This talk show how Spring technologies can help to develop applications for the cloud. PaaS like Google App Engine, Amazon Beanstalk, Cloud Bees and Cloud Foundry are shown as well as other technologies such as NoSQL, RabbitMQ and Hadoop.

1. Spring in the Cloud

2. About me
• Eberhard Wolff
• Architecture & Technology Manager at adesso
• adesso is a leading IT consultancy in Germany
• Speaker
• Author (i.e. first German Spring book)
• Blog: http://ewolff.com
• Twitter: @ewolff
• eberhard.wolff@adesso.de

3. Content
• What is the Cloud?
• My First Cloud App
• Scaling the App
• Another Cloud Challenge
• Java EE 7
• Conclusion

4. What is the Cloud?
Infrastructure Platform Software
as a Service as a Service as a Service
> Virtual Servers > Virtual Application Server > Software or Service
that you use
> Similar to Virtualization > Handles Scale-Out
> Components that you
> Manage Everything > Mostly Managed by add/integrate into your
Yourself Provider app

5. Why Cloud?
• Compelling Economics
– Lower CapEx
– Cheaper handling of peak loads
• Flexibility and better productivity for
development
– Self service portal
– Much easier to set up environments
• Better Service
– Redundancy across multiple data centers
– Lower latency because of local data centers

6. So: What Shall I use?
• IaaS: Maximum control, but:
– Need to install infrastructure
– Need to install application
– How do you do application updates?
• PaaS: Much easier to handle
– …but often less flexible
– Monitoring, deployment etc built in
– Probably the better choice

7. Google App Engine
• Non relation data store (NoSQL)
• (Limited) JPA interface to data store
• Available Java classes limited (White List)
• No control over web server, operating system
• Strategic partnership SpringSource / Google
• Spring works

8. Amazon Beanstalk
• Tomcat + OpenJDK + Linux on EC2
• + Monitoring, Versioning, Events…
• Auto Scaling
• Can add MySQL (RDS) with backup etc
• Sample application uses Spring

9. Cloud Bees
• Focus on DEV@Cloud
– Continuous Integration
– Maven Repository
• RUN@Cloud: Tomcat+OpenJDK + Linux on
EC2
• Announce beta for Private Cloud
• MySQL also available
• Very limited scaling / SLAs
• Spring works

10. VMware Cloud Foundry
• Tomcat on VMware Public Cloud or Micro
Cloud (i.e. laptop) or EC2 or …
• Platform is Open Source
• Can add MySQL, Redis, MongoDB
• Services injected into application
• Framework support needed
• Spring: A MySQL DataSource is rewired to a
MySQL service

11. Cloud Foundry Spring Integration
<cloud:data-source id="dataSource"/>
<cloud:mongo id="mongo"/>
<cloud:redis-connection-factory
id="redisConnectionFactory"/>
• Cloud namespace: bind to Cloud Foundry
services
• Works with Spring 3.0.5
• Note: No need for environment specific config
• App can run unchanged in test / dev /
production Cloud Foundry environments

12. PaaS Conclusion
• Spring (+ Tomcat) own the Cloud
• Also predominant non-Cloud Enterprise
platform
• Lightweight – more important for Cloud
– Pay any consumed resource
– PaaS: Need operations / monitoring / deployment
– Easier for a simple environment
• Spring has always been about portability
• Not just Java EE / Tomcat / … - also Cloud

13. Full Java EE Stack?
• Spring & Tomcat:
The Enterprise & Cloud Java dream team
• Of course can also use other Java EE APIs
on Tomcat
• There is no offering for a full Java EE PaaS
stack
• i.e. including EJB, JMS, JTA …

14. My First Cloud App
Tomcat
Catalog
Order
RDBMS

15. My First Cloud App: Platform
• Programming model
Same as for Enterprise Java Apps
• E.g. JSF + Spring + JPA …
• Run on IaaS: Need to install infrastructure
– Must be automated
• Google App Engine
– JSF probably won’t work
– Spring and GWT will
– No RDBMS - but NoSQL with (limited) JPA

16. My First Cloud App: Platform
• Amazon Beanstalk
Cloud Bees
Cloud Foundry
– Environment you are used to: Tomcat + MySQL
– Cloud Foundry: Optional cloud namespace in the
configuration
• For a start a normal Spring application is OK
• What are the limits?

17. How Is Cloud Different?
• Need to be able to add new servers
• Provided for by PaaS
• New servers must be able to handle load
instantly (warm cache)
• Prefer Scale Out instead of Scale Up
• i.e. add new servers instead of bigger ones
• Usually not an option with RDBMS
• Scale up is possible but limited

18. How Is Cloud Different?
• Complex networks setup
• Nodes might be unavailable
• Might want to run cluster across data center
for availability
• Server might and will fail
• Very different from normal servers

19. How Is Cloud Different?

21. So How Is Cloud Different?
• No operations department to blame
• Unreliable and potentially slow network
• How can you even dare to build reliable
systems that way?
• Amazon and Google accomplish that
• Will happen in Enterprises as well
• Much more efficient in operations /
development

22. So How Is Cloud Different?
• No operations department to deal with – just
a portal!
• Multiple world-wide distributed data centers
available to you!
– Redundancy
– Low latency from any place
• New virtual computers spun up in minutes!
• Need a larger machine – just reboot!

