As presented by Randy Stafford at Oracle Technology Network Architect Day in Phoenix, AZ on December 14, 2011.

1. Innovations in Data Grid Technology with Oracle
Coherence
Randy Stafford | Architect At-Large
Oracle Product Development | Cloud Application Foundation
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2. The following is intended to outline general product use
and direction. It is intended for information purposes only,
and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality,
and should not be relied upon in making purchasing
decisions.
The development, release, and timing of any features or
functionality described for Oracle’s products remains at the
sole discretion of Oracle.
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3. Presentation Objectives
To Understand…
1) What is driving the need for Distributed Caching and In-
Memory Data Grid products?
2) How Coherence improves performance, enables linear
scalability, and provides availability for shared services
3) Coherence’s Strategic Direction and Latest Features
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4. Internet Scale
Problem: How to deal with the flood of customer demand for
services that occurs when you expose your infrastructure to a
large, potentially unbounded network.
- Or, stated more positively -
Opportunity: How to build infrastructure that allows you to
scale business and improve margins by exposing your services
to a large, potentially unbounded network.
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5. The Internet Scale Problem is Multi-Faceted
• Virtually unlimited number of customers
• Services exposed to partners’ overuse
• Customers’ experience relies on partners’ services
• Global and Real-time
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6. Scaling Infrastructure to Meet the Problem
• Applications must act on data in real-time
– Access, query, and aggregate the data
– Trigger business actions based on changes to data
• Must scale predictably and cost-effectively
– Failure to do so results in loss of revenue and cost overruns
– No time to re-architect in Internet time
• Need a new paradigm in data management
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7. More. Faster. Cheaper.
•Scale to more users and more data
•Scale predictably and cost-effectively
•Act on data in real-time
•Reduce spending on backend systems Get on the Grid
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8. Oracle Coherence
• Coherence: in-memory data grid
End Users
– Distributed caching to applications
– Scalable extreme transaction
processing Application
Servers
– Real-time eventing, query, and
map/reduce aggregations
– Abstraction from back-end data Application
sources Objects
– High-availability to applications
Mainframes,
Databases, etc.
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9. Oracle Fusion Middleware
Complete, 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
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10. Presentation Objectives
To Understand…
1) What is driving the need for Distributed Caching and In-
Memory Data Grid products?
2) How Coherence improves performance, enables linear
scalability, and provides availability for shared services
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reserved.

11. Enterprise Application Scalability Challenges
Ease 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 services, expensive to scale
mainframe applications, and so on. • High latency, SPOB,
and SPOF
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12. Traditional Clustering
• Dependent on shared service
latency
Service Clients
• Increase in size of cluster leads to
increase in load on shared service
Service Service Service
Node 1 Node 2 Node N
• Inconsistent view of data across
cluster nodes
Cached Cached Cached • No possibility for in-memory
Objects Objects Objects
analytics
• Limited in-memory cache due to
• Capacity implications
• Java garbage collection
Shared Service
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13. Enter the Coherence Data Grid
Ease of Scaling
• Clustered Caching
Service Clients – Consistent view of data
across cluster
• Partitioned Caching
Service Service Service
Node 1 Node 2 Node N – Increase in size of cluster
adds capacity but does not
decrease performance
In-memory Data Grid • Distributed Data Processing
– Enables a scalable platform
for high performance in-
memory analytics
Shared Services
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14. How to Integrate with Coherence
Two Options
Custom integration through Coherence API
- or –
Through Existing ‘Switch-On’ OOTB Integrations
Oracle Coherence*Web: HTTP session state
replication with WebLogic, GlassFish, Others
Oracle TopLink Grid: JPA object-relational data
Other Oracle Products that integrate Coherence
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15. ActiveCache: Premier WebLogic integration
• Distributed HTTP Session State Replication via WebLogic SPI
• Configuration, Lifecycle and Monitoring of Coherence Clusters
and Servers via WebLogic Admin Console & MBeans
• Dependency Injection in JEE modules
– Configure in web.xml, use annotations to inject named
cache
– Alternative to CacheFactory.getCache() lookup
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16. Gartner, Magic Quadrant for Enterprise
Application Servers
This Magic Quadrant graphic was published by Gartner, Inc. as part
of a larger research note and should be evaluated in the context of
the entire report. The Gartner report is available here:
http://www.gartner.com/technology/reprints.do?id=1-17GUO5Z&ct=110928&st=sb
Source: Gartner, Magic Quadrant for Enterprise Application Servers – Massimo
Pezzini, Yefim V. Natis, Kimihiko Iijima, Daniel Sholler, Raffaella Faveta –
September 26, 2011
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17. Coherence Basic Inteface
Custom Integration
NamedCache neCache = CacheFactory
• Application uses
.getCache("NetworkElement");
Coherence API to
…
store/access its data NetworkElement ne = new
• Application cache NetworkElement(“ID-321”, “OC-
196”, “Nortel Networks);
properties are configured neCache.put(“ID-321”, ne);
externally through XML …
file NetworkElement ne =
neCache.get(“ID-321”);
• Basic cache access
through NamedCache
Map interface
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18. Data Management
Partitioned Caching
• Extreme Scalability: Automatically,
dynamically and transparently
partitions the data set across the
members of the grid.
