Scale out scenarios with transaccional replication

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Transactional Replication exists in SQL Server since 1995; It is a feature that used correctly brings customization and scalability to your data. Considering your solutions from the data-flow …

Transactional Replication exists in SQL Server since 1995; It is a feature that used correctly brings customization and scalability to your data. Considering your solutions from the data-flow perspective, SQL Server Transactional Replication allows you to move data (articles) across servers transparently to your ERP/LOB applications. In this session we will introduce a real customer scenario moving data from OLTP to DW server almost transparently. You will see how and where to make the changes/transformations to your data to addecuate to your business rules. To close the cicle you will see how to consume the data from SSAS, and how to customize it to near-real time sincronization.

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  • 1. Scale-out Scenarios withTransactional ReplicationEladio RincónSolidQ, OLTP Director for Spain&PortugalSQL Server
  • 2. The Sponsors
  • 3. The Volunteers They spend their FREE time to give you this event. (2 months per person) Because they are crazy. Because they want YOU to learn from the BEST IN THE WORLD. If you see a guy with “STAFF” on their back – buy them a beer, they deserve it.
  • 4. Paulo Matos:
  • 5. Paulo Borges:
  • 6. João Fialho:
  • 7. Bruno Basto:
  • 8. Upcoming SQL Server events:XXXIII Encontro da Comunidade SQLPortData Evento: 23 Abril 2013 - 18:30Local do Evento: Auditório Microsoft, Parque das Nações, Lisboa18:30 - Abertura e recepção.19:10 - "Analyzing Twitter Data" - Niko Neugebauer (SQL Server MVP, Community Evangelist –PASS)20:15 - Coffee break20:30 - "First Approach to SQL Server Analysis Services" - João Fialho (Consultor BI Independente)21:30 - Sorteio de prémiosXXXIV Encontro da Comunidade SQLPortData Evento: 7 Maio 2013 - 19:00Local do Evento: Porto18:30 - Abertura e recepção.19:00 - «Apresentação para Developers» - para definir20:15 - Coffee break20:30 - «Apresentação para definir» - para definir21:30 - Sorteio de prémios
  • 9. Eladio Rincón OLTP Director @ SolidQ Spain & Portugal SQL Server MVP since 2003 Manages with other MVPs PASS Spanish Chapter What I do?  Designing HA and DR solutions  Troubleshooting and Optimization  Complex Upgrade and Migration projects  Datawarehousing on PDW and Fast Track DW
  • 10. Agenda The business Case to Improve Transactional Replication Concepts Demo: Seting up Transactional Replication Demo: Applying Business Logic (Transform) Demo: Consuming Data (Query)
  • 11. Business Case to Improve  Processing Type  Online (OLTP)  Analytical (BI / DW)  Batches (mix OLTP y BI)  Resources needed  IOPS – IO Subsystem  Volume – IO Subsystem  Processing – CPU  Concurrency – Apps
  • 12. Proposed Architecture Roles Diversification
  • 13. Proposed Architecture Cons Pros Objects Location  Scalability  Data in Several Servers  Scale-out Sync-ing Objects  Async Objects  Data Coordination Processing Business Rules and  Non Real Time Processing Rules Processing  Might need to process in  Resources Fine- several servers Allocation
  • 14. Proposed Architecture: Technology Transactional Replication  Allocate the Data in Different Servers/Sites to:  Async Processing  Ad-hoc Reporting  Data Aggregation SQL Server Analysis Services  Data Aggretation Strenghts  Client Tools for Querying (Excel  Self-Service BI)  In Multi-Dimensional  Proactive Caching
  • 15. Transactional Replication – Concepts
  • 16. Transactional Replication – Concepts
  • 17. Transactional Replication: Demo Scenario Publisher Subscriptor SNUCKI9SQL2012 SNUCKI9SQL2012DEV Source DB Destination DB AdventureWorksLT2012 MyDW Source Table Destination Table SalesOrderDetail SalesOrderDetail SalesOrderHeader SalesOrderHeader
  • 18. SetupTransactionalReplication
  • 19. Applying Business Logic to theDistributed Data Replication
  • 20. ApplyingBL toDistributed Data
  • 21. Consuming Data Multi-Dimensional or Tabular Models  Pre-calculated data  Less resources usage (CPU, IO)  Periodical refresh: what business says  SQL Server Agent jobs  Proactive Caching (notifications) Data Consumption  Excel or Reporting Services  Flexible vs less-flexible
  • 22. ConsumingData
  • 23. Following these TechniquesServers 1 Servers 2Procs 32 Procs 32 (Agg)Memory 128GB Memory 64GB (Agg)CPU Consumpt. +80% avg CPU Consumpt. 25% +30%SAN High SAN LowBatches/sec 1400 Batches/sec 2600Activity ActivityOLTP 40% OLTP 30%BI-Low 25% BI-Low 30%BI-Medium 30% BI-Medium 35%
  • 24. Final Thoughts Combine existing Technologies  Partitioning, Replication, AlwaysOn, Log Shipping Improving the Infra (hardware) really helps (ROI)  Memory at very atractive prices  CPU and IO nice price Escalability vs Architecture  Design your solution (Software) with Escalability in mind  Adjust the Technology to your Solution needs (Software)
  • 25. Eladio Rincó