GemFire is an in-memory data grid that pools memory across processes to manage application objects and behavior like caching, querying, transactions, and event notifications. It uses a shared-nothing architecture to allow for linear scalability and high availability. Data can be stored in replicated or partitioned regions. GemFire supports operations like read-through, write-through, and write-behind caching to integrate with backend data sources. It also allows querying data and continuous querying of events, as well as running application functions on members and data sets. An example broker application is discussed that could use GemFire for high availability, parallel aggregation, and scalability.

1. GemFire: In-Memory Data GridSeptember 8th, 2011

2. Typical applicationClientApplication TierData Base2

3. Is it easy to scale Data Base?New users means, more application servers and more load to database.Application TierClientsData Base3

4. Moore's law: The number of transistors doubles approximately every 24 monthsWhat about data? 90% of today’s data were created in the last 2 yearsWeb logs, financial transactions, medical records, etc4

5. “Hardware can give you a generic 20 percent improvement in performance, but there is only so far you can go with hardware.”Rob Wallos,Global Head of marketing data Citi5

6. What is latency?Latency – is the amount of time that it takes to get information from one designated point to another.6

7. Why worry about it?Amazon - every 100ms of latency cost them 1% in salesGoogle - an extra 0.5seconds in search page generation time dropped traffic by 20%Financial - If a broker's electronic trading platform is 5ms behind the competition it could loose them at least 1% of the flow - that's 4$ million in revenues per ms.7

8. How to make data access even fast?Distributed Architecture

9. Drop ACID

10. Atomicity

11. Consistency

12. Isolation

13. Durability

14. Simplify Contract

15. Drop Disk8

16. Data GridData Grid is the combination of computers what works together to manage information and reach a common goal in a distributed environment.9

17. Shared nothing architectureIs a distributed computing architecture in which each node is independent and self-sufficient, and there is no single point of contention across the system.Popularized by BigTable and NoSQL

18. Massive storage potential

19. Massive scalability of processing10

20. In-Memory Data GridData are stored in memory, always available and consistent.Low Latency

21. Linear Scalability

22. No Single Point of failure

23. Associate arrays

24. Replicated

25. Partitioned11

26. GemFireThe GemFire is in-memory distributed data management platform that pools memory across multiple processes to manage application objects and behavior.Caching

27. Querying

28. Transactions

29. Event Notification

30. Function Invocation12

31. CAP TheoremOnly two of these three desirable properties in distributed system can be achieved:Consistent

32. Available

33. Partition-Tolerant13

34. RegionsData region is a logical grouping within a cache for a single data set.A region lets you store data in many VMs in the system without regard to which peer the data is stored on. Work similar to Map interface.14

35. Region ExampleCache cache = new CacheFactory().set("cache-xml-file", "cache.xml”).create();CacheServercacheServer = cache.addCacheServer();cacheServer.start();Regionpeople = cache.getRegion(”people");people.put(“John”, john);<cache> <regionname="people"> </region> </cache>Create Cache Server