this presentation courtesy of the international soa symposium

1

1

Founding Sponsors

This Presentation Courtesy of the

International SOA Symposium

October 7-8, 2008 Amsterdam Arena

www.soasymposium.com

[email protected]

Gold Sponsors

Platinum Sponsors

Silver Sponsors

<Insert Picture Here>

SOA on App Grid: Not Your MOM‟s Bus

Dave Chappell

VP & Chief Technologist, SOA

2

The following is intended to outline our general

product 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.

4

Next Level Challenges of SOA

• Re-Focus on Innovation, New Business Services• Service Enablement of IT Assets

• Leverage investment, reuse

• Business Agility• Better align with the Business Needs

• Automate Business Function

• Business Process Orchestration

• Composite Applications

• Flexibility• Loose Coupling, Modularity

• Easily integrated, upgraded, replaced

• IT Cost Reduction• Shared Services

• Eliminate Redundancy

Large XML Payloads

Unexpected Usage Demands

Tearing Down / De-coupling

Silos

Sharing InformationAcross Multiple

Services

Meeting SLAExpectations

Increased Cost of Transacting

Benefits New Challenges

Moving Forward While Reducing Complexity

3

5


"If you want a 3 second response

time, you get 1 sec in the

presentation layer, 1 sec in the

middle tier, and 1 sec in the

backend. Period!”*

*commenting on the need for middle tier caching and

in-memory state management

VP Architecture

Leading Commercial Rating and

Information Insight Company

6

Concerns linger about lack of performance,

scalability

52%

52%

54%

59%

61%

65%

60%

54%

55%

60%

58%

64%

36%

53%

65%

53%

62%

0% 20% 40% 60% 80%

Lack of security

Too difficult to adapt and build new applicatons

Does not meet business needs

Poor reliability

Difficult to manage services

Performance or scalability issues

2005

2006

2007

Q: What would constitute SOA failure? 2007 Base: 549 / 2006 Base: 473 / 2005 Base: 167 (Among current deployers)

*

* Not asked in 2005

4

8

Service Grid(a.k.a. SOA on App Grid)

9

Service GridCombines Orchestration, Mediation, State Caching,

Demand based provisioning, Deterministic GC

Portal

BPEL

My

Services

CRM /

ERP

CEP Rules

2 3 4

5

1

Service

Provider

BAM

Service

Consumer

WS-Addr

<ReplyTo>

Callback

WS-Addr

<ReplyTo>

Callback

State-aware continuous availability for service infrastructure, services, application data, and processing logic

=Service state data2 3 4

5

1

=Stateful Service Orchestration

5

10

SOA on App Grid Value Proposition

• Reduce cost of hitting back-end systems multiple times over the life of a

session

• Improve response times for service requests and front-end users while

keeping load off backend systems

• Improved fault tolerance without sacrificing performance

• Ensuring predictable latency under increased sustained loads

• Achieving dramatic overall increase in performance and throughput

• Predictable scalability for XTP

• Based on advanced software clustering techniques

• Scales out linearly, whether 2 or 2,000 servers

• Heterogeneous Environment

• High-end / low-cost commodity hardware

11

Key Capabilities

• Caching Results of Services Calls

• To offload loads on backend services and SOA Infrastructure use side cache to

cache results of service calls, c.f. Web cache

• Without sacrificing consistency and programmability

• Seamless State Management for Stateful Services

• Without the need to enforce “state passing” / scrap book design patterns across the

organization

• Reduced/Eliminate dependency on disk persistence and State Repositories

• Without sacrificing HA

• Ability to Efficiently Handle Process XML Payloads within SOAs

• Without Loss in throughput and increases in latency s

• Data Grid and Compute Grid

• Linearly scalable shared memory and logic

• Intelligent co-location and affinity between processing logic and Grid storage

6

12

Data/State Management Techniques in SOA

Service Sophistication,

Longevity

Complexity

Stateless

Tight Coupling

Stateful

Cookies +Servlet Session

State passing viaXML Payloads

Between services

2 3 4

5

1

Service State

RepositoryService

13

SOA on App Grid Here and Now - Case Studies

7

14

Amir Razmara

Director, The Gap

Problem:

