Scale-out Scenarios with

Transactional Replication

Eladio Rincón

SolidQ, OLTP Director for Spain&Portugal

SQL Server MVP

eladio@solidq.com

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XXXIII Encontro da Comunidade SQLPort

Data Evento: 23 Abril 2013 - 18:30

Local do Evento: Auditório Microsoft, Parque das Nações, Lisboa

18:30 - Abertura e recepção.

19:10 - "Analyzing Twitter Data" - Niko Neugebauer (SQL Server MVP, Community Evangelist – PASS)

20:15 - Coffee break

20:30 - "First Approach to SQL Server Analysis Services" - João Fialho (Consultor BI Independente)

21:30 - Sorteio de prémios

XXXIV Encontro da Comunidade SQLPort

Data Evento: 7 Maio 2013 - 19:00

Local do Evento: Porto

18:30 - Abertura e recepção.

19:00 - «Apresentação para Developers» - para definir

20:15 - Coffee break

20:30 - «Apresentação para definir» - para definir

21:30 - Sorteio de prémios

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

Agenda

The business Case to Improve

Transactional Replication Concepts

Demo: Seting up Transactional Replication

Demo: Applying Business Logic (Transform)

Demo: Consuming Data (Query)

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

Proposed Architecture

Roles

Diversification

Proposed Architecture

Objects Location

Data in Several Servers

Sync-ing Objects

Data Coordination

Business Rules and

Processing Rules

Might need to process in

several servers

Cons Pros

Scalability

Scale-out

Async Objects

Processing

Non Real Time

Processing

Resources Fine-

Allocation

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

Transactional Replication – Concepts

Transactional Replication – Concepts

Transactional Replication: Demo Scenario

Publisher Subscriptor

SNUCKI9\SQL2012 SNUCKI9\SQL2012DEV

Source DB Destination DB

AdventureWorksLT2012 MyDW

Source Table Destination Table

SalesOrderDetail SalesOrderDetail

SalesOrderHeader SalesOrderHeader

Setup

Transactional

Replication

Applying Business Logic to the

Distributed Data

Replication

Applying

BL to

Distributed Data

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

Consuming

Data

Following these Techniques

Servers 1

Procs 32

Memory 128GB

CPU Consumpt. +80% avg

SAN High

Batches/sec 1400

Activity

OLTP 40%

BI-Low 25%

BI-Medium 30%

Servers 2

Procs 32 (Agg)

Memory 64GB (Agg)

CPU Consumpt. 25% +30%

SAN Low

Batches/sec 2600

Activity

OLTP 30%

BI-Low 30%

BI-Medium 35%

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)

Eladio Rincón

eladio@solidq.com

www.elrincondelDBA.com

www.SolidQ.com