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Sun Oracle Exadata and Database Machine Overview

Copyright 2009, Oracle Corporation and/or its affiliates

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Enterprise Technology Center

The Architecture of the FutureMassively Parallel Grid

Best for Data WarehousingBest for OLTPBest for Consolidation

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Database Machine Success

Database Machine is succeeding in all geographies andindustries against every competitor

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The Oracle Database Machine is an ideal cost-effectiveplatform to meet our speed and scalability needs .

Ketan Parekh, Manager Database Systems

After carefully testing several data warehouse platforms, wechose the Oracle Database Machine. Oracle Exadata was able tospeed up one of our critical processes from days to minutes ..

Brian Camp, Sr. VP of Infrastructure Services

A query that used to take 24 hours to complete now runs inless than 30 minutes. The Oracle Database Machine beatscompeting solutions on bandwidth, load rate, disk capacity,and transparency.

Christian Maar, CIO

Database Machine Success

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Agenda

Overview Exadata Based Product Offerings Exadata Architecture and Features

Best Data Warehousing Machine Best OLTP Machine Best Consolidation Machine

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The Products Exadata Storage Server & Database Machine

Exadata Storage Server Storage Product Optimized for Oracle

Database Extreme I/O and SQL Processing

performance Combination of hardware and

software Exadata Storage Server Software

Sun Oracle Database Machine Pre-Configured High Performance Balanced performance

configuration Takes the guess work out of

building an Oracle deployment Exadata Storage Server Software Oracle Database 11.2

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Business Value of Exadata

Extreme Performance : Dramatic performanceimprovements (10-100X) for your data warehouses.Dramatic performance improvements (20X) for OLTPapplications

Linear Scalability : Performance scales linearly with

increase in data volumes. Enterprise Ready : Get up and running quickly with a

complete system containing all the required hardware andsoftware. Single enterprise level support for all hardwareand software from Oracle.

Standard: Works transparently with existing applications.Manage your databases and applications the same wayyou do today.

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Storage Bottlenecks

Today, database performance is limited by storage Storage systems limit data bandwidth from storage to servers Storage Array internal bottlenecks SAN bottlenecks

Random I/O bottlenecks due to physical disk speeds Data Bandwidth limits severely restrict performance for data

warehousing Random I/O bottlenecks limit performance of OLTP applications

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Exadata Smart StorageBreaks Data Bandwidth and Random I/O Bottleneck

Oracle addresses data bandwidthbottleneck 3 ways

Massively parallel storage grid of high performanceExadata storage servers (cells).

Data bandwidth scales with data volume

Data intensive processing runs in Exadata storage. Queries run in storage as data streams from

disk, offloading database server CPUs Columnar compression reduces data volume up to

10x Exadata Hybrid Columnar Compression

provides 10x lower cost, 10x higher performance

Oracle solves random I/O bottlenecks usingExadata Smart Flash Cache

Increase random I/Os by factor of 20X

Exadata Storage Cells

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Exadata Based Product Offerings

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Sun Oracle Database Machine Grid is the architecture of the future

Highest performance, lowest cost, redundant, incrementally scalable Sun Oracle Database Machine delivers the first and only complete

grid architecture for all data management needs

Exadata Storage Server Grid 14 High-performance low-cost

storage servers 100 TB raw SAS disk storage

or 336 TB raw SATA disk storage

5TB+ flash storage!

RAC Database Server Grid 8 High-performance low-cost

compute servers 2 Intel quad-core Xeons each

InfiniBand Network 40 Gb/sec fault-tolerant unified

server and storage network

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Sun Oracle Database Machine

Exadata Storage Server Grid 21 GB/sec disk bandwidth 50 GB/sec flash bandwidth 1 million I/Os per second

RAC Database Server Grid Millions of transactions

per minute Tens of millions of queries

per minute Billions of rows per minute

InfiniBand Network 880 Gb/sec aggregate

throughput

Extreme Performance

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Start Small and Grow

FullRack

Half Rack

Quarter Rack

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Scale Performance and Capacity

Scalable Scales to 8 rack database machine

by just adding wires More with external

InfiniBand switches Scales to hundreds of storage servers

Multi-petabyte databases

Redundant and FaultTolerant

Failure of any componentis tolerated Data is mirrored across

storage servers

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Drastically Simplified Deployments