Universal user profile, preferences, shopping cart and single sign-on across 4 brands

Shared sessions across all 4 brands leading to a need for a global session (each brand has its own cluster of servers)

Possible Solutions:

State repository with DB

Data Grid backed session management with Coherence*Web

Coherence*Web Solution:

Create a “Global Cache Cloud” to maintain a brand agnostic session caching layer, enabling the global session

Any server from any brand is able to obtain a session from the global cache cloud, enabling SSO and shared bag

Session durability, immune from crashes in the application tier

Sessions maintained during the nightly cell switch for publishing content

*GAP - OOW2008 S299392 - Beyond Performance: Pushing Transaction…

15

State Management in SOA on App Grid | Web Cache

Shared Servlet Session State

Database:

Use for storing, managing transactions

Data Grid: Web Containers share servlet session state via data grid

Business logic: Traditional middle tier EJB

Web Cntr

Web Cntr

DB Grid

POJO

EJB

EJB

.NET

*GAP

8

16

State Management in SOA on Application Grid | Shared

Services

PrimaryNode

= Write Behind Queue

DB Grid

Service

Component

Service

Component

Service

Component

Service

Component

Database:


Write through for state information from data grid

Data Grid:

Use for managing transient state (read and write)

Business logic:

Services manage their state in the data gridWeb Cntr

Web Cntr

Similar architecture, similar benefits!

17

Kiran Dattani

Director SOA & Enterprise Integration

Pfizer Worldwide Technology

Problem

Need Sub 2.5 second response times –boundary crossing incur cost

Need to trade-off – between agility and performance

Possible Solutions

XML processing hardware/ appliances

Mid-tier caching / data grid

Solution Adopted

Data grid to cache data for real-time SOA

Real-time SOA:

Architect the overall process and application for performance

The more backend systems you hit during a real-time process, the more response time costs you incur

Make up for the loss in performance from boundary costs by:

Doing work in advance – Cache

Do less work in real-time

9

18

FIDUCIA IT AG manages and runs …

• 63M bank accounts

• 5.5M online accounts

• 3.4B funds transfers / year

• 13.5B IMS transactions / year

• Peak: 70M Transactions / day2,750 Transactions / Second

• 101.000 PCs

• 9,500 Routers

• 9,300 Switches

• 10,700 ATMs

• 10,023 Statement Printers

• 1,192 Service Terminals

*Fiducia - OOW2008 S299392 - Beyond Performance: Pushing Transaction…

19

<Insert Picture Here> Mathis Schorer

Architect

Fiducia

Problem

Make use of existing Services in a batch driven environment

Make better use of existing hardware resources

Push throughput & Make use of high parallelism

Move workload off the mainframe = saving MIPS, Saving Euros!

Solutions Considered

(1) Queuing; (2) Data Grid

Caching of Mainframe Data

Improved service performance across all servers

Reduction in mainframe MIPS usage

Replacing Queues with Coherence

Much easier Setup

Improved performance

Drastically reduced load on Oracle Database

Ability to form a “cloud” of computing resources

*Fiducia - OOW2008 S299392 - Beyond Performance: Pushing Transaction…

10

20

SOA on App Grid Patterns for Shared Service State

21


“Composable stateful services is an architectural challenge we are

struggling with as we build out our new e-learning enterprise

architecture. We are very concerned about scalability using

conventional approaches

You definitely got the attention of our architects and engineers who

are now thinking about how SOA grid technology can help”

Director Architecture

Leading Educational Publishing Co.

