This document introduces Infinispan, an open source distributed in-memory data grid. It provides high availability and elasticity through data replication across nodes. The API allows developers to treat Infinispan as a distributed concurrent hash map. Key features include transactions, persistence, querying, and map/reduce capabilities. Infinispan can be used as a local cache, cluster of caches, or autonomous data store accessed via protocols like REST and Memcached. Future plans include improvements to transactions, non-blocking state transfers, and support for cross-datacenter replication and eventual consistency.

1. Introducing Infinispan
• Mircea Markus
• Senior Software Engineer
• Red Hat, Inc

2. Mircea Markus
• R&D Engineer, Red Hat Inc.
• Twitter: @mirceamarkus
• Blog: mirceamarkus.blogspot.com

3. Agenda
• What is Infinispan
• API
• Key features
• Three use cases
• The path ahead

4. What is Infinispan?
• Distributed, in memory, data structure
• Highly available
• Elastic
• Open source

5. Distributed data structure

6. High availability
• Memory is volatile
• Make redundant copies
• Total replication (Replication Mode)
• Partial replication (Distribution Mode)
• Topology changes
• Node will crash!
• Re-arrange state

7. Elasticity
• Expect
• Node additions
• Node removals
• Topology changes
• are totally consistent
• do not "stop the world"

8. API
• Key-value store
• java.util.concurrent.ConcurrentHashMap
• JSR-107 compliant
• CDI support

9. Key features
• Transactions
• Persistence
• Map/Reduce
• Querying

10. Transactions
• JTA transactions support
• XA or Synchronization based enlistment
• recovery!
• Local transactions (batching)
• Transactional modes
• optimistic
• pessimistic

11. Persistence/Cache Store
• Used for
• durability
• increased storage
• warm caches
• Various implementations
• jdbc, bdbje, file, cloud, remote/infinispan, cassandra
• Extensible

12. Map/Reduce example

13. Map/Reduce

14. Map/Reduce

15. Querying
• What’s in C7?
• Where is the white king?

16. Querying
• Based on Hibernate Search/Lucene
• Objects in the grid are indexed
• index management

17. Use cases
• Local cache
• Cluster of caches
• Autonomous data store
• access protocols

18. Local cache

19. Features of a local cache
• Eviction
• Expiry
• Write through, write behind
• Preloading
• Notifications
• Statistics

20. Local cache not good enough...

21. Cluster of caches

22. Some limitations still
• Client is affected by cache topology changes
• Shared resources
• Tier management
• incompatible JVM tuning
• security
• garbage collection
• Non-JVM clients

23. Cache servers

24. Client/Server protocols
• REST
• Memcached
• Hotrod
• proprietary
• java, python, ruby

25. The path ahead
• 5.2
• transactions performance
• non-blocking state transfer
• hotrod enhancements
• 6+
• X-datacenter replication
• eventual consistency
• continuous queries

26. Thank you!
• infinispan.blogspot.com
• @infinispan
• mirceamarkus.blogspot.com
• @mirceamarkus