & space in the same PU • Partitioned or replicated? • Is it really collocation? • No dynamic scaling • Web app & space in separate PUs • Separate scaling models – more flexible • No collocation • Local cache can help

  17. Let’s See It in Action

  18. Future Roadmap (Suggestions) • Object navigation in queries • user.address.street=’Oxford’ • Native aggregate functions • max, min, avg, std, custom • Relationships • JPA support • Embedded EJB container?

  19. Q&A

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