23. Cloud is Just Another Trade Off
• How can applications be successfully created
in this environment?

24. CAP Theorem
• C: Consistency: All nodes see the same data
• A: Availability: Node failure does not prevent
survivors from operating
• P: Partition Tolerance: System works even
though message are lost
• Choose any two
• No P in the Cloud?
• So A or C?

25. Consistency
RDBMS
Quorum 2 Phase
Commit
Partition
Tolerance Replication DNS Availability

26. My First Cloud App
Actually works Tomcat
Now let’s
scale it up Catalog
Order
…and enter
the globalized
world
RDBMS

27. Scaling My First Cloud App
Global
Replication?
Tomcat business
Across data
More
centers?
Catalog customers
Write
Order Reliable?
performance?
Fast?
Catalog used
a lot
more
than RDBMS RDBMS RDBMS
Order

28. Scaling My First Cloud App
• IaaS
– Can use other regions and scale datastore
– Limits of technology (RDBMS) still apply
– Need to installation / configuration it yourself
• Google App Engine
– Datastore scales well
– Global data centers?
• Amazon Beanstalk
– Turn key MySQL with replication (RDS)
– …but of you can’t change the limits of RDBMS
– Beanstalk only in US East at the moment

29. Scaling My First Cloud App
• Cloud Bees
– Data center only in the US
– MySQL installation very limited
– Can use Amazon RDS
• Cloud Foundry
– Data centers only in the US
– MySQL limited
• You will need to do more yourself e.g. on EC2
• Less support by the PaaS

30. My Second Cloud Application
Tomcat Tomcat
Catalog Catalog
Updates as messages
Catalog separated
Will be eventually delivered
Because of load
AP, but no C

31. My Second Cloud Application
• Parts of application separated because of scaling
scheme
• Introduce “Eventual Consistency”
– Either using NoSQL (e.g. CouchDB)
– …or using messaging
– Might consider in memory or cache (Redis, memcached,
GemFire)
• RabbitMQ is the predominant approach for messaging
in the cloud
– NASA’s nebula project
– #1 on EC2
– Support in Cloud Foundry planned
• More messaging: Amazon SNS, SQS …

32. My Second Cloud Application
Tomcat Tomcat
Order Order
Orders are sent as messages
Will eventually be sent to the backend
Will eventually be handled by the backend

33. 2nd Cloud App: Programming
Model
• Very different from a normal Enterprise Java
three tier application
• Not so different from a portal
• No one global database
• Focus on messaging
• Need support for RabbitMQ / AMQP
• I.e. Spring AMQP (see other talk)
• Programming model very much like Spring JMS
• Can create message driven POJOs

34. 2nd Cloud App: Programming
Model
• Need NoSQL / Caching / Data Fabric
• See Spring Data talk
• Spring supports many relational technologies
– iBATIS, Hibernate, JPA …
– E.g. common exception
– Uniform API approach
– Still specific API for each technology
• These abstractions are flexible
• Can be used for NoSQL as well!

35. Another Cloud Challenges
• Catalog browsing statistics from log files
• i.e. how many customer looked at X?
• Sounds like a batch
• File in, statistics out
• With Spring Batch
• But: Globally distributed data
• A lot of data
• Some systems might not be online

36. Spring Batch?
• Spring Batch can be used to run batches
• I.e. Jobs with steps
• But:
– How do you deal with the distribution?
– No central place with all the data
– Do you prefer incomplete data or no data at all?
• Need something different.

37. Map / Reduce
• Map: Apply a function to all data
– Emit (item name, 1) for each log file line
• Master sorts by item name
• Reduce: Add all (item name, 1) to the total
score Reduce
• Map can be done on
any node Map Map
• Master collects data

38. Map / Reduce Benefits
• Data can stay on workers
• Can done in parallel on many machines
• Prefer answer on available data over no
answer at all
• Can deal with redundant storage
• But:
– Lot of network communication
– More work in total

39. Spring Hadoop
• Hadoop is a complete solution for Map/Reduce
• HDFS Filesystem
• HBase Database
• Hive for Data Warehousing / ad hoc queries
• Spring Hadoop adds templates and POJO based
model
• https://github.com/springsource/spring-hadoop

40. And What About Java EE 7?
• Cloud is one of topics for Java EE 7
• Theme: Multi-Tenant / Isolation of customers
• New role: PaaS Administrator
• Additional security
• QoS
• Application Metadata
• Caching will become more important and will
be covered by Java EE

41. The Cloud Challenge Java EE
Does Not Solve
• JTA = 2PC = CA, not P
• JPA / JDBC = CA, not P
• Will you use that in the Cloud?
• NoSQL?
• RabbitMQ?

42. Java EE 7 & Cloud
• Multi tenant: Just one way to isolate users
• Can also use virtual machines, individual servers
etc
• Multi tenant good for lots of small applications
• Has been used for SaaS systems
• Tomcat and Servlets dominate the Cloud market
• When will certified Java EE 7 servers be
available?
• Who will provide a Cloud based on Java EE 7?

43. Conclusion
• IaaS is very flexible
• PaaS makes deployment, monitoring and operations
easier
• All Java PaaS support Spring
• Demanding Cloud applications need
– AMQP messaging
– NoSQL / Caching
– Map/Reduce
• Spring’s general abstractions can be extended to support
this
• Concepts you are used to!
• Java EE is stuck with “traditional” enterprise systems

44. Wir suchen Sie als
 Software-Architekt (m/w)
 Projektleiter (m/w)
 Senior Software Engineer (m/w)
 Kommen Sie zum Stand und gewinnen Sie ein iPad 2!
jobs@adesso.de
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