Innovations in Grid Computing with Oracle Coherence
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Innovations in Grid Computing with Oracle Coherence

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As presented by Mike Lehmann at Oracle Technology Network Architect Day, Redwood Shores, CA, August 30, 2011.

As presented by Mike Lehmann at Oracle Technology Network Architect Day, Redwood Shores, CA, August 30, 2011.

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Innovations in Grid Computing with Oracle Coherence Innovations in Grid Computing with Oracle Coherence Presentation Transcript

  • <Insert Picture Here>Oracle Coherence OverviewMike LehmannOracle Coherence Product Managementmike.lehmann@oracle.com
  • The following is intended to outline general productuse and direction. It is intended for informationpurposes only, and may not be incorporated into anycontract. It is not a commitment to deliver anymaterial, code, or functionality, and should not berelied upon in making purchasing decisions.The development, release, and timing of any featuresor functionality described for Oracle’s productsremains at the sole discretion of Oracle.
  • Presentation ObjectivesTo Understand…1) How Coherence improves performance, enables linear scalability, and provides availability for shared services2) How Coherence leverages its distributed architecture to enable high performance analytics and processing on application domain objects3) Coherence’s roadmap and strategic direction
  • Oracle Fusion MiddlewareComplete, Open, Integrated, Best-in-Class Web Mobile Social User Engagement Content Management Identity Management Business Intelligence Development Business Process Tools Management Service Integration Enterprise Management Data Integration Cloud Application Foundation
  • Oracle Coherence Value Path Lowered Costs Increased Quality of ServiceBenefits Performance Linear Scalability Availability Reliability Resource Utilization Interoperability Oracle Coherence Data Grid Caching Analytics Transactions EventsFeatures Cache Map Query Map Observable Map Write Behind Caching Invocable Map Coherence*Web Coherence*Web C++/.NET Interop TopLink Grid
  • Enterprise Application Scalability ChallengesEase of Scaling Service Clients • Grows and scales • Browsers, supplier and partner clients, application naturally clients, mobile apps, and other service consumers. • Increasing load Service Implementation • Computationally • Custom applications, BPM processes, service bus intensive work endpoints, UI services, and other service providers. • Highly dependent on shared services Shared Services • Complex and • RDBMS, cloud services, supplier and partner expensive to scale services, mainframe applications, and so on. • High latency SPOB and SPOF
  • Traditional Clustering • Dependent on shared service Service Clients latency • Increase in size of cluster leads to increase in load on shared serviceService Service ServiceNode 1 Node 2 Node N • Inconsistent view of data across cluster nodesCached Cached CachedObjects Objects Objects • No possibility for in-memory analytics • Limited in-memory cache due to Shared Service • Capacity implications • Java garbage collection
  • Enters the Coherence Data GridEase of Scaling • Clustered Caching • Provides a consistent view Service Clients of data across cluster • Partitioned Caching Service Service Service • Increase in size of cluster Node 1 Node 2 Node N adds capacity but does not decrease performance In-memory Data Grid • Distributed Data Processing • Enables a distributed Shared Services platform for high performance in-memory analytics
  • How to Integrate with Coherence Two Options1. Custom integration through Coherence API • For Java, C++, and .NET applications2. Through Existing ‘Switch-On’ Integrations • Oracle TopLink Grid – For JPA object relational data offload • Oracle Service Bus – For service results caching • Oracle Coherence*Web – For HTTP session caching
  • Coherence Basic Inteface Custom Integration• Application uses NamedCache neCache = CacheFactory Coherence API to .getCache("NetworkElement"); store/access its data …• Application cache NetworkElement ne = new properties are configured NetworkElement(“ID-321”, “OC- externally through XML 196”, “Nortel Networks); file neCache.put(“ID-321”, ne); …• Basic cache access through NamedCache NetworkElement ne = neCache.get(“ID-321”); Map interface
  • Replicated Caching Scheme JVM JVM Application Logic Application Logic Business Logic Business LogicApplication Cluster Domain Objects Coherence Cluster Object Object Object Object A B Replicated Cache A B Service Object Object Object Object C D C D Coherence Node Coherence Node
  • Distributed Caching Scheme JVM JVM Application Logic Application Logic Business Logic Business LogicApplication Cluster Domain Objects Primary Primary Coherence Cluster Object Object Object Object A B Distributed Cache C D Service Coherence Node Coherence Node
  • Near-Caching Topology JVM JVM Application Logic Application Logic Business Logic Business Logic Local Cache Local CacheApplication Cluster Coherence Cluster JVM JVM Domain Objects Primary Primary Object Object Object Object A B Distributed Cache C D Service Coherence Node Coherence Node
  • Heterogeneous Topology C++ Application .NET Application Application Logic Application Logic Business Logic Business Logic Local Cache Local CacheApplication Cluster Coherence Cluster JVM JVM Domain Objects Primary Primary Object Object Object Object A B Distributed Cache C D Service Coherence Node Coherence Node
