soft-shake.ch - Data grids and Data Grids
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This document summarizes a presentation given by Galder Zamarreño on data grids versus databases. The presentation discusses how data grids provide extremely fast access to in-memory data and how they are suitable for scaling applications horizontally. It also covers how data grids can be used to cache database access for improved performance or potentially replace databases for certain use cases requiring high speed and scalability. However, databases are still necessary for many applications and both data grids and databases each have their appropriate use cases.
soft-shake.ch - Data grids and Data Grids
- 1. cocktail d’expérience informatiques Genève 3 & 4 octobre 2011 Seconde édition soft-shake.ch Auteur Galder ZAMARREÑO Track Java Session Data Grids vs Databases
- 3. Tuesday, October 11, 11
- 4. Tuesday, October 11, 11
- 5. Data Grids vs Databases Galder Zamarreño Senior Software Engineer Red Hat, Inc 3rd October 2011, Soft Shake Tuesday, October 11, 11
- 6. Galder Zamarreño • R&D Engineer, Red Hat Inc. • Infinispan developer • 5+ years exp. with distributed data systems • Twitter: @galderz • Blog: zamarreno.com Tuesday, October 11, 11
- 7. Agenda • Why do we need Data Grids? • What are exactly In-memory Data Grids? • Data Grids + Databases • Data Grids without a Database • Can Data Grids replace Databases? Tuesday, October 11, 11
- 8. Traditionally... Store everything in a DB! Tuesday, October 11, 11
- 9. Modern requirements DBs not particularly good at horizontal scaling... Tuesday, October 11, 11
- 10. One size doesn’t fill all! DBs are not bad, but they’re not the solution to every problem either Tuesday, October 11, 11
- 11. Data Grids Tuesday, October 11, 11
- 12. Data Grids are not new Mainstream traction only recent: vertical scaling needs, cheaper memory... and cloud! Tuesday, October 11, 11
- 13. Who’s offering Data Grids? Tuesday, October 11, 11
- 14. The Players • Open Source: • Infinispan, EhCache, Hazelcast... • Commercial: • Oracle Coherence, Gigaspaces, Gemfire, IBM eXtreme Scale Tuesday, October 11, 11
- 15. But, what are In-memory DGs? Tuesday, October 11, 11
- 16. Definition In-memory data structures that offer extremely fast access to data Tuesday, October 11, 11
- 17. Maps are popular! Normally come with a Map-like API, but often come with alternatives Tuesday, October 11, 11
- 18. Data distribution Store data in a subset of the grid to provide failover while being able to scale up! Tuesday, October 11, 11
- 19. With failure in mind Suitable for commodity hardware because they can handle failure Tuesday, October 11, 11
- 20. Elastic Remain available during topology changes Tuesday, October 11, 11
- 21. Durability More durability achieved flushing to a persistent store Tuesday, October 11, 11
- 22. Access patterns Embedded (client and DG in same VM) or Remote (just like DBs) Tuesday, October 11, 11
- 23. ACID or BASE Transactions or Eventual Consistency? Tuesday, October 11, 11
- 24. DGs + DBs? Tuesday, October 11, 11
- 25. Caching! Use Data Grids as caches to enhance Database access performance! Tuesday, October 11, 11
- 26. Can a Data Grid replace a DB? Tuesday, October 11, 11
- 27. Reiterating benefits Speed, scalability, cloud- friendliness...etc Tuesday, October 11, 11
- 28. What are the Data Grid challenges? Tuesday, October 11, 11
- 29. Access patterns Migrating from SQL to Map or alternative APIs not easy Tuesday, October 11, 11
- 30. Skill set Different skill set: OO programmer vs SQL Tuesday, October 11, 11
- 31. Application data layer Data layer to take data collocation into account and do more validation (less strict schema) Tuesday, October 11, 11
- 32. E.g. with a DB... Tuesday, October 11, 11
- 33. Same with Infinispan Tuesday, October 11, 11
- 34. Map/Reduce in detail Tuesday, October 11, 11
- 35. Technology to bridge gap? Tuesday, October 11, 11
- 36. What about JPA? Hibernate OGM (Object/Grid Mapper) uses JPA to store in DGs as opposed to DBs Tuesday, October 11, 11
- 37. Most frequent use cases for DGs? Tuesday, October 11, 11
- 38. Use cases • Analytic systems, i.e. financial/trading apps • XTP • Event driven apps, i.e. CEP • Clustering toolkit Tuesday, October 11, 11
- 39. Do I see DGs as DB replacements? Tuesday, October 11, 11
- 40. DBs are here to stay! No. DBs are proven, mature, well understood plus, there are millions of systems out there! Tuesday, October 11, 11
- 41. One size doesn’t fill all! DBs are not a universal data storage system any more Tuesday, October 11, 11
- 42. Consider Data Grids For their speed, capabilities as data store, and cloud friendliness Tuesday, October 11, 11
- 43. Still some way to go More deployments and standardization (JSR-107, JSR-347) Tuesday, October 11, 11
- 44. Questions infinispan.org - @infinispan speakerrate.com/galder Tuesday, October 11, 11