Web Oriented Architecture at Oracle

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Information about Web Oriented Architecture at Oracle
Technology

Published on December 4, 2008

Author: EmilianoPecis

Source: slideshare.net

Description

Web Oriented Architecture using Oracle software

Web Oriented Architecture at Oracle Emiliano Pecis Technology Business Unit

Agenda

What is WOA

WOA and the “Network effects” People is: the “Network effects” Tags: facebook blogger wikipedia digg twitter fickr networkeffects

People is: the “Network effects”

<Insert Picture Here> “ In other words, the Web model provides a single, open, and unified information architecture that is consistent, easily consumed, extremely scalable, securable, very reusable, resilient, and highly federated.” Dion Hinchcliffe Founder, Hinchcliffe & Co. Dion Hinchcliffe’s Blog

WOA – Web Oriented Architecture Tags: URI WOA REST web markup resources

WOA – All About Resources Tags: WOA REST resources URI SOA HTTP

WOA vs SOA Tags: WOA REST resources ws-* rich reach pox json HTT

WOA is a substyle of SOA but… Tags: WOA REST SOA top-down bottom-up ws rich reach

WOA and low case semantic web Tags: browser rest ajax microformats web3.0 semantic lowcase

WOA and (server side) scalability Tags: WOA REST datagrid database scalability

WOA and (server side) scalability Client side Server side Tags: WOA REST datagrid database scalability

Client side

Server side

WOA at Oracle

Oracle Software involved in WOA Tags: ensemble webcenter portal weblogic framework REST disc portlet dvt coherence datagrid

WebCenter Ensemble Portal Portlet Portlet Portlet Portlet Oracle WebCenter Ensemble Consumer Widgets Java App WSRP REST .NET App .NET Control .NET Control .NET Control .NET Control Static HTML Other App: Cold Fusion, PHP, Perl, Ruby on Rails, REST Tags: ALUI Ensemble rest woa php perl ruby on rails wsrp

Portal Framework REST Services & Disc Tags: Portal REST DISC Weblogic Portlet ajax dvt

HTTP Portlet Publishing Tags: http portlet publishing webloigc portal igoogle widget

Dynamic Visitor Tool Tags: dynamic visitor tool svt ajax portlet customization web20 portal

Data Grid

Oracle Coherence Data Grid Provides a reliable data tier with a single, consistent view of data Enables dynamic data capacity including fault tolerance and load balancing Ensures that data capacity scales with processing capacity Data Services Mainframes Databases Enterprise Applications Real Time Clients Distributed in Memory Data Management Oracle Coherence Data Grid Tags: http portlet publishing webloigc portal igoogle widget Web services Web services

Provides a reliable data tier with a single, consistent view of data

Enables dynamic data capacity including fault tolerance and load balancing

Ensures that data capacity scales with processing capacity

Data Grid Uses Caching Applications request data from the Data Grid rather than backend data sources Analytics Applications ask the Data Grid questions from simple queries to advanced scenario modeling Transactions Data Grid acts as a transactional System of Record, hosting data and business logic Events Automated processing based on event Tags: datagrid caching analytics transactions events

Coherence: A Unique Approach Members share responsibilities (health, services, data…) Completely Peer-to-Peer No Single Points of Bottleneck (SPOBs) No Single Points of Failure (SPOFs) Linearly scalable to hundreds of servers by design No Masters / Slaves etc. Data Grid members work together as a team Tags: datagrid coherence members nomasters noslaves spf spb peer-to-peer

Members share responsibilities (health, services, data…)

Completely Peer-to-Peer

No Single Points of Bottleneck (SPOBs)

No Single Points of Failure (SPOFs)

Linearly scalable to hundreds of servers by design

No Masters / Slaves etc.

Data Grid members work together as a team

Partitioned Topology : Data Access Data spread and backed up across Members Transparent to developer Members have access to all Data All Data locations are known – no lookup & no registry! Tags: datagrid coherence members data access

Data spread and backed up across Members

Transparent to developer

Members have access to all Data

All Data locations are known – no lookup & no registry!

Partitioned Topology : Data Update Synchronous Update Avoids potential Data Loss & Corruption Predictable Performance Backup Partitions are partitioned away from Primaries for resilience No engineering requirement to setup Primaries or Backups Automatically and Dynamically Managed Write-behind Tags: datagrid coherence membersdata update

Synchronous Update

Avoids potential Data Loss & Corruption

Predictable Performance

Backup Partitions are partitioned away from Primaries for resilience

No engineering requirement to setup Primaries or Backups

Automatically and Dynamically Managed

Write-behind

Partitioned Topology : Recovery Membership changes (new members added or members leaving) Other members, using consensus , recover and repartition automatically No in-flight operations lost, no availability gap! Some latencies (due to higher priority of asynchronous recovery) Information Reliability & Continuous Availability are the priorities Tags: datagrid coherence recovery data

Membership changes (new members added or members leaving)

Other members, using consensus , recover and repartition automatically

No in-flight operations lost, no availability gap!

