Data Services for Service Oriented Architecture in Finance D. Britton Johnston Chief Technology Evangelist

Data Services forService Oriented Architecture

in Finance

D. Britton Johnston

Chief Technology Evangelist

Copyright © 2005 Progress Software Corporation. All Rights Reserved.2

Agenda

Service Oriented Architecture Ideal for high performance trading systems

SOA requires enterprise data architecture Reliable, consistent and timely data

Trading system case studies demonstrate benefits of well thought-out

and executed data architecture for SOA


Case Example: Sell-Side Bank

DBDB DBDB DBDB DBDB DBDB

DBDB DBDB DBDB DBDB DBDB

Real-Time Trading Applications

ApplicationApplication ApplicationApplication ApplicationApplication ApplicationApplication ApplicationApplication

Enterprise Service Bus

40 global trading applications, $7B trades/day, over 5,000 txns/second

Each application “re-invents” the data access layer:• Reduces developer productivity• Increases maintenance costs• Raises operating risks, system failures, downtime

Data ServicesIntegrated Data Access And Caching


The Optimist’s View of SOA

Messaging Services- SOAP- XML- UDDI/LDAP

Looser coupling of common tasks Reuse at long last through shared services Eliminates tyranny of silos Everything just works™

SOA


Distribution Can Cause Bottlenecks

Shared Data

Apps share data cache, data silos can be out of sync

Each app requires separate data, all data must stay in sync

Data Data Data

DB DB

Check_Avail

Place_Order

Show_Status

Check_Avail

App

Place_Order Show_Status

DB DB DB DB

Business drivers: lower cost, higher flexibilityTechnology enablers: grid computing, web services


SOA Data Consistency Problem

Data Data

DB2

Check_Avail Place_Order

DB1

Item 3 = “out of stock”Item 3 = “in stock”

Nightly Sync

Data silos can cause inconsistent results


The Pessimist's View of SOA

Looser coupling of commonly performed tasks…– But, tighter consistency for commonly used data

Reuse at long last through shared services…– But, lengthier development time for shared services

Eliminates tyranny of silos…– But, lose application boundaries

Everything just works™…– But, Nothing ever works as advertised™


Agenda


SOA requires enterprise data architecture Consistent and timely data

Trading system case studies demonstrate benefits of well thought-out



Requirements For Data Services

DB

AppAppApp

Cache Cache Cache

Data ServicesDistributed Caching

O-R Mapping

Replication

Functional Services

DB DB

Data Caching Services: stage data with app for performance and scalability

Data Replication Services: position data for distributed computing, high availability

Data Mapping Services: native language bindings for optimal performance


When To Worry: The 50/50 Rule

Ob

ject

Mod

el

50+

cla

sses

< 5

0 c

lass

es

Request Rate(Peak transactions/sec)

< 50 TPS 50+ TPS

Data-intensive

applications

Model-intensive

applications

Transaction-intensive

applications

Basic applications

Requires intelligent caching

Requires data services layer

Requires model-driven O/R mapping


Real-Time Data Services “Stack”

DB 1 DB 3DB 2

•Flexibility – bindings

•Performance –caching improves response time

•Scalability –cache replication enables scaling

•Availability–reliable sync improves app resilience

Data IntegrationVirtualized Database

C# AppJava AppC++ App

Compute GridDistributed Execution

Cache Cache Cache

Real-time Data ServicesDistributed Caching


The Iceberg Model For SOA

SOA Strengths• Loose task coupling• Reuse of shared tasks• Eliminate silos

Messaging Services

Data Services

Functional Services

Legacy Environment

SOA Data Gotchas• Data consistency• Data bottlenecks• Data availability


Agenda


SOA requires enterprise data architecture Consistent and timely data

Trading System Case Study demonstrate benefits of well thought-out



Case Study: Sell-Side Bank Business Requirements

Project Requirements– Front & middle office equity trading: >40 global apps– High transaction volumes: >5,000 TPS, millions per day– High availability: max downtime from failure <30 seconds– High scalability: support 5x volume at minimal cost– Reference data usage: all apps share common

reference & order book data = huge potential for bottleneck Deployment Architecture

– Service Oriented Architecture: trading tasks exposed as shared functional services

– Progress Real-time Data Services: Java binding, mapping, replication, caching

– Grid Deployment: Unix Servers (>100 CPUs), Multi-site (US, Europe, Asia), Messaging Middleware, SQL Database


NY

Order Service

Reporting Service

NY

Exchange Service

Case Study: Real-time Data Services Architecture

A-L S-ZM-R

Real-time Data ServicesDistributed Caching, Mapping, Synchronization

Counterparty ServiceSecurities

Service


Service


ServiceOrder Book Service

NJ

Partitioned databases

• O/R Mapping• Caching• Replication

App examples• Trading desk, STP• Auto-exec engine• VWAP Pricing

Relational Databases

Vendor Feeds• Reuters• Bloomberg

Validation Workflow• Extract• Transform• Data cleanse• Change mgmt


Case Study: Benefits Achieved Scalability: grid data services infrastructure scaled to

$7B/day in trades (mainframe maint savings > $4m/yr) Availability: stateful failover between grid data services

caches helped cut failover time from 5 min to 30 sec Productivity: SOA delivered 50% productivity through

service reuse, required up-front resource (~30% of team) Grid Data Services: distributed caching required to “grid

enable” stateful SOA services to run in compute grid


ROI For SOA*

2x Developer productivity: reliable shared services should account for > 50% of new application functionality

3x maintenance productivity: systems deployed using SOA can be maintained with 75% fewer resources

2x “virtual data center” savings: distributed application deployment with centralized data storage (aka virtual data center) can achieve 40% capital cost savings, 30% annual operating cost savings vs traditional data centers

* Source: Progress customer case studies


SOA Data Architecture Roadmap

1. Start with data virtualization: create “golden master” data

2. Add data services: provide consistent language bindings, distributed caching

3. Migrate functionality to SOA: plan to invest 30% of dev resources into shared services

Consolidate SW

infrastructure: eliminate silos,

DBs (2+yrs)

Data Services forService Oriented Architecture

in Finance

D. Britton Johnston

Chief Technology Evangelist

Documents

Data Services for Service Oriented Architecture in Finance D. Britton Johnston Chief Technology Evangelist