GigaSapces on CANOE'09

1. The Next Generation Application Server – How Event Based Processing yields scalability Guy Korland R&D Team Leader

4. GigaSpaces XAP – Designed For: Performance Scalability Latency

5. GigaSpaces Evolution Single space Load Balance Partition & Replication SLA container Event Container NG Application Server PaaS Cloud 2000 2003 2005 2006 2007 2008 2009

8. Today’s Reality – Tier Based Architecture Separate technology implementation Bottlenecks in all areas where state is stored, architecture can’t scale linearly! Separate technology implementation Separate technology implementation bottlenecks bottlenecks

9. Traditional Architecture - path to complexity… (marktplaats.nl) Auction Owner Auction Service Bid Service Trade Service Info Service Timer Service Auction Service Bid Service Trade Service Info Service Timer Service T Bidder Validate Result Process Bid Bid Accepted Bid Result Process Trade Get Bid Result Place bid

12. There is a huge gap between peak and average loads

13. Bottlenecks, Performance, Scalability and High availability headaches Bad Publicity Revenue Loss Customer Dissatisfaction Regulatory Penalties

17. Event Containers

18. Based on JavaSpaces C++

19. The Master Worker Pattern

21. Event Containers

27. Event Containers

28. Step 5 – Auto Scale Out

29. Processing Unit – Scalability Unit Single Processing Unit Processing Unit - Scaled Involves Config Change No code changes!

30. Processing Unit – High-Availability Unit Sync Replication Primary - Processing Unit Business logic – Active mode Backup - Processing Unit Business logic – Standby mode

31. Database Integration - Async persistency Sync Replication Primary - Processing Unit Business logic – Active mode Backup - Processing Unit Business logic – Standby mode Mirror Process ORM Initial Load Async Replication Async Replication

37. Typical Application

40. Event Containers

41. The POJO Based Data Domain Model @SpaceClass(fifo=true) public class Data { … @SpaceId ( autoGenerate = true ) public String getId () { return id; } public String setId ( String id ) { this .id = id; } public void setProcessed ( boolean processed ) { this . processed = processed; } public boolean isProcessed ( boolean processed ) { return this . processed; } } SpaceClass indicate that this is a SpaceEntry – SpaceClass includes classlevel attributes such as FIFO,Persistent… SpaceId used to define the key for that entry.

42. Data Processor Service Bean @SpaceDataEvent to be called when an event is triggered. public class DataProcessor{ @SpaceDataEvent public Data processData ( Data data ) { … data . setProcessed ( true ) ; //updates the space return data; } } Updates the data in the Space.

43. Wiring Order Processor Service Bean through Spring <bean id=&quot; dataProcessor “ class=&quot;com.gigaspaces.pu.example1.processor.DataProcessor&quot; /> <os-events:polling-container id=&quot;dataProcessorPollingEventContainer&quot; giga-space=&quot;gigaSpace&quot;> <os-events:tx-support tx-manager=&quot;transactionManager&quot;/> <os-core:template> <bean class=&quot;org.openspaces.example.data.common.Data&quot;> <property name=&quot;processed&quot; value=&quot;false&quot;/> </bean> </os-core:template> <os-events:listener> <os-events:annotation-adapter> <os-events:delegate ref=&quot; dataProcessor &quot;/> </os-events:annotation-adapter> </os-events:listener> </os-events:polling-container> The event Template The event Listener

44. Data Feeder public class DataFeeder { public void feed(){ Data data = new Data(counter++); data.setProcessed( false ); //feed data gigaSpace.write(data); } } Feed Data

45. Remoting – Taking one step forward Event

46. Remoting – Taking one step forward Reduce

47. Remoting – IDataProcessor Service API public interface IDataProcessor { // Process a given Data object Data processData(Data data); }

48. Remoting - DataProcessor Service @RemotingService public class DataProcessor implements IDataProcessor { public Data processData(Data data) { … data.setProcessed( true ); return data; } }

49. Remoting - Order Feeder public class DataFeeder { private IDataProcessor dataProcessor; public void setDataProcessor(…) { this .dataProcessor = dataProcessor; } public Data feed(){ Data data = new Data(counter++); // remoting call return dataProcessor.process (data) } }

50. Summary

53. Scale up Throughput Benchmark – Embedded mode 1.8 Million read sec! 1.1 Million write/take sec!

54. Scale up Throughput Benchmark – Remote mode 90,00 read sec! 45,00 write/take sec!

56. Event Containers

58. Web application – Pet Clinic

59. Classic Architecture – Step 1- Request Submission Get request and invoke Service Data Grid

60. Classic Architecture – Step 2- Retrieve Results Page Generation Data Grid

61. Web Application Benchmark Results - Capacity

62. Web Application Benchmark Results - Capacity

66. Dynamic repartitioning and load sharing I SLA Driven Container Indexed Notify / Query template Notify / Query template Partitioned Space Partitioned Space Partitioned Space

67. Dynamic repartitioning and load sharing II SLA Driven Container SLA Driven Container Partitioned Space Partitioned Space Partitioned Space

70. Thank You! Q&A

71. Appendix

73. Continuous High Availability Fail-Over Failure

74. Dynamic Partitioning = Dynamic Capacity Growth VM 1 ,2G GSC VM 3 , 2G GSC VM 2 ,2G GSC Max Capacity=2G Max Capacity=4G Max Capacity=6G In some point VM 1 free memory is below 20 % - it about the time to increase the capacity – lets move Partitions 1 to another GSC and recover the data from the running backup! Later .. Partition 2 needs to move… After the move , data is recovered from the backup VM 5 , 4G GSC VM 4 ,4G GSC P - Primary B - Backup P P P B B B E F Partition 1 A B Partition 2 C D Partition 3 A B Partition 2 E F Partition 1 C D Partition 3

80. Service Executors

81. Service Executors

83. IMDG Basic Operations