Some latencies (due to higher priority of asynchronous recovery)

Information Reliability & Continuous Availability are the priorities

Features : Traditional Implements Map interface Drop in replacement. Full concurrency control. Multi-threaded. Scalable and resilient! get, put, putAll, size, clear, lock, unlock… Implements JCache interface Extensive support for a multitude of expiration policies, including none ! More than “just a Cache”. More than “just a Map” Tags: datagrid coherence jcache get put

Implements Map interface

Drop in replacement. Full concurrency control. Multi-threaded. Scalable and resilient!

get, put, putAll, size, clear, lock, unlock…

Implements JCache interface

Extensive support for a multitude of expiration policies, including none !

More than “just a Cache”. More than “just a Map”

Features : Observable Interface Real-time filterable (bean) events for entry insert, update, delete Filters applied in parallel (in the Grid) Filters completely extensible A large range of filters out-of-the-box: All, Always, And, Any, Array, Between, Class, Comparison, ContainsAll, ContainsAny, Contains, Equals, GreaterEquals, Greater, In, InKeySet, IsNotNull, IsNull, LessEquals, Less, Like, Limit, Never, NotEquals, Not, Or, Present, Xor… Events may be synchronous* trades.addMapListener( new StockEventFilter(“ORCL”), new MyMapListener(…)); Tags: datagrid coherence observable interface

Real-time filterable (bean) events for entry insert, update, delete

Filters applied in parallel (in the Grid)

Filters completely extensible

A large range of filters out-of-the-box:

All, Always, And, Any, Array, Between, Class, Comparison, ContainsAll, ContainsAny, Contains, Equals, GreaterEquals, Greater, In, InKeySet, IsNotNull, IsNull, LessEquals, Less, Like, Limit, Never, NotEquals, Not, Or, Present, Xor…

Events may be synchronous*

trades.addMapListener( new StockEventFilter(“ORCL”), new MyMapListener(…));

Features : InvocableMap Interface Execute processors against an Entry, a Collection or a Filter Executions occur in parallel (aka: Grid-style) No “workers” to manage! Processors may return any value trades.invoke( new EqualsFilter(“getSecurity”,“ORCL”), new StockSplit(2.0)); Aggregate Entries based on a Filter positions.aggregate( new EqualsFilter(“getSecurity”,“ORCL”), new SumFilter(“amount”)); Tags: datagrid coherence processors collection filter

Execute processors against an Entry, a Collection or a Filter

Executions occur in parallel (aka: Grid-style)

No “workers” to manage!

Processors may return any value

trades.invoke( new EqualsFilter(“getSecurity”,“ORCL”), new StockSplit(2.0));

Aggregate Entries based on a Filter

positions.aggregate( new EqualsFilter(“getSecurity”,“ORCL”), new SumFilter(“amount”));

What is the Business Impact? Reduce infrastructure & dev cost Leverage IT investment, Grid Computing Build vs. Buy Scale as you grow Dynamic, predictable application scalability aligned with business needs Increase customer satisfaction Better application performance Faster access to data, shorter response times Enable business continuity Continuous data availability and reliability Meet / exceed service level agreements Tags: datagrid coherence customers references

Reduce infrastructure & dev cost

Leverage IT investment, Grid Computing

Build vs. Buy

Scale as you grow

Dynamic, predictable application scalability aligned with business needs

Increase customer satisfaction

Better application performance

Faster access to data, shorter response times

Enable business continuity

Continuous data availability and reliability

Meet / exceed service level agreements

Typical Scenario & collaterals

Typical Scenario

About Software

Collaterals About WOA Gartner’s definition Dion Hinchcliffe’s blog Oracle WOA enablers Weblogic Portal REST API Weblogic Portal Disc Framework Weblogic Portal “Portlet Publishing” Weblogic Server support for Rest Weblogic Server support for pub-sub server Ensemble for WebCenter Interaction Oracle Datagrid Oracle Coherence Others Microformats for lowcase Semantic Web

About WOA

Gartner’s definition

Dion Hinchcliffe’s blog

Oracle WOA enablers

Weblogic Portal REST API

Weblogic Portal Disc Framework

Weblogic Portal “Portlet Publishing”

Weblogic Server support for Rest

Weblogic Server support for pub-sub server

Ensemble for WebCenter Interaction

Oracle Datagrid

Oracle Coherence

Others

Microformats for lowcase Semantic Web

Q & A

 

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