Exadata Business

Case Analysis

Ram S. Mohan

Procter & Gamble

AGENDA

Company Background

Exadata Proof-of-Concept (POC)

POC Results - Reason To Believe

Business Requirements

Exadata Approach

Monthly Dashboard

System Landscape

Financial Model

Financial Due Diligence

Risk Mitigation

Opportunities

• Founded in 1837

• HQ in Cincinnati

• $76.7 Billion annual sales

• 135,000 employees

• More than 300 brands sold in

180 countries

• Operations in more than

80 countries

• Three Global Business Units - Beauty and Grooming, Household Care and Health and Well-Being

• Market Development Organization, Global Business Services and Corporate Function.

• 22 Billion Dollar Brands

P&G at a G

lance

William Procter

James Gamble

Exadata Proof of Concept (POC) Overview

• POC was conducted on Exadata V1• Testing Approach was designed over 2 months• >1 billion row DB was used• Designed queries based on business scenarios• Intentionally included queries that we CAN’T reliably run today

(e.g. > 8 hrs to complete)• All queries were run against base detail, no summarization layer

was involved.• No DB design tuning was performed.• Indices were NOT used to get the results.

POC Results – Reason To Believe

• Load Data - approximately 2 hours for 1.25 billion rows(!!!!)

• Run Baseline Queries – results (drum roll):

22X faster was the SLOWEST improvement

• >144X faster (conservatively!) seen for greatest improvement

• True ad hoc queries, that typically take 30 minutes in our environment, returned in 5 seconds.

• One of our pricing reports (complex and high data volume), which takes 6 hours today, ran in 5 minutes.

• We saw queries touching 1.25 billion rows, completing in under 3 minutes.

Business Requirements

• Deliver DW scale that matches the data scale that is over-running us.

• MUCH more head room for capacity challenged applications

• Produce business answers that we cannot today.

• Move from “custom created” Data Warehouse, to commodity delivered, “black box” appliances that are priced accordingly.

• Create an integrated data capability that gives us agility we sorely need.

• Dramatically expand what is an otherwise shrinking batch window.

• Create a Shopper-centric data eco-system (100TB-ish) that becomes the basis for our real-time business sensing.

Exadata Approach

Build, Operate and Govern a single Exadata Retail Trade Platform

• To perform data acquisition for all data coming from the Trade – POS, Market

Measurements, Distributor and others

Migrate applications from other Oracle platforms (RAC and Non-RAC)

Redesign back-end data loading and front-end reporting to take full advantage of infrastructure and capabilities

Drive Operational Excellence

Perform health checks once/quarter

System Management and Alerting – Oracle Enterprise Manager

8 Compute Nodes X86 64 Intel Cores

Storage Cells 14

Number of Disks 168 SAS

Disk Capacity (base 1024) 559GB

Total Storage 93.9 TB

Space for disk failure coverage 3.35 TB

Usable storage after ASM mirroring 43.59TB

System disk group (OCR, Voting disk, DBFS) 2.04 TB

Recovery Disk Group 40% 16.62 TB

Data Disk Group 60% 24.93 TB

Database Overhead (System, SysAux, Redo

log)

2 TB

UNDO Tablespaces 2 TB

TEMP Tablespaces 10% of DB 2.49TB

20% reserve in DGDATA 3.68TB

Space available for user data – DGDATA 80%

full (before compression)

14.71TB

Production Full Rack

Alpharetta, GA

4 Compute Nodes X86 32 Intel Cores

Storage Cells 7

Number of Disks 84 SATA

Disk Capacity (base 1024) 1.86TB

Total Storage 156.5 TB

Space for disk failure coverage 3.353 TB

Usable storage after ASM mirroring 67.1 TB

System disk group (OCR, Voting disk, DBFS) 1.02 TB

Recovery Disk Group 40% 26.4 TB

Data Disk Group 60% 39.6 TB

Database Overhead (System, SysAux, Redo

log)

1 TB

UNDO Tablespaces 1 TB

TEMP Tablespaces 10% of DB 3.96 TB

20% reserve in DGDATA 6.72 TB

Space available for user data – DGDATA 80%

full (before compression)

26.92 TB

Non-Production Half Rack

DEV & UAT Instances

Cincinnati, OH

Financial Model

Current

State

Key Deliverables

In-Scope &

Out-of-Scope

Future State Financial

Analysis

Stand alone

(Non-RAC)

Shared RAC Strategic Exadata

Servers Box CPU Utilization Business value Hosting

Storage Storage Architecture fit Hardware

Operations Support Operations Support Integration Software

Production Production Scalability Support

Non-Production Non-Production Lease vs. Buy Appl. Migration

Technology

Obsolescence

SQL* Server Storage Global vs.

Regional

Infrastructure

Integration

No head room SQL Cubes Co-existence Payout / ROI

Financial Due Diligence

Budget

� Be clear on savings – Hard savings, Cost Avoidance

� Manage Expectations – Best/Average/Worst case financial model – Use Average

Money in / Money out

� Minimize dual infrastructure – Decommission infrastructure ahead of schedule

� Streamline application migration – Fail Cheap!! --- Move, Learn & Reapply

� Store Less – Use compression. Implement ILM strategy

� Deliver on commitments – deliver early. Don’t start on the most complex system migration.

� Communicate value adds in business terms

Risks Mitigation

Issues

1. Minimize negative business impact from new technology deployment– HW, OS and DB

2. Avoid rework – Do it right the first time

3. Technology obsolescence

4. Decommission timing, if delayed, hurts financials

Action Steps

1. End-to-End SINGLE VENDOR support model. Oracle HW/SW integrated with Oracle Managed Services support

2. Leveraged Oracle Expert Services for Exadata implementation

3. Financial model built on 3-year straight line depreciation

4. Use expedited approach� Forklift application #1 (10gR2 schema)

� Migrate application #2 to this schema

Opportunities

• InfiniBand cabling for backup media management servers

• Support for Non-Exadata storage

• Consider Exadata Cloud – POC, New release/patch tests

• Exadata for SAP OLTP and Data Warehouse?

• Greater 100 TB database performance

• High Availability alternatives