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

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 Real-Time Trading Applications Enterprise Service Bus Application Application Application Application Application Data Services Integrated Data Access And Caching Each application “re-invents” the data access layer: Reduces developer productivity Increases maintenance costs Raises operating risks, system failures, downtime DB 40 global trading applications, $7B trades/day, over 5,000 txns/second

The Optimist’s View of SOA Messaging Services SOA - SOAP XML UDDI/LDAP Looser coupling of common tasks Reuse at long last through shared services Eliminates tyranny of silos Everything just works™

Distribution Can Cause Bottlenecks Business drivers: lower cost, higher flexibility Technology enablers: grid computing, web services App Check_Avail Check_Avail Place_Order Show_Status Place_Order Data Data Data Show_Status Shared Data DB DB DB DB DB DB Apps share data cache, data silos can be out of sync Each app requires separate data, all data must stay in sync

SOA Data Consistency Problem Check_Avail Place_Order Data Data Nightly Sync DB1 DB2 Item 3 = “in stock” Item 3 = “out of stock” 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 Service Oriented Architecture Ideal for high performance trading systems SOA requires enterprise data architecture Consistent and timely data Trading system case studies demonstrate benefits of well thought-out and executed data architecture for SOA

Requirements For Data Services Functional Services Data Caching Services: stage data with app for performance and scalability App App App Cache Cache Cache Data Replication Services: position data for distributed computing, high availability Data Services Distributed Caching Replication O-R Mapping Data Mapping Services: native language bindings for optimal performance DB DB DB

When To Worry: The 50/50 Rule Requires model-driven O/R mapping Requires data services layer Model-intensive applications Data-intensive applications 50+ classes Requires intelligent caching Object Model Transaction-intensive applications Basic applications < 50 classes < 50 TPS 50+ TPS Request Rate (Peak transactions/sec)

Real-Time Data Services “Stack” Compute Grid Distributed Execution Flexibility – bindings Performance –caching improves response time Scalability –cache replication enables scaling Availability–reliable sync improves app resilience C++ App Java App C# App Cache Cache Cache Real-time Data Services Distributed Caching Data Integration Virtualized Database DB 1 DB 2 DB 3

The Iceberg Model For SOA SOA Strengths Loose task coupling Reuse of shared tasks Eliminate silos Messaging Services Functional Services SOA Data Gotchas Data consistency Data bottlenecks Data availability Data Services Legacy Environment

Agenda Service Oriented Architecture Ideal for high performance trading systems SOA requires enterprise data architecture Consistent and timely data Trading System Case Study demonstrate benefits of well thought-out and executed data architecture for SOA

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

Case Study: Real-time Data Services Architecture App examples Trading desk, STP Auto-exec engine VWAP Pricing NY NJ NY Vendor Feeds Reuters Bloomberg Order Service Reporting Service Exchange Service Counterparty Service Counterparty Service Counterparty Service Validation Workflow Extract Transform Data cleanse Change mgmt Securities Service Securities Service Securities Service Order Book Service Real-time Data Services Distributed Caching, Mapping, Synchronization O/R Mapping Caching Replication Partitioned databases A-L M-R S-Z Relational Databases

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

* Source: Progress customer case studies 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 Consolidate SW infrastructure: eliminate silos, DBs (2+yrs) 3. Migrate functionality to SOA: plan to invest 30% of dev resources into shared services 1. Start with data virtualization: create “golden master” data 2. Add data services: provide consistent language bindings, distributed caching

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