Innovations in Grid Computing with Oracle Coherence

Innovations in Grid Computing with Oracle
Coherence
Craig Blitz
Oracle Coherence Product Management
1 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Insert Information Protection Policy Classification from Slide 8

The following is intended to outline general product use 
and direc4on. It is intended for informa4on purposes only, 
and may not be incorporated into any contract. It is not a 
commitment to deliver any material, code, or func4onality, 
and should not be relied upon in making purchasing 
decisions. 

The development, release, and 4ming of any features or 
func4onality described for Oracle’s products remains at the 
sole discre4on of Oracle. 


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 Direc8on and Latest Features 


Internet Scale

Problem: How to deal with the ﬂood of customer demand for 
services that occurs when you expose your infrastructure to a 
large, poten4ally unbounded network. 

‐ Or, stated more posi4vely ‐  

Opportunity: How to build infrastructure that allows you to 
scale business and improve margins by exposing your services 
to a large, poten4ally unbounded network. 


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


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


Oracle Coherence

•  Elastic caching is “the secret sauce for internet scale”
•  In-Memory Data Grid pioneer since 2001
•  Gartner’s Leader’s Quadrant for Enterprise Application Servers,
2009


More. Faster. Cheaper.

• Scale to more users and more data 
• Scale predictably and cost‐eﬀec4vely 
• Act on data in real‐4me  
• Reduce spending on backend systems  Get on the Grid 


Oracle Coherence

•  Coherence: in-memory data grid
End Users 
–  Distributed caching to applications
–  Scalable extreme transaction
processing Applica4on 
Servers 
–  Real-time eventing, query, and map/
reduce aggregations
–  Abstraction from back-end data Applica4on 
sources Objects 
–  High-availability to applications

Mainframes, 
Databases, etc. 


Oracle Fusion Middleware
Complete, Open, Integrated, Best-in-Class

Web  Mobile  Social 

User Engagement 

Content Management 
Iden4ty 
Management 
Business Intelligence 

Development  Business Process 
Tools  Management 

Service Integra4on 

Enterprise 
Management 
Data Integra4on 

Cloud Applica4on Founda4on 


To Understand…




Enterprise Application Scalability Challenges

Ease of Scaling 

Service Clients  •  Grows and scales 
Browsers, supplier and partner clients, applica4on  naturally 
clients, mobile apps, and other service consumers. 

•  Increasing load 
Service Implementa8on  •  Computa4onally 
Custom applica4ons, 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 applica4ons, and so on.  •  High latency, SPOB, 
and SPOF 


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  •  Inconsistent view of data across
Node N 
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 


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 


How to Integrate with Coherence
Two Options
Custom integration through Coherence API

- or –

Through Existing ‘Switch-On’ OOTB Integrations

Oracle TopLink Grid: JPA object-relational data
Oracle Coherence*Web: HTTP sessions
Other Oracle Products that integrate Coherence


Coherence Basic Inteface
Custom Integration
•  Application uses NamedCache neCache = CacheFactory
.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


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


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.


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.


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


Data Source Integration
Write Behind

•  All data writes occur through cache
•  Updates to the cache are written asynchronously to the
data source


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


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 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)


Data Processing
Events

•  Build Complex Event-Driven Apps
•  Java Bean Model
•  Key-Based
•  Filter-Based


To Understand…



3) Coherence’s Strategic Direc8on and Latest Features 


Release Overview

•  Oracle Coherence 3.7.1
–  Released September 23
–  Strategic investment in:
•  Exalogic Innovation
•  Ease of Use
•  Integration Points
–  Significant hardening

27 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Insert Information Protection Policy Classification from Slide 8 © 2011 Oracle Corporation

"Oracle Coherence has been a market leader helping customers scale 
applica4ons to meet the demands of internet‐scale compu4ng for over a 
decade now.  Coherence 3.7.1 con4nues to reduce complexity, ease 
integra4on, and accelerate 4me to market of scalable solu4ons...  At the 
same 4me, when running on an Oracle Exalogic engineered system, 
Coherence 3.7.1 leverages Oracle's Exabus technology to achieve the lowest 
latencies and highest throughput.” 