• Pros:
– Linear scalability of data capacity
– Processing power scales with data
capacity.
– Fixed cost per data access
• Cons:
– Cost Per Access: High percentage
chance that each data access will go
across the wire.
• Primary Use:
• Large in-memory storage environments
• Parallel processing environments
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19. Data Management
Partitioned Fault Tolerance
• Automatically,
dynamically 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.
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20. Data Management
Near Caching
• Extreme Scalability and Extreme
Performance: The best of both
worlds between the Replicated and
Partitioned topologies. Most
recently/frequently used data is
stored locally.
• Pros:
– All of the same Pros as the Partitioned
topology plus…
– High percentage chance data is local to
request.
• Cons:
– Cost Per Update: There is a cost
associated with each update to a piece of
data that is stored locally on other nodes.
• Primary Use:
– Large in-memory storage environments
with likelihood of repetitive data access.
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21. Data Source Integration
Read Through / Write Through
• Read Through
– All data reads occur through cache
– If there is a cache miss, the cache will load the data from the data
source automatically
• Write Through
– All data writes occur through cache
– Updates to the cache are written synchronously to the data
source
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22. Data Source Integration
Write Behind
• All data writes occur through cache
• Updates to the cache are written asynchronously to the
data source
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23. Data Processing
Parallel 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
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24. 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
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25. Data Processing
Invocable Map
• The inverse of caching
• Sends the processing to where the data is in the grid
– Once and only once guarantees
– Processing is automatically fault-tolerant
• Processing can be:
• Targeted to a specific key or collection of keys
• Targeted to any object that matches a specific criteria (i.e. Filter)
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26. Data Processing
Events
• Build Complex Event-Driven Apps
• Java Bean Model
• Key-Based
• Filter-Based
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27. Presentation Objectives
To Understand…
1) What is driving the need for Distributed Caching and In-
Memory Data Grid products?
2) How Coherence improves performance, enables linear
scalability, and provides availability for shared services
3) Coherence’s Strategic Direction and Latest Features
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reserved.

28. Release Overview
• Oracle Coherence 3.7.1
– Released September 23
– Strategic investment in:
• Exalogic Innovation
• Ease of Use
• Integration Points
– Significant hardening
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29. Elastic Data
Virtual Memory for Your Data
• Revolutionizes the scale of data grids
• Use block storage for cached data
– Optimized for flash
Cache Cache Cache
– Works well with NAS, Disk, etc. Server 1 Server 2 Server 3
• Simplifies capacity planning and
deployment Heap Heap Heap
– Configure amount of on-heap data to store
– Overflow data written to block storage
– Data stored in buffers until flushed to journal
– Reduces chances of Out-Of-Memory errors
Flash Flash Flash
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30. REST Support
Simplified Integration
Scalable Distributed Cache to Scalable, Event-Driven Real-Time Applications
Java / C++ / .NET
REST (Python, PHP,…)
Aggregation
Query Filter Grid Real-Time
Map API (Map-Reduce)
API Processing API Client API
API
Coherence Replication Coherence
Cluster Cluster
Per Member
Data
Cache
Source
Store/Loader API
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31. Coherence API Types
Scalable Distributed Cache to Scalable, Event-Driven Real-Time Applications
Java / C++ / .NET
REST (Python, PHP,…)
Aggregation
Query Filter Grid Real-Time
Map API (Map-Reduce)
API Processing API Client API
API
Coherence WAN Coherence
Cluster Replication Cluster
Per Member
Data
Cache
Source
Store/Loader API
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32. Query Explain Plan
• Evaluate query cost and
index effectiveness
• Quicker time to market of
optimized Coherence
solutions
• Explain Plan
• Provides the estimated cost of
evaluating a filter as part of a
query operation
• Trace
• Performs the associated query
• Provides the actual cost of
evaluating a filter as part of a
query operation.
