1. No More Hops: Towards a linearly scalable application infrastructure. Owen Taylor Blog: http://www.jroller.com/owentaylor

2. About me... ORBs Containers Frustration “The Final Frontier”

3. Technical Target: Linear Scalability Process increasing volume of information –Scale out to get more processing power when volume increases Shorter time –Mostly through parallelizing of transactions Lower cost –Pooling low commodity resources Total TPS = X*N X TPS Processing unit 1 Processing unit 2 Processing unit 3 Processing unit N Incoming Transactions

4. 4 Database-Based Architectures: scaling bottlenecks Data – I/O overhead, Contention Messaging – Complex workflows increase latencies Processing – CPU, Parallelization Silo approach, each tier has its own clustering model, lots of moving parts

5. Another Way of Looking at It: A Tightening Bottleneck Static Web Thousands of (concurrent) connections Web App Hundreds of connections Business components Dozens of connections Database Several connections

6. 6 Services can be Java, C++,.Net Content-Based Routing Typical SOA model based on ESB Most ESB solutions do not address stateful services and often use a centralized database to share state between services

7. Pinpoint ‘That method call...’ [what about the app?] Presentation Logic Business Logic Integration Logic Data Tier Messaging Tier Client Tier If we just add a caching layer to the system...

8. TPC: Transparent Partitioning & Colocation The next wave of applications will scale in this manner –Because Gartner says so Many already scale one or two tiers in this way –Partitioned Databases with Triggers –Content-Based Routing though Messaging with coupled consumers –All-in one web applications using caching (IMDG) Space-Based Architecture offers an implementation of TPC using Spaces –GigaSpaces enables this architecture for.NET, C++, and Java

9. Space-Based Architecture is an Implementation of TPC Utilizing a single logical/virtual resource to share: –Data –Logic –Events Services: –Interact with each other through the space –Can be co-located with data/events for faster results –Are deployed and managed in an adaptive and fail-safe way } Objects! Data Provisioning Event Propagation Logic Processing

10. Parallel Processing: Divide and Scale out Request Feed Proxy Request Proxy Write Service1 Service2 Service3 Partitioned Spaces Request: Zoom-in update stocks where … Proxy Virtual Table

11. Colocation: Dramatically Reduce Latency Request Feed Proxy Request Requests Request Proxy Write Partitioned Spaces Request: Zoom-in Proxy Virtual Table update stocks where …

12. Client Registration Offer Driver Execution Engine Write Notify Register Matching Engine Update Average Latency ~14 milliseconds (+ ~2 milliseconds for backup) Example: Algorithmic Trading

13. Reduced Latency Due To Colocation Offer Driver Execution Engine Write Notify Matching Engine Average Latency ~600 microseconds (+ ~2 milliseconds for backup) Client Registration Register Update Write [TradingStrategy]

14. RTD topic GSC 1 Embedded Space Processing Unit 1 StockConverter Spring Bean GSC 2 Embedded Space Processing Unit 2 GSC 3 Embedded Space Processing Unit 3 One Logical Space (Partitioned) GSC 4 Space Proxy Processing Unit 4 MarketFeed Spring Bean [Demo] Excel With a Grid Backbone: } StockConverter Spring Bean StockConverter Spring Bean

15. 15 Nuts and Bolts: 4 Verbs WriteTakeReadWriteNotify Write + Read = IMDG Write + Notify = Messaging Write + Take = Parallel Processing Take  Write  Read  Take  Notify

16. 16 Services can be Java, C++,.Net Content-Based Routing Shared state to enable stateful services SBA = Real-time SOA for Stateful Applications

17. 17 Space Bus Validation Service Service Bean Matching Service Service Bean TakeWriteNotify Declarative Spring-SBA - Making Scalability Even Simpler Space Bus Validation Service Service Bean Matching Service Service Bean TakeWriteNotify Take/Notify Write Take/Notify Write Polling Event Container Notify Event Container Polling Event Container Notify Event Container Declarative Abstraction

18. 18 Reaching Scalability with Space Based SOA Write Take Read Write Notify

19. Order Processing Demo: Logical Workflow Write new Orders (Feed) Client Accounting BrokenOrderCleaner Logistics Take Orders(new) Write Orders (approved) Take Orders (approved) Write Orders (shipped) Take Orders (shipped, unshipped, unapproved) Write new Orders (once cleaned) OrderSystem

20. Linearly Scalable OrderProcessing: Deploy Once: GSC 1 Space Proxy Processing Unit 1 orderfeed Spring Bean GSC 2 Embedded Space Processing Unit 2 ordersystem Spring Bean

21. GSC 1 Embedded Space Processing Unit 1 ordersystem Spring Bean GSC 2 Embedded Space Processing Unit 2 ordersystem Spring Bean GSC 3 Embedded Space Processing Unit 3 ordersystem Spring Bean One Logical Space (Partitioned) GSC 4 Space Proxy Processing Unit 4 orderfeed Spring Bean Linearly Scalable OrderProcessing: Deploy Again: The throughput of the application grows linearly as the number of ordersystem deployments increase. }

22. 22 Building SBA Application – How it works.. Step 1: –Implement POJO domain model Step 2: –Implement the POJO Services Step 3: –Wire the services through spring Step 4: –Packaging –Deploy to Grid (Scale-Out) Register

23. 23 Come To The “Let’s Code” BOF!!! November 8 th 2007 @7:15 PM in “City Room” Everyone bring a laptop Download GigaSpacesXAP6.0.1 –http://www.gigaspaces.com/os_downloads.html Install Eclipse 3.3 Europa [ or Intellij ] Go to my blog and download: “projectcreator.jar” (it is a zip in hiding) –http://www.jroller.com/resources/o/owentaylor/project-creator.jar

24. Code Time: The project-creator Demo This example shows –Rapid development of two simple projects Processor Publisher –Integration with IDE Run projects in eclipse to test quickly –Fail-safe behavior without code awareness Run projects in Service Containers for transparent: –Failover –Scaling

25. 25 So, Why use a Declarative SBA? Simple! –Building a fully fledged distributed application becomes just a matter of wiring simple local beans It’s all declarative through annotation or XML Non intrusiveness through abstraction –High level abstraction provides higher degree of flexibility to plug-in different solutions from different topologies, technologies and standards without compromising on the least common denominator Extremely Extensible –Very easy to extend the model and the underlying implementation without changing the application Reduced learning curve – It’s just Spring –If you already know Spring – you are ready to begin Fits natively to the existing Spring model Utilizes common practices such as annotation, IoC etc.

26. 26 Questions Please? Thank You! http://www.jroller.com/owentaylor