Data Services for Service Oriented Architecture

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Data Services for Service Oriented Architecture

  1. 1. Data Services for Service Oriented Architecture in Finance D. Britton Johnston Chief Technology Evangelist
  2. 2. Agenda <ul><li>Service Oriented Architecture </li></ul><ul><ul><ul><li>Ideal for high performance trading systems </li></ul></ul></ul><ul><li>SOA requires enterprise data architecture </li></ul><ul><ul><ul><li>Reliable, consistent and timely data </li></ul></ul></ul><ul><li>Trading system case studies </li></ul><ul><ul><ul><li>demonstrate benefits of well thought-out and executed data architecture for SOA </li></ul></ul></ul>
  3. 3. Case Example: Sell-Side Bank Real-Time Trading Applications 40 global trading applications, $7B trades/day, over 5,000 txns/second DB DB DB DB DB DB DB DB DB DB Application Application Application Application Application Enterprise Service Bus <ul><li>Each application “re-invents” the data access layer: </li></ul><ul><li>Reduces developer productivity </li></ul><ul><li>Increases maintenance costs </li></ul><ul><li>Raises operating risks, system failures, downtime </li></ul>Data Services Integrated Data Access And Caching
  4. 4. The Optimist’s View of SOA <ul><li>Looser coupling of common tasks </li></ul><ul><li>Reuse at long last through shared services </li></ul><ul><li>Eliminates tyranny of silos </li></ul><ul><li>Everything just works™ </li></ul>Messaging Services <ul><li>- SOAP </li></ul><ul><li>XML </li></ul><ul><li>UDDI/LDAP </li></ul>SOA
  5. 5. 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 flexibility Technology enablers: grid computing, web services
  6. 6. 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
  7. 7. The Pessimist's View of SOA <ul><li>Looser coupling of commonly performed tasks… </li></ul><ul><ul><li>But, tighter consistency for commonly used data </li></ul></ul><ul><li>Reuse at long last through shared services… </li></ul><ul><ul><li>But, lengthier development time for shared services </li></ul></ul><ul><li>Eliminates tyranny of silos… </li></ul><ul><ul><li>But, lose application boundaries </li></ul></ul><ul><li>Everything just works™… </li></ul><ul><ul><li>But, Nothing ever works as advertised™ </li></ul></ul>
  8. 8. Agenda <ul><li>Service Oriented Architecture </li></ul><ul><ul><ul><li>Ideal for high performance trading systems </li></ul></ul></ul><ul><li>SOA requires enterprise data architecture </li></ul><ul><ul><ul><li>Consistent and timely data </li></ul></ul></ul><ul><li>Trading system case studies </li></ul><ul><ul><ul><li>demonstrate benefits of well thought-out and executed data architecture for SOA </li></ul></ul></ul>
  9. 9. Requirements For Data Services DB App App App Cache Cache Cache Data Services Distributed 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
  10. 10. When To Worry: The 50/50 Rule Object Model 50+ classes < 50 classes 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
  11. 11. Real-Time Data Services “Stack” DB 1 DB 3 DB 2 <ul><li>Flexibility – bindings </li></ul><ul><li>Performance –caching improves response time </li></ul><ul><li>Scalability –cache replication enables scaling </li></ul><ul><li>Availability –reliable sync improves app resilience </li></ul>Data Integration Virtualized Database C# App Java App C++ App Compute Grid Distributed Execution Cache Cache Cache Real-time Data Services Distributed Caching
  12. 12. The Iceberg Model For SOA <ul><li>SOA Strengths </li></ul><ul><li>Loose task coupling </li></ul><ul><li>Reuse of shared tasks </li></ul><ul><li>Eliminate silos </li></ul>Messaging Services Data Services Functional Services Legacy Environment <ul><li>SOA Data Gotchas </li></ul><ul><li>Data consistency </li></ul><ul><li>Data bottlenecks </li></ul><ul><li>Data availability </li></ul>
  13. 13. Agenda <ul><li>Service Oriented Architecture </li></ul><ul><ul><ul><li>Ideal for high performance trading systems </li></ul></ul></ul><ul><li>SOA requires enterprise data architecture </li></ul><ul><ul><ul><li>Consistent and timely data </li></ul></ul></ul><ul><li>Trading System Case Study </li></ul><ul><ul><ul><li>demonstrate benefits of well thought-out and executed data architecture for SOA </li></ul></ul></ul>
  14. 14. Case Study: Sell-Side Bank Business Requirements <ul><li>Project Requirements </li></ul><ul><ul><li>Front & middle office equity trading: >40 global apps </li></ul></ul><ul><ul><li>High transaction volumes : >5,000 TPS, millions per day </li></ul></ul><ul><ul><li>High availability : max downtime from failure <30 seconds </li></ul></ul><ul><ul><li>High scalability : support 5x volume at minimal cost </li></ul></ul><ul><ul><li>Reference data usage : all apps share common reference & order book data = huge potential for bottleneck </li></ul></ul><ul><li>Deployment Architecture </li></ul><ul><ul><li>Service Oriented Architecture: trading tasks exposed as shared functional services </li></ul></ul><ul><ul><li>Progress Real-time Data Services: Java binding, mapping, replication, caching </li></ul></ul><ul><ul><li>Grid Deployment : Unix Servers (>100 CPUs), Multi-site (US, Europe, Asia), Messaging Middleware, SQL Database </li></ul></ul>
  15. 15. Case Study: Real-time Data Services Architecture NY Order Service Reporting Service NY Exchange Service A-L S-Z M-R Real-time Data Services Distributed Caching, Mapping, Synchronization Counterparty Service Securities Service Counterparty Service Securities Service Counterparty Service Securities Service Order Book Service NJ Partitioned databases <ul><li>O/R Mapping </li></ul><ul><li>Caching </li></ul><ul><li>Replication </li></ul><ul><li>App examples </li></ul><ul><li>Trading desk, STP </li></ul><ul><li>Auto-exec engine </li></ul><ul><li>VWAP Pricing </li></ul>Relational Databases <ul><li>Vendor Feeds </li></ul><ul><li>Reuters </li></ul><ul><li>Bloomberg </li></ul><ul><li>Validation </li></ul><ul><li>Workflow </li></ul><ul><li>Extract </li></ul><ul><li>Transform </li></ul><ul><li>Data cleanse </li></ul><ul><li>Change mgmt </li></ul>
  16. 16. Case Study: Benefits Achieved <ul><li>Scalability : grid data services infrastructure scaled to $7B/day in trades (mainframe maint savings > $4m/yr) </li></ul><ul><li>Availability : stateful failover between grid data services caches helped cut failover time from 5 min to 30 sec </li></ul><ul><li>Productivity : SOA delivered 50% productivity through service reuse, required up-front resource (~30% of team) </li></ul><ul><li>Grid Data Services : distributed caching required to “grid enable” stateful SOA services to run in compute grid </li></ul>
  17. 17. ROI For SOA* <ul><li>2x Developer productivity : reliable shared services should account for > 50% of new application functionality </li></ul><ul><li>3x maintenance productivity : systems deployed using SOA can be maintained with 75% fewer resources </li></ul><ul><li>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 </li></ul>* Source: Progress customer case studies
  18. 18. 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)
  19. 19. Data Services for Service Oriented Architecture in Finance D. Britton Johnston Chief Technology Evangelist
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