11

22

SOA Grid Patterns

• SOA Grid: Basic Patterns for State Management Using Middle Tier Caching• Fault Tolerant Collection

• Inline Service State Cache

• Side Cache

• Stateful Service Load Balancing

• Stateful Service Availability/Failover

• Advanced State Management Patterns• State Based Notification

• Asynch DB Update

• Near Cache Optimization

• Sticky Load Balancers and Cached Service State

• Business Logic Affinity

• Claim Check/Pass-by-ref/Deferred State

23

Fault-Tolerant Collection - Primary/Backup

Synchronization

Application

Object

Application

Object

P

B

Put()

Backup Node[1...n]

PrimaryNode

In-Memory State Storage / Networked Datagrid

servers/Hashmap

iFace

HashKey/CacheKey

Available Today with Oracle Coherence

12

24


Synchronization

P

BBackup Node

PrimaryNode


FT Collection

Application

Object

Put()

HashKey/CacheKey

In-Memory State Storage / Networked Datagrid servers/

HashmapiFace

Application

Object

Application

Object

25


Synchronization


FT Collection

Application

Object

Put()


HashmapiFace

Application

Object

Application

Object

P

B

Backup Node

PrimaryNode

X

13

26


Synchronization


FT Collection

Application

Object

Put()


HashmapiFace

Application

Object

Application

Object

P

BBackup Node

PrimaryNode

X

28

Service State Repository

Service

Component

Service

Component

Service State

Service State

State Repository

Service

Component

Service

Component Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

14

29

In-Memory FT Service State

Service

Component

Service

Component

Service State

Service State

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

= Primary / Backup memory storage

30

In-Memory FT Service State

Service

Component

Service

Component

Service State

Service State

Service

Component

Service

Component Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

Service

Component

= Primary / Backup memory storage

15

31

Inline Shared State Cache

P

B

Put(StateData)

Cachekey

Backup Node

PrimaryNode

Service ‘A’

Invocation

Load B

ala

ncer

Shared State Cache

WSA:StatekeyService

Provider

A

Service

Provider

A1

Service

Consumer

Here today: Usage Patterns with Oracle Coherence and ESB/BPEL

32

After | Caching Results to Service Calls to Backend Systems

Process Flow /

Orchestration

ESB M

edia

tion

(Tra

nsfo

rm, R

oute

, Retry

)

Legacy

MQService

Provider

Service

Consumer

Service

Provider

Healthcare Portal: Minimizing expensive backend access

P

BBackup Node

PrimaryNodePut (State Data)

CacheKey

Cache

Side-

Cache

Service

16

33

After | Caching Results to Service Calls to Backend Systems

Process Flow /

Orchestration

Legacy

MQService

Provider

Service

Consumer

Service

Provider

P

BBackup Node

PrimaryNodeGet(Cachekey)

State Data

Cache

Side-

Cache

Service

Healthcare Portal: Minimizing expensive backend accessESB M

edia

tion

(Tra

nsfo

rm, R

oute

, Retry

)

35

Side-Cache in OSB (ALSB) Proxy / Pipeline

(Telco Use Case)

Telco: Usage Patterns with Oracle Coherence and OSB

17

41

Service

Provider

Service

Provider

Service

Provider

Near-Cache Optimization

P

B

Fetch

Cache

Backup Node

PrimaryNode

DB Grid

Geico: 400X Improvement on Reads, 40X Improvement on Writes


Service

Consumer

Service

Consumer

46

Transform

& RouteSvc CSvc BSvc A

2 3 4

5

1

Relational Store

(Oracle DB)Svc A

SQLSvc B

SQLSvc C

SQL

Shared Service State Repository Revisited

Svc D

Svc A Svc B Svc C

Service Bus / BPEL

Transform

& Route

In Memory Data Grid w/ Async Write-Behind, Sync Write-thru, Cached-Read

18

47

SOA on App Grid

Not Your MOM‟s Bus

48

SOA Grid – Not Your MOM‟s Bus

Why send it when its already there?

