Goal. Outline. Outline. J2EE architecture. Enterprise JavaBeans. J2EE Performance Scalability and Clustering Part 1

Transcription

1 Emmanuel Cecchet INRIA Rhône-Alpes, ObjectWeb J2EE Performance Scalability and Clustering Part 1 Goal J2EE performance scalability evaluation design patterns communication layers Java Virtual Machine J2EE architecture and business separation Internet Web container EJB container J2EE Application Server server Enterprise Javas beans map data stored in the database Managed Persistence (BMP) Container Managed Persistence (CMP) Session beans stateless: temporary operations stateful: temporary objects design patterns communication layers 1

2 Design patterns: s only Design patterns: Session s and business mixed and business separation Session bean Session bean Design pattern: s Data Access Objects separation with s (BMP or CMP) Design patterns: Session Façade session bean with EJB 1.1 Remote interface Communication layer Design patterns: EJB 2. local Session with EJB 2. local interface s Local interface design patterns communication layers 2

3 Container design Reflexive approach (JBoss 2.4) dynamic proxy home and component interfaces generated at run-time reflection to locate a bean or map method signatures Precompiled approach (JOnAS) custom implementations of home and component interfaces direct call of the bean instance need a specific compiler Communication layers Comm. layer Standard RMI Optimized RMI Jeremie Protocol on TCP/IP JRMP JRMP GIOP Object passing value reference reference Local calls optimized no no yes EJB container JBoss JOnAS JBoss JOnAS RUBiS Auction site modeled after ebay 26 interactions Browsing mix: read-only mix Bidding mix: 15% read-write interactions : 1.4 GB 1 million users, ~5 comments >5 items, 33 active bids Code complexity Code complexity Lines of code EJB s s are easy but verbose to write Large number of beans result in large code base Main portability issues naming conventions deployment descriptors sonly Session beans EB CMP EB BMP Session EJB 2. local 3

4 Design pattern evaluation Comparison of the 6 design patterns Results using: Sun JVM best container design best communication layer Metric: throughput in interactions per minute Design pattern results Design patterns Maximum throughput in interactions/minute sonly Session s EB-CMP EB-BMP Session EJB 2. local Session s = s only s BMP = CMP data access too fine grain Session allows coarser grain access 2.5 times slower than Session s Other performance factors RUBiS Overall results Comparison of the 5 EJB design patterns Varying: communication layer Metric: throughput in interactions per minute Maximum throughput in interactions/minute Session s EB-CMP EB-BMP Session JBoss - Standard RMI JOnAS - Standard RMI JBoss - Optimized RMI JOnAS - Jeremie EJB 2. Local 4

5 Performance factor analysis Container design pre-compiled scales better than dynamic proxy significant impact on s Container design RUBiS Session Execution time breakdown 1,9,8,7,6,5,4,3,2 Other Garbage Collector Security Naming MM-MySQL Connection pooling Transaction manager Container Reflexion s (edu.rice.*) Communication,1 JBoss - Optimized RMI JOnAS Jeremie Performance factor analysis Communication layers Communication layers optimized layers needed for scalability EJB 2. local interfaces to avoid local communications RUBiS Session Execution time breakdown 1,9,8,7,6,5,4,3,2 Other Garbage Collector Security Naming MM-MySQL Connection pooling Transaction manager Container Reflexion s (edu.rice.*) RMI or Jeremie TCP/IP (java.net.*),1 JBoss - Optimized RMI JOnAS Jeremie JVM Performance JVM Performance Throughput in requests/minute IBM JRockit Sun IBM is the best performance/stability tradeoff JRockit is very unstable and performance can dramatically drop Sun or 1.4.x is the worst performer but is quite stable Number of clients 5

6 Other results Monitoring of CPU, disk, network and memory CPU profiling at the peak point using OptimizeIt 45-9% spent in communication up to 32% in reflection JDK 1.4 does not improve performance J2EE Clustering J2EE Clustering Internet Server EJB Server Server EJB Server Server EJB Server clustering hardware: L4-switch software: LVS, RR-DNS, /JSP server clustering in-memory session replication Server EJB Server Server EJB Server Server EJB Server J2EE Clustering EJB Server clustering cluster stubs for load-balancing database based persistency clustering Oracle RAC Clustered-JDBC Conclusion Design pattern determines performance Communication accounts for 45-9% of CPU time Reflection limits container scalability Less than 2% of execution time in user bean code JVM limits overall scalability Clustering can help scaling further 6

7 Some links RUBiS: JMS EJB 2. CMP JDO JOnAS: JBoss: C-JDBC: More to come on J2EE Clustering Emmanuel Cecchet INRIA Rhône-Alpes, ObjectWeb J2EE Performance Scalability and Clustering Part 2 7