Cameron Purdy 
VP Development, Oracle 


Elastic Data
Virtual Memory for Your Data

•  Revolutionizes the scale of data grids Cache  Cache  Cache 
•  Use block storage for cached data Server  Server  Server 
–  Optimized for flash
–  Works well with NAS, Disk, etc.
1  2   3 
•  Simplifies capacity planning and Heap  Heap   Heap  
deployment
–  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 


REST Support

•  REST API
– cache creation
– data access & modification
– parallel aggregation/
processing/querying
•  Easy Integration
– iOS, Ruby, PHP, Python…
– Via the HTTP protocol
•  Compliments C++/.NET/
Java Support


Coherence API Types
Scalable Distributed Cache to Scalable, Event‐Driven Real‐Time Applica4ons 
Java / C++ / .NET 
REST (Python, PHP,…)

Aggrega4on 
Query Filter  Grid  Real‐Time 
Map API  (Map‐Reduce) 
API  Processing API  Client API 
API 

Coherence  WAN Replica4on  Coherence 
Cluster   Cluster  

Per Member 
Data 
Cache Store/
Source 
Loader API 


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.


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


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


GoldenGate/Coherence Integration

Custom  Coherence  TopLink Grid 
Update  GoldenGate  
Packaged  Enabled  Coherence  
Cohere
Cache Updater   Java Client
 
Applica4on  Applica4on  nce 

•  Simplifies Coherence use in shared
database environment
<entity

–  Propagates DB updates to Coherence
class="Address">
<table
name="ADDR”/>

Coherence 
<attributes>
<id name="id">
–  GoldenGate captures changes to <generated-

  Database Log Stream Capture 
value />
</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 


Coherence Integra8ons 

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 PeopleSoR  SOA Business Rules 
Oracle CEP  Oracle Secure Token Service  SOA Coherence Adapter 
Real‐8me Decision  Oracle Iden8ty Federa8on  SOA Technology Adapters 
TopLink 
ATG  BI Publisher 
WebLogic Portal 
Oracle En8tlement Services 
Service Delivery PlaQorm 
Oracle BEAM 
WebLogic Server 
Oracle Web Services Manager 
Oracle Service Bus 
 Advanced Capabili4es 
SOA Infrastructure   Data Grid  ATG Hosted Services 
 Data Cache 
Oracle Data Integra8on   Simple Clustering 


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 1.8)


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
•  My email: Email: craig.blitz@oracle.com


Q & (hopefully) A


Oracle Enterprise Manager 12C
Comprehensive Coherence Management

Performance 
Monitoring &  Administra4on 
Diagnos4cs 

Conﬁgura4on 
Provisioning 
Management  


Monitoring & Diagnostics

Deep Performance Visibility & Alerts Based Proac4ve Monitoring 
• Completely customizable performance views – save different views for different monitoring 
requirements of the enterprise 
• Proac4ve Alerts and no4fica4ons based on granular thresholds 
• Topology view to show associa4ons and dependencies 
• Log monitoring – generate Alerts based on log pajers 
• Advanced monitoring – push replica4on, reap sessions, transac4onal caches, etc 

Real‐4me JVM Diagnos4cs for Coherence Nodes 
• Real‐4me threads analysis – find call stack, locks, method local variables, etc  
• Real‐4me heap analysis – heap distribu4on in different spaces (eden, perm‐gen, etc), 
garbage collec4on 
• Differen4al heap analysis – pin point heap leaks by comparing two snapshots 


Administration & Provisioning

Centralized Cache Data Management  

•  Query based data opera4ons  
•  Central UI for key cache opera4ons – view, update, 
import, export, purge, add/remove indexes 
•  Dras4cally saves 4me in cache opera4ons and makes it 
easy for administrators 
•  Save queries for future references 

Automated Provisioning 

•  Maintain ‘gold image’ in the solware library 
•  Provisioning new cluster or add nodes to an exis4ng 
cluster 
•  Support Unicast Addrress as well as WKA based clusters 


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