Portal

BPEL

CRM ERP

CEP Rules

2 3 4

5

1

Web Service

Provider

BAM

Web

Service

Consumer

WS-Addr

<ReplyTo>

Callback

WS-Addr

<ReplyTo>

Callback

•This is already accomplished today using stateful

service caching patterns with out-of-box capabilities of

Oracle SOA Suite and Coherence

*Fiducia

19

49

Case Study: Computing versus Communicating

46 Boundary Crossings

46 network

hops/data

serialization

steps

5-7

5 - 7 network

hops/data

serialization

steps

Horizontally

scalable

2 2 40

2Mediation

Custom Redundant

Data Cache

CustomRedundant

Data Cache

Biz LogicClient

Conventional SOA

Service

Service

Service

Process Flow

ESB

Media

tion

SOA Grid

Client

Coherence

50

Learn Morehttp://www.oracle.com/technology/products/

• Whitepapers

• Webcasts

• Buyers Guides

• Analyst Reports

• Case Studies

• Podcasts

• Technical Information & Forums• http://www.oracle.com/technology/products/webservices_manager

• http://ws-security.blogspot.com (Blog)

• http://blogs.oracle.com/davidchappell/ (Blog)

http://www.oracle.com/technology/products/webservices_manager

http://ws-security.blogspot.com/



http://blogs.oracle.com/davidchappell/

20

51

Questions, Feedback, Use Cases

• Mohamad Afshar

• [email protected]

• Dave Chappell


52

http://www.tangosol.com/customerstory-jivesoftware.jsp

mailto:[email protected]

21

53

SOA on App Grid Patterns for Shared Service State

54

SOA Grid Patterns

• SOA Grid: Basic Patterns for State Management Using Middle Tier Caching• Fault Tolerant Collection

• Inline Service State Cache

• Side Cache



• Advanced State Management Patterns• State Based Notification





• Claim Check/Pass-by-ref/Deferred State

22

55


Synchronization

Application

Object

Application

Object

P

B

Put()

Backup Node[1...n]

PrimaryNode

In-Memory State Storage / Networked Datagrid

servers/Hashmap

iFace

HashKey/CacheKey


56


Synchronization

P

BBackup Node

PrimaryNode


FT Collection

Application

Object

Put()

HashKey/CacheKey


HashmapiFace

Application

Object

Application

Object

23

57


Synchronization


FT Collection

Application

Object

Put()


HashmapiFace

Application

Object

Application

Object

P

B

Backup Node

PrimaryNode

X

58


Synchronization


FT Collection

Application

Object

Put()


HashmapiFace

Application

Object

Application

Object

P

BBackup Node

PrimaryNode

X

24

59

Inline State Cache

P

B

Put(StateData)

Cachekey

Backup Node

PrimaryNode

Service ‘A’

Invocation

Load B

ala

ncer

State Cache

WSA:StatekeyService

Provider

A

Service

Provider

A1

Service

Consumer


60


• Whitepapers

• Webcasts

• Buyers Guides

• Analyst Reports

• Case Studies

• Podcasts









25

61


• Mohamad Afshar


• Dave Chappell


62



26

63


Appendix

64

More Patterns Available

• Claim Check / Pass-by-ref / Deferred State



• State Based Notification





27

65

For More Information

• Dave Chappell Blog –

• http://blogs.oracle.com/davidchappell• SOA Magazine – SOA – Ready for Primetime: The Next Generation,

Grid Enabled SOA

• http://www.soamag.com/I10/0907-1.asp

• SOA Magazine –The Next Generation, Grid Enabled SOA : Not Your MOM‟s Bus


• XTPP – Gartner Research ID # G00151768 – Massimo Pezzini

• Data Grid –

• http://www.oracle.com/products/middleware/coherence/index.html

• General SOA Information –

http://www.oracle.com/technologies/soa/index.html• http://www.oracle.com/soa

• SOA Best Practices: The BPEL Cookbook

66

Industry Coverage

Top 10 Product in Network World Next

Generation Data Center Product Review

‟With Coherence, performance has

improved by as much as 100 times …‟

Network World, March 2007

…the result is almost linear scalability…

…from 2 million to more than 60 million “aggregations” per second…

(based on a benchmark initiated by an investment-bank)

