GridGain – Java Grid Computing Made Simple

1. GridGain – Java Grid Computing Made Simple Nikita Ivanov www.gridgain.org

2. Agenda GridGain What is Grid Computing and why? What is GridGain and why? Why Java and Open Source? Key Concepts Demos Grid Application in 15 Minutes Running JUnits on Grid GridGain – Java Grid Computing Made Simple Slide 2

3. What is Grid Computing? • Compute Grids • Parallelize logic execution • Data Grids • Parallelize data storage • Grid Computing = Data Grids + Compute Grids • Data Partitioning + Affinity Map/Reduce Product. Business. People.

4. Why Grid Computing? • Ask Google, Yahoo, eBay, Amazon • Solves problems often unsolvable otherwise • Google has almost 1,000,000 nodes in its grid • Uniformed programming paradigm that scales from garage to Google • This code returns the same result on 1, 2 or 1,000 nodes – only faster, much faster… @Gridify(taskName = "Optimizer") public double optimizePortfolio(Portfolio p) { ... } Product. Business. People.

5. What is GridGain? Grid computing framework Full-stack grid computing Innovative Map/Reduce Integration with leading Data Grids Elegant simplicity Think Spring, Hibernate vs. EJB2 Java centric Built in Java and for Java Open source LGPL/Apache license GridGain – Java Grid Computing Made Simple Slide 5

6. Why GridGain? Existing projects are: Too complex to use Globus, GridEngine anyone? Too expensive to use $M for DataSynapse, Platform, UD Not Java-based or Java friendly Alien to Java 5 and JEE GridGain – Java Grid Computing Made Simple Slide 6

7. Professional Open Source GridGain – Java Grid Computing Made Simple Slide 7 GridGain - Professional Open Source • Open source • FREE software • FREE upgrades • FREE community support • FREE source code • No gimmicks • Commercial enterprise-level support and services • Indemnification • Custom SLAs • Guaranteed response time Like JBoss, Spring Source, Mule Source…

8. Key Concepts GridGain – Java Grid Computing Made Simple MapReduce Zero Deployment On Demand Scalability Blend-In Integration Transparent Grid Enabling with AOP Data Grids Integration

9. MapReduce • Task execution request • Task splits into jobs • Result of job execution • Aggregation of job results GridGain – Java Grid Computing Made Simple

10. MapReduce Example GridGain – Java Grid Computing Made Simple

11. Zero Deployment Peer-to-Peer On-Demand Class Loading technology No Ant scripts to run No JARs to copy or FTP No nodes to restart Develop in EXACTLY the same way as locally Change->Compile->Run on the grid Start many grid nodes in Single JVM – debug grid apps locally (!) Single computer – run grid on your workstation Single biggest developers’ productivity boost GridGain – Java Grid Computing Made Simple Slide 11

12. On Demand Scalability GridGain – Java Grid Computing Made Simple

13. Blend-In Integration Service Provider Interface (SPI)-based architecture Plug in and customize almost any aspect of grid computing framework LEGO-like assembly of custom grid infrastructure Grid computing framework aspect that are fully pluggable: • Checkpoints • Failover • Collision Resolution • Topology management • Load balancing • Deployment • Communication • Discovery • Tracing • Startup • Event storage GridGain – Java Grid Computing Made Simple Slide 13

14. “Out-of-the-box” integration with: Blend-In Integration, cont. Application Servers • JBoss AS • BEA Weblogic • IBM Websphere • Glassfish • Tomcat Data Grids • JBoss Cache • Coherence • GigaSpaces AOP • JBoss AOP • Spring AOP • AspectJ Messaging Middleware Mule JMS • ActiveMQ • SunMQ Jgroups Email TCP, IP-Multicast Others Spring Junit JXInsight GridGain – Java Grid Computing Made Simple

15. Transparent Grid Enabling w/AOP Execution of process() method will be performed on the grid GridGain – Java Grid Computing Made Simple 01 class BizLogic {02@Gridify(…) 03   public static Result process(String param) {04     ...05   }06 }0708 class Caller {09   public static void Main(String[] args) {10GridFactory.start();1112     try {13BizLogic.process(args[0]);14     }15     finally {16GridFactory.stop();17     }18   }19 }

16. Data Grids Integration Data + Compute Grids = Full stack grid computing Compute grids parallelize processing logic Data grids parallelize data storage Affinity Map/Reduce – ability to co-locate processing logic and the data Minimizes “noise” traffic Optimal grid load and performance GridGain – Java Grid Computing Made Simple Slide 16

17. Data Grids Integration, cont. GridGain – Java Grid Computing Made Simple GridGain full stack grid computing: • GridGain+JBoss Cache • Full OPEN SOURCE grid computing platform • Native integration • GridGain+Coherence Data Grid • Native integration • GridGain+GigaSpaces Data Grid One compute grid - many data grids • Freedom of choice

18. Data Grid Integration, cont. GridGain – Java Grid Computing Made Simple

19. Demos Java 5/Eclipse 3.2/Windows XP GridGain 2.0 GridGain – Java Grid Computing Made Simple Slide 19

20. Q & A Thanks for your time! Nikita Ivanov: nivanov@gridgain.com GridGain: www.gridgain.org GridGain – Java Grid Computing Made Simple