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33. Live Objects
New Coherence Incubator Project
• Building blocks for an event-
driven finite-state machine
– …that is distributed
– …and scalable
– …and resistant to machine failure
• Architectural possibilities:
– Stated Event Driven Processing
– Ripple Effect
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34. Coherence on Exalogic Engineered System
Optimized Scalability and Performance in a Box
• Coherence optimized for Exabus 4x Throughput,
• Pre-configured network/compute nodes 1/6th Latency!!!
• Elastic Data: Expand Capacity with Flash
• Easy deployment as demand spikes
• Grow from ¼ to multi-rack
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35. Coherence Integrations
Oracle IGBU Oracle Golden Gate WebCenter
Oracle CGBU Meta-data Services (MDS) SOA BPEL
Oracle GlassFish Oracle ADF SOA Human Workflow
Oracle Access Manager Oracle PeopleSoft SOA Business Rules
Oracle CEP Oracle Secure Token Service SOA Coherence Adapter
Real-time Decision Oracle Identity Federation SOA Technology Adapters
TopLink
ATG BI Publisher
WebLogic Portal
Oracle Entitlement Services
Service Delivery Platform
Oracle BEAM
WebLogic Server
Oracle Web Services Manager
Oracle Service Bus
Advanced Capabilities
SOA Infrastructure Data Grid ATG Hosted Services
Data Cache
Oracle Data Integration Simple Clustering
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36. GoldenGate/Coherence Integration
Custom Coherence TopLink Grid
Update GoldenGate
Packaged Enabled Coherence
Cohere
Cache Updater Java Client
Application Application nce
• Simplifies Coherence use in shared
database environment
<entity
– Propagates DB updates to Coherence
class="Address">
<table
name="ADDR”/>
<attributes>
Coherence
– GoldenGate captures changes to
<id name="id">
<generated-
value />
Database Log Stream Capture
</id>
database tables
Direct Database Updates
<version
name="ver“/>
</attributes>
Update </entity>
– TopLink maps database changes to Coherence
cached objects JPA
Metadata
Read/Write Through
– Availability—2012
– GoldenGate 11gR2
– Available as patch to WebLogic 12.1.1
and 11.1.1.6/10.3.6
– Oracle database only
Database
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37. Oracle Enterprise Pack for Eclipse (OEPE)
• Coherence Project Configuration
– Facets
– Library Management
– Descriptor Generation
• Runtime Configuration
– Launch Config Editor
– Run/Deploy/Debug from IDE
• Configuration Support
– tangosol-coherence-override.xml editor
– Validation
– Context Sensitive Help Integration
– Cache Configuration Editor (OEPE 11.1.1.8)
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38. Oracle Enterprise Manager 12C
Plan
Optimize
DEEP PERFORMANCE VISIBILITY &
ALERTS-BASED MONITORING Meter & Setup
Charge Applications and
REAL-TIME JVM DIAGNOSTICS FOR Business Services
COHERENCE NODES* Platform as a Service
DBaaS MWaaS
Build
CENTRALIZED CACHE DATA
Infrastructure
MANAGEMENT Manage as a Service
AUTOMATED PROVISIONING
Test
Monitor
Deploy
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39. Oracle Enterprise Manager 12C
Monitoring & Diagnostics
Deep Performance Visibility & Alerts Based Proactive Monitoring
• Completely customizable performance views – save different views for different monitoring
requirements of the enterprise
• Proactive Alerts and notifications based on granular thresholds
• Topology view to show associations and dependencies
• Log monitoring – generate Alerts based on log patters
• Advanced monitoring – push replication, reap sessions, transactional caches, etc
Real-time JVM Diagnostics for Coherence Nodes
• Real-time threads analysis – find call stack, locks, method local variables, etc
• Real-time heap analysis – heap distribution in different spaces (eden, perm-gen, etc),
garbage collection
• Differential heap analysis – pin point heap leaks by comparing two snapshots
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40. Oracle Enterprise Manager 12C
Administration & Provisioning
Centralized Cache Data Management
• Query based data operations
• Central UI for key cache operations – view, update,
import, export, purge, add/remove indexes
• Drastically saves time in cache operations and makes it
easy for administrators
• Save queries for future references
Automated Provisioning
• Maintain ‘gold image’ in the software library
• Provisioning new cluster or add nodes to an existing
cluster
• Support Unicast Addrress as well as WKA based clusters
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41. For More Information
• General Information: http://coherence.oracle.com
• Coherence Training: http://education.oracle.com
• Coherence Discussion Forum: http://forums.oracle.com
• Coherence User Group on Linkedin
• “Oracle Coherence 3.5” by Aleks Seovic
• Email: craig.blitz@oracle.com
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42. Q & (hopefully) A
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