February 2007

http://www.gridtoday.com/grid/1142166.html

http://blogs.oracle.com/davidchappell

http://www.soamag.com/I10/0907-1.asp



http://www.oracle.com/products/middleware/coherence/index.html

http://www.oracle.com/technologies/soa/index.html

http://www.oracle.com/soa

28

67

Over 1,000 production installations!

Sample Customers

68


How do you make this

happen?





29

69

Apps

Adapters B2B

PartnersDB Legacy

CONNECTIVITY

Routing Transform DataQuality

ETL &Replication

Data IntegratorOracle Service Bus

Mediation

ROUTING & DATA SERVICES

NativeBPEL

Business Rules

Human Workflow

BPEL Process Manager

ORCHESTRATION

Coherence Cache

J2EE Application Server(Oracle AS, WebLogic, WebSphere, JBoss)

Messaging

Oracle SOA Suite

GOVERNANCESystem

Monitoring

Enterprise Manager

UDDI

WS PoliciesSecurity

Web Services Manager

Registry

GOVERNANCE

SOA lifecyclegovernance

EnterpriseRepository

JRockit VM & RT

REAL-TIMEVISIBILITY

& PROCESSING AlertsBusiness Monitoring

BAM

EventsData Streams

CEP

ApplicationDevelopmentFramework

EnterpriseModeling

BPA Suite

JDeveloper

Business UserModeling

BPM Suite

70

10110001011001011101100101100101110001110

101100010110010111011001011001011100011101111110001110

Application Servers

Web Servers

Ever Expanding Universe of Users

Oracle Coherence

• Oracle

Coherence

brokers Data

Supply with

Data Demand

• Scale out

Data Grid in

middle tier

using

commodity

hardware

Data Sources

Data Supply

Data Demand

Java Objects

WebLogic Suite

30

71

JRockit Real Time

• Deterministic, Extreme Low

Response JVM

• Deterministic Garbage Collection with

1ms guarantee

• Under heavy load

• Design goal – Pause less!!

• „Swap out‟ any JVM with Zero-

coding for instantaneous GC

predictability

• No code re-writes

• Advanced tools for monitoring and

tuning application performance

JRockit -- the Industry‟s FASTEST Real Time solution for Standard

Java

0

10

20

30

40

50

60

99.9 pctl 99 pctl 95 pctl 80 pctl median average

res

po

ns

e tim

e (

ms

)

Response times under fixed load

JRockit Det GC

Tuned Sun VM

JRRT Sun Java

JRRTJava

72

WebLogic Operations ControlMonitoring and Service Management

Dynamic Resource

Allocation

• SLAs by way of WLOC

policies and rules

• Constant rules evaluation

• Dynamic scale management

Protects app availability

• Failover within the pool

• Failover across pools

• Rules can protect

performance as well as

availability

Active monitoring, alerting

• What WLOC starts, it

monitors, protects

• Agent-based framework

31

73

WL EM Diagnostic PacksManagement and Monitoring

• Manage multiple WebLogic domains from a single console

• 24x7 monitoring of availability, performance, load, and usage metrics of WebLogic Server and the host

• Blackouts, events, notifications, and metrics comparison

• Real-time and historical performance monitoring

• Historical trends of metrics and events

• Reports

• End User Monitoring

• System/Service Modeling and Dashboards

74

Oracle Fusion Middleware

32

75


• Whitepapers

• Webcasts

• Buyers Guides

• Analyst Reports

• Case Studies

• Podcasts




76


• Mohamad Afshar


• Dave Chappell









33

77

For More Information

• Dave Chappell Blog –

• http://blogs.oracle.com/davidchappell• SOA Magazine – SOA – Ready for Primetime: The Next Generation,

