Manik Surtani is the founder and project lead of Infinispan, an open source data grid platform. He discussed data grids, NoSQL, and their role in cloud storage. Data grids evolved from distributed caches to provide features like querying, task execution, and co-location control. NoSQL systems are alternative data storage that is scalable and distributed but lacks relational structure. JSR 347 aims to standardize data grid APIs for the Java platform. Infinispan implements JSR 107 and will support JSR 347, acting as the reference backend for Hibernate OGM.

1. Infinispan
Data Grids, NoSQL, Cloud Storage & JSR-347
Manik Surtani
Founder and Project Lead, Infinispan
Red Hat, Inc.

2. Who is Manik?
• Hacker@JBoss, Red Hat’s middleware division
• Founder and Project Lead, Infinispan
• Spec lead, JSR 347
•Data Grids for Java
• EG representative, JSR 107
•Temporary Caching for Java
• http://blog.infinispan.org
• http://twitter.com/maniksurtani

3. Agenda
• A brief introduction to Infinispan
• Understanding Data Grids
• .. and NoSQL
• Their role in Cloud Storage
• JSR 347 and related standards

5. What is Infinispan?
• An open source data grid platform
• Written in Java and Scala
• Not just for the JVM though
• Distributed key/value store
• Transactional (JTA)
• Low-latency (in-memory)
• Optionally persisted to disk
• Feature-rich

6. P2P Embedded Architecture

7. Client/Server Architecture
Supported
Protocols
• REST
• Memcached
• Hot Rod

8. WTF is Hot Rod?
• Wire protocol for client server
communications
• Open
• Language independent
• Built-in failover and load
balancing
• Smart routing

9. Server Endpoint Comparison
Protocol Client Clustered? Smart Load Balancing/
Libraries Routing Failover
REST Text N/A Yes No Any HTTP load
balancer
Memcached Text Plenty Yes No Only with
predefined
server list
Hot Rod Binary Java, Yes Yes Dynamic
Python,
Ruby

10. Understanding
Data Grids

11. Data Grids.
What Are They?
An evolution of
distributed caches

12. Why use distributed caches?
• Cache data that is expensive to retrieve/calculate
• E.g., from a database
• The need for fast, low-latency data access
• Performance or time-sensitive applications
• Very commonly used in:
• Financial Services industry
• Telcos
• Highly scalable e-commerce

13. Data grids as clustering toolkits
• To introduce high availability
and failover to frameworks
• Commercial and open source
frameworks
• In-house frameworks and
reusable architectures
• Delegate all state
management to the data
grid
• Framework becomes
stateless and hence elastic

14. But
Data Grids > Distributed Caches
• Querying
• Task execution and map/reduce
• Control over data co-location

15. Understanding
NoSQL

16. What is NoSQL?
• An alternative form of typically disk-based data
storage
• Free from relational structure
• Usually key/value or document-based
• Allows for greater scalability and easier
clustering/distribution

17. NoSQL and Consistency

18. NoSQL and Consistency
• BASE not ACID
• Relax consistency in exchange for high availability
and partition tolerance
• Usually eventually consistent
• Which means applications need to be designed with
this in mind

19. NoSQL and Consistency

20. Data Grids and NoSQL
used as
Cloud Storage

21. Cloud Storage
• Traditional mechanisms (RDBMSs and file
systems) are hard to deal with
• Clouds are ephemeral
• All cloud components are expected to be:
• elastic
• highly available

22. Cloud Storage
•Data grids and NoSQL win over traditional
storage mechanisms in the cloud
• Data grids and NoSQL are fast converging in
feature sets
• E.g., Data grids can write through to disk; many
NoSQL engines would also cache in memory

23. JSR 347

24. JSR 347
Data Grids for the Java Platform
• A new JSR for proposed inclusion in Java EE 8
• to make enterprise Java more cloud-friendly
• Standardize data grid APIs and behavior for the
Java platform
• Does not define NoSQL
• Data grids primarily used from within a JVM
• NoSQL primarily used via client connectors over a
socket
• Standardizing wire protocols beyond the scope of the JCP

25. JSR 347
Data Grids for the Java Platform
• Extends JSR 107 (Temporary Caching for Java)
• Adds:
• Asynchronous, non-blocking API
• Grouping API to control co-location
• Distributed code execution and Map/Reduce APIs
• Eventually consistent API
• Possibly more
• Still very much work in progress
• Participate!

26. Related standards and efforts
• JSR 107
• A temporary caching API that defines:
• Basic interaction
• JTA compatibility
• Persistence: write-through and write-behind
• Listeners

27. Related standards and efforts
• Hibernate OGM
• JPA for key/value stores!
• Common and familiar paradigm for persisting data
• Except persistence is made to a data grid or NoSQL store

28. Related standards and efforts
• Contexts and Dependency Injection
• Interaction with caches defined in JSR 107
• Familiar and well proven programming model
• Works well with JPA and hence Hibernate OGM
• Works well even for direct access to key/value data
grids

29. Where does Infinispan fit in?
• Will implement JSR 107
• Currently implements most of this at least in concept
• Will implement JSR 347
• Currently serves as a “donor” for most of JSR 347
features and API
• Is already the reference backend for Hibernate
OGM
• Already supports CDI integration

30. To Summarize
•What data grids and distributed caches are
•Where NoSQL came from and main differences
between NoSQL and data grids
•Cloud storage challenges
•JSR 347: Data Grids for the Java Platform
•Infinispan and where it sits in all this

31. Questions &
More Info
• http://github.com/datagrids/spec/wiki
• http://groups.google.com/group/jsr347
• http://twitter.com/jsr347
• http://www.infinispan.org
• http://twitter.com/infinispan
• http://hibernate.org/subprojects/ogm.html