  • Reliability Partitioned Fault Tolerance• Automatically and transparently manages the fault tolerance of your data.• Backups are guaranteed to be on a separate physical machine as the primary.• Backup responsibilities for one node’s data is shared amongst the other nodes in the grid.
  • Write Behind• All data writes occur through cache• Updates to the cache are written asynchronously to the data source
  • Coherence Elastic Data | In-Memory Cache Seamless management of data across memory and disk-based Coherence devices, including RAM, Solid State Disk (SSD), and Storage Area Networks (SAN) RAM Tuned journaling algorithms enabling near memory speed access to data regardless of FLAS storage medium H 4x increase in data capacity Very high performance, low latency DIS Massively concurrent reads and K writes (to external storage)
  • Preliminary Data - Elastic Data on ExaLogic
  • Trading & Risk Platform Example of conceptual architecture Profit & Loss Management & Modelling & Trading Risk Management Compliance Development Multi-Channel Management Continuous Monitoring Trading Desk Portals Existing Systems Business Insight Real-time Alerts Market Data Models Orders Trading Compliance Pricing Enterprise Management Algorithms Positions Trades Instruments Risk Profit & Loss SLA Management Order Low-Latency Infrastructure Management Risk Modelling Data Event High Activity Management Data Grid Integration Processing Availability Monitoring Market Feeds High Performance Persistent Data Performance Tuning News Feeds Valuation Models Historic Market Data Market Reference Data Instruments Positions Content Shared Dynamic Infrastructure KeyOracle Confidential and Proprietary Existing Assets New Components In Memory New Interface
  • Presentation ObjectivesTo Understand…1) How Coherence improves performance, enables linear scalability, and provides availability for shared services2) How Coherence leverages its distributed architecture to enable high performance analytics and processing on application domain objects3) Coherence’s roadmap and strategic direction
  • Data ProcessingParallel Query• Programmatic query mechanism• Queries performed in parallel across the grid• Standard indexes provided out-of-the-box and supports implementing your own custom indexes
  • Data Processing Continuous Query Cache• Automatically, transparently and dynamically maintains a view locally based on a specific criteria (i.e. Filter)• Same API as all other Coherence caches
  • Data Processing Invocable Map• The inverse of caching• Sends the processing (e.g. EntryProcessors) to where the data is in the grid• Standard EntryProcessors provided Out-of-the-box• Once and only once guarantees• Processing is automatically fault-tolerant• Processing can be: • Targeted to a specific key • Targeted to a collection of keys • Targeted to any object that matches a specific criteria (i.e. Filter)
  • Data ProcessingInvocable Map
  • Data ProcessingEvents• Java Bean Model• Key-Based• Filter-Based
  • Presentation ObjectivesTo Understand…1) How Coherence improves performance, enables linear scalability, and provides availability for shared services2) How Coherence leverages its distributed architecture to enable high performance analytics and processing on application domain objects3) Coherence’s roadmap and strategic direction
  • Coherence RoadmapTheme Features BenefitEase Of Use Cache Configuration GUI and Eclipse user-friendly configuration toolingShared Coherence Container Increase manageability and HAInfrastructure Customizable partitioning Finer grained control of partitioning REST API Extended client language integration Multi-Datacenter Replication Push replication feature full supportRASP Query “Explain Plan” Better query visibility Delta backups Increase write performanceInnovation Exabus: Native IB support Dramatically Lower Latency Continuous Aggregation Real-Time AnalyticsOracle Confidential – Do Not Distribute
  • How to Learn More• Attend the Oracle University (OU) course • You can register in this course by using this link• Read Coherence 3.5 book • Provides a great introduction to Coherence• Attend the Coherence SIG Meetings • Available in San Francisco, NYC, London, and Toronto• Use OTN Coherence Forum• Follow-ups with Coherence Architects/PMs with deep experience in FS
  • Q&A 29
  • Coherence Fusion Middleware Integrations at a Glance TopLink Grid for Database Access Coherence to Coherence*Web Performance Improve Quality for HTTP ImprovementsEnd-To-End of RTD Results Session OffloadManagement Oracle EM Coherence Oracle RTD Enterprise Applications Management Pack WebLogic GlassFish Server Oracle Oracle Golden Database Service Gate Bus Golden Gate Coherence Adapter for Service Result Caching for Change Data Capture Improved Service Performance
  • Oracle Integrations By Value Oracle IGBU Oracle Golden Gate WebCenter Oracle CGBU Real-time Decision SOA BPEL Oracle GlassFish Oracle ADF SOA Human WorkflowOracle Access Manager Oracle PeopleSoft SOA Business Rules Oracle CEP Oracle Secure Token Service SOA Coherence Adapter TopLink Oracle Identity Federation SOA Technology Adapters WebLogic Portal Meta-data Services (MDS) BI PublisherService Delivery Platform Oracle Entitlement Services WebLogic Server Oracle BEAM Oracle Service Bus ATG SOA Infrastructure ATG Hosted Services Oracle Data Integration Oracle Web Services Manager Advanced Capabilities Value Data Grid Data Cache Oracle Confidential – Do Not Distribute Simple Clustering 31