Grid Enabled SOA


• SOA Magazine –The Next Generation, Grid Enabled SOA : Not Your MOM‟s Bus


• XTPP – Gartner Research ID # G00151768 – Massimo Pezzini

• Data Grid –

• http://www.oracle.com/products/middleware/coherence/index.html

• General SOA Information –

http://www.oracle.com/technologies/soa/index.html• http://www.oracle.com/soa

• SOA Best Practices: The BPEL Cookbook

78


Appendix Appendix

Extra Slides






http://www.oracle.com/technologies/soa/index.html

http://www.oracle.com/soa

34

79

What does it mean to get it right?

CPUs

transact

ions per

second

What happens

in practice

Linear Scale up

Time

Latency

Predictable latency

What happens

in practice

So what do you do when you don‟t get it right?

-Revisit how the application was architected

-Throw hardware at the problem OR

-Make sure you architect it so that it‟s scalable first time around!

80

JRockitHigh Speed JavaVM with Predictable Performance

Code Generation

Thread Mgmt. Language Model

I/O

External Interfaces

Memory & Resource

Management

Traditional Java VM – Stop the World Pauses

JRockit - < 10 ms Pause

35

81

WebLogic ServerJava Enterprise Platform

JPA (TopLink)

OSGI Microkernel

Web Services

SecurityJavaServer

Faces

WebLogic Server (Java EE 5.0)

Clusters, Messaging, Transactions, Workload Mgt, JMX

Services Component Architecture Fabric

WebLogic Operations

Control

82

CoherenceReliable, Transactional, In-Memory Data Grid

Database Access & O-R Mapping

Java, JavaEE, .NET & Other Applications

Better Throughput &Lower Latency

Guaranteed Zero Data Loss

Dynamic Capacity Addition

Efficient Data Access

36

83

Oracle Service BusUnmatched Performance & Scalability

22.7 Million Messages Per Hour

• Oracle Service Bus 10gR3

• Intel Tigerton Processors

• Oracle Enterprise Linux 5.0

• 4X2.93GHz Quad Core Processors

• 6 GB RAM Per Server

• Number of Process Steps/Instance = 2

• Number of Transformations = 1

• Size of Document Payload = 5 KB

84

Oracle BPEL Process ManagerUnmatched Performance & Scalability

3.5 Million Process Instances Per

Hour

• Oracle BPEL Process Manager 10gR3

• Intel Tigerton Processors




• Number of Process Steps/Instance = 8

• Number of Transformations = 4


37

85

Oracle AIA for Communications 2.0Leverages BPEL Performance & Scalability

185,400 Business Transactions Per

Hour

• Oracle BPEL Process Manager 10gR3

• Siebel CRM 7.8.2; Oracle BRM 7.3

• Intel Xeon Processors

• 2x3.6 GHz Processor


• Windows 2003 Service Pack 1


• Number of XML Schema Elements = 62

86

Oracle Complex Event ProcessorUnmatched Performance & Scalability

1 Million Events Processed per

Second

• <1 Millisecond Latency for Events

• 1000s of Rules & Event Queries

• Events from Java, Queues, DBMS, SOA

• Oracle Event Server 10gR3




38

87

Get Customer Activities

*Pfizer - OOW2008 S299392 - Beyond Performance: Pushing Transaction…

88

*Find Customer BPEL Process

• Web 2.0 apps demand real-time SOA

• Response time limited by slowest system in a real-time SOA / BPEL flow

• Ask yourself are you getting the benefit of the real-time call? If not, why pay SOA tax?

