This document summarizes a presentation about data grids versus databases. It discusses how data grids provide extremely fast in-memory access to distributed data and can be used for caching to improve database performance. While data grids offer benefits like scalability, their use requires a different programming model than databases. They may replace databases for some use cases like analytics but databases will remain important for their maturity and existing implementations. Data grids are best viewed as complementing rather than replacing databases.

1. Thursday, November 3, 11

2. Thursday, November 3, 11

3. Data Grids vs
Databases
Galder Zamarreño
Senior Software Engineer
Red Hat, Inc
3rd October 2011, Soft Shake
Thursday, November 3, 11

4. Galder Zamarreño
• R&D Engineer, Red Hat Inc.
• Infinispan developer
• 5+ years exp. with distributed data systems
• Twitter: @galderz
• Blog: zamarreno.com
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5. 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?
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6. Traditionally...
Store everything in a DB!
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7. Modern requirements
DBs not particularly good at
horizontal scaling...
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8. One size doesn’t fill all!
DBs are not bad, but they’re
not the solution to every
problem either
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9. Data Grids
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10. Data Grids are not new
Mainstream traction only
recent: vertical scaling needs,
cheaper memory... and cloud!
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11. Who’s offering
Data Grids?
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12. The Players
• Open Source:
• Infinispan, EhCache, Hazelcast...
• Commercial:
• Oracle Coherence, Gigaspaces, Gemfire,
IBM eXtreme Scale
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13. But, what are
In-memory DGs?
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14. Definition
In-memory data structures that
offer extremely fast access to
data
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15. Maps are popular!
Normally come with a Map-like
API, but often come with
alternatives
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16. Data distribution
Store data in a subset of the
grid to provide failover while
being able to scale up!
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17. With failure in mind
Suitable for commodity
hardware because they can
handle failure
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18. Elastic
Remain available during
topology changes
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19. Durability
More durability achieved
flushing to a persistent store
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20. Access patterns
Embedded (client and DG in
same VM)
or Remote (just like DBs)
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21. ACID or BASE
Transactions or Eventual
Consistency?
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22. DGs + DBs?
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23. Caching!
Use Data Grids as caches to
enhance Database access
performance!
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24. Can a Data Grid
replace a DB?
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25. Reiterating benefits
Speed, scalability, cloud-
friendliness...etc
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26. What are the Data
Grid challenges?
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27. Access patterns
Migrating from SQL to Map or
alternative APIs not easy
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28. Skill set
Different skill set:
OO programmer vs SQL
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29. Application data layer
Data layer to take data
collocation into account and
do more validation (less strict
schema)
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30. E.g. with a DB...
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31. Same with Infinispan
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32. Map/Reduce in detail
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33. Technology to
bridge gap?
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34. What about JPA?
Hibernate OGM (Object/Grid
Mapper) uses JPA to store in
DGs as opposed to DBs
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35. Most frequent use
cases for DGs?
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36. Use cases
• Analytic systems, i.e. financial/trading apps
• XTP
• Event driven apps, i.e. CEP
• Clustering toolkit
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37. Do I see DGs as DB
replacements?
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38. DBs are here to stay!
No. DBs are proven, mature,
well understood plus, there are
millions of systems out there!
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39. One size doesn’t fill all!
DBs are not a universal data
storage system any more
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40. Consider Data Grids
For their speed, capabilities as
data store, and cloud
friendliness
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41. Still some way to go
More deployments and
standardization (JSR-107,
JSR-347)
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42. Questions
infinispan.org - @infinispan
speakerrate.com/galder
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