Spring in the Cloud

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

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

What is the Cloud? Infrastructure

as a Service Platform

as a Service Software

as a Service

> Software or Service that you use

> Components that you add/integrate into your app

> Virtual Application Server > Handles Scale-Out

> Mostly Managed by Provider

> Virtual Servers > Similar to Virtualization

> Manage Everything Yourself

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

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

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

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

•  Sample application uses Spring

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

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

Cloud Foundry Spring Integration

•  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

<cloud:data-source id="dataSource"/><cloud:mongo id="mongo"/><cloud:redis-connection-factory id="redisConnectionFactory"/>

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

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 …

My First Cloud App

Tomcat

Catalog Order

RDBMS

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

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?

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

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

How Is Cloud Different?

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

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!

Cloud is Just Another Trade Off •  How can applications be successfully created

in this environment?

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?

Consistency

Partition Tolerance

Availability

RDBMS 2 Phase Commit

DNS Replication

Quorum

My First Cloud App

Tomcat

Catalog Order

RDBMS

Actually works Now let’s scale it up …and enter the globalized world

Scaling My First Cloud App

Tomcat

Catalog Order

RDBMS RDBMS RDBMS

Replication? Across data centers? Write performance? Reliable?

Fast?

Global business More customers

Catalog used a lot more than Order

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

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

Tomcat

My Second Cloud Application

Tomcat

Catalog Catalog

Updates as messages Will be eventually delivered AP, but no C

Catalog separated Because of load

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 …

Tomcat

My Second Cloud Application

Tomcat

Order Order

Orders are sent as messages Will eventually be sent to the backend Will eventually be handled by the backend

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

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!

•  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 •  How do you deal with that?

Another Cloud Challenges

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.

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

•  Map can be done on any node

•  Master collects data Map Map

Reduce

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

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

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

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?

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?

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

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