• Do work in advance by pre-populating the data grid with data

• If possible cache the entire results / output of the flow

• Cache results to service calls as a façade that are part of the flow

• Reduce amount of work at run-time

• This is easy to put together with BPEL PM and Coherence

*Pfizer - OOW2008 S299392 - Beyond Performance: Pushing Transaction…

39

89

Before | Service Calls to Backend Systems

Process Flow /

Orchestration

ESB M

edia

tion

(Tra

nsfo

rm, R

oute

, Retry

)

Legacy

MQService

Provider

Service

Consumer

Service

Provider


90

Relational Store

(Oracle DB)

Sta

te T

able

Svc A

SQL

Sta

te T

able

Svc B

SQL

Sta

te T

able

Svc C

SQL

Traditional SOA Stateless/State

Passing Model

Svc D

Stateless

Svc AStateless

Svc B

StatelessSvc C

Service Bus / BPEL

Transform

& Route

Carry XML From Service to Service

2 3 4

5

1

40

91

Relational Store

(Oracle DB)

Sta

te T

able

Svc A

SQL

Sta

te T

able

Svc B

SQL

Sta

te T

able

Svc C

SQL

Shared State Repository

Svc D

Svc A Svc B Svc C

Service Bus / BPEL

Transform

& Route

Implement the “Claim Check” Pattern

2 3 4

5

1

92

2 3 4

5

1

Relational Store

(Oracle DB)Svc A

SQLSvc B

SQLSvc C

SQL

Shared Service State Repository

Revisited

Svc D

Svc A Svc B Svc C

Service Bus / BPEL

Transform

& Route

In Memory Data Grid w/ Async Write-Behind, Sync Write-thru, Cached-Read

41

94

SOA on App Grid Case StudyWeb-Tier

95

The Universal Experience: Sister Tabs


42

96

The Universal Experience: Shopping Cart


97

State Management in SOA on App Grid | Web

Cache

Shared Servlet Session State

Database:


Data Grid: Web Containers share servlet session state via data grid

Business logic: Traditional middle tier EJB

Web Cntr

Web Cntr

DB Grid

POJO

EJB

EJB

.NET

*GAP

43

98

State Management in SOA on Application Grid |

Shared Services

PrimaryNode

= Write Behind Queue

DB Grid

Service

Component

Service

Component

Service

Component

Service

Component

Database:


Write through for state information from data grid

Data Grid:

Use for managing transient state (read and write)

Business logic:

Services manage their state in the data gridWeb Cntr

Web Cntr

Similar architecture, similar benefits!

99

SOA on App Grid Case StudyCaching Middle Tier, Offloading

Backend Systems

44

100


“It’s hard to beat zero!”

Kiran Dattani

Director SOA & Enterprise Integration

Pfizer Worldwide Technology

101

After | Caching Results to Service Calls to

Backend Systems

Process Flow /

Orchestration

ESB M

edia

tion

(Tra

nsfo

rm, R

oute

, Retry

)

Legacy

MQService

Provider

Service

Consumer

Service

Provider


P

BBackup Node

PrimaryNodePut (State Data)

CacheKey

Cache

Side-

Cache

Service

45

102

After | Caching Results to Service Calls to

Backend Systems

Process Flow /

Orchestration

Legacy

MQService

Provider

Service

Consumer

Service

Provider

P

BBackup Node


State Data

Cache

Side-

Cache

Service

Healthcare Portal: Minimizing expensive backend accessESB M

edia

tion

(Tra

nsfo

rm, R

oute

, Retry

)

103

After | With Fault Tolerance

Process Flow /

Orchestration

Legacy

MQService

Provider

Service

Consumer

Service

Provider

P

BBackup Node


State Data

Cache

Side-

Cache

Service

ESB M

edia

tion

(Tra

nsfo

rm, R

oute

,, Retry

)

X


46

104

Side-Cache in OSB (ALSB) Proxy /

Pipeline (Telco Use Case)


this presentation courtesy of the international soa symposium

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