Database Machine eliminates thecomplexity of deploying databasesystems

Months of configuration, troubleshooting, tuning

Database Machine is ready on dayone

Pre-built, tested, standard, supportable configuration Runs existing applications unchanged

Extreme performance out of the boxMonths toDays

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Sun Exadata Storage Server Hardware

Building block of massively parallel Exadata Storage Grid Up to 1.5 GB/sec raw data bandwidth per cell Up to 75,000 IOPS with Flash

Sun Fire X4275 Server 2 Quad-Core Intel Xeon E5540 Processors 24GB RAM Dual-port 4X QDR (40Gb/s) InfiniBand card Disk Options

12 x 600 GB SAS disks (7.2 TB total) 12 x 2TB SATA disks (24 TB total)

4 x 96 GB Sun Flash PCIe Cards (384 GB total) Software pre-installed

Oracle Exadata Storage Server Software Oracle Enterprise Linux Drivers, Utilities

Single Point of Support from Oracle 3 year, 24 x 7, 4 Hr On-site response

Sun Exadata Storage

Server Hardware

Hardware by

Software by

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Sun Exadata Storage Server Hardware

24 GB DRAM

12 x 3.5 Disk Drives

2 Quad-Core IntelXeon Processors

Pre-installed Software:

Oracle Exadata Storage Server Software

Oracle Enterprise Linux

Drivers

Disk Controller

HBA with 512Mbattery backedcache

InfiniBand QDR(40Gb/s) dualport card

ILOM

Dual-redundant, hot-swappable power supplies

4 x 96GB SunFlash PCIeCards

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Sun Oracle Database Machine Full RackPre-Configured for Extreme Performance

8 Sun Fire X4170 Oracle Database servers 14 Exadata Storage Servers (All SAS or all

SATA) 3 Sun Datacenter InfiniBand Switch 36

36-port Managed QDR (40Gb/s) switch

1 Admin Cisco Ethernet switch Keyboard, Video, Mouse (KVM) hardware Redundant Power Distributions Units (PDUs) Single Point of Support from Oracle

3 year, 24 x 7, 4 Hr On-site response

Add more racks for additional scalability

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36-port Managed QDR (40Gb/s) switch

1 Admin Cisco Ethernet switch Keyboard, Video, Mouse (KVM) hardware Redundant PDUs Single Point of Support from Oracle


Can Upgrade to a Full Rack

Sun Oracle Database Machine Half RackPre-Configured for Extreme Performance

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36-port Managed QDR (40Gb/s)

InfiniBand switch 1 Admin Cisco Ethernet switch Keyboard, Video, Mouse (KVM) hardware Redundant PDUs Single Point of Support from Oracle


Can Upgrade to an Half Rack

Sun Oracle Database Machine Quarter RackPre-Configured for Extreme Performance

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Sun Oracle Database Machine Basic SystemEntry Level non-HA Configuration

1 Sun Fire X4170 Oracle Database servers 1 Exadata Storage Servers (All SAS or allSATA)

1 Sun Datacenter InfiniBand Switch 36 36-port Managed QDR (40Gb/s)

InfiniBand switch InfiniBand Cables Installed in Customer supplied Rack Customer supplied Ethernet and KVM

Infrastructure Single Point of Support from Oracle


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Standalone Exadata Storage Servers

Purchase Exadata Storage Servers from Oracle Customer supplied standard 19 inch rack Customer supplied x86 64-bit Linux Database

Servers

Hardware installation more complex No single point of support for entire deployment

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Exadata Product CapacitySingle Server Quarter Rack Half Rack Full Rack

Raw Disk 1 SAS 7.2 TB 21 TB 50 TB 100 TB

SATA 24 TB 72 TB 168 TB 336 TB

Raw Flash 1 384 GB 1.1 TB 2.6 TB 5.3 TB

User Data 2(assuming nocompression)

SAS 2 TB 6 TB 14 TB 28 TBSATA 7 TB 21 TB 50 TB 100 TB

1 Raw capacity calculated using 1 GB = 1000 x 1000 x 1000 bytes and 1 TB = 1000 x 1000 x 1000 x 1000 bytes.

2 - User Data: Actual space for end-user data, computed after single mirroring (ASM normal redundancy) and after allowing space for database structures such as temp, logs, undo, and indexes. Actual user data capacity varies byapplication. User Data capacity calculated using 1 TB = 1024 * 1024 * 1024 * 1024 bytes.

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Exadata Product PerformanceSingleServer

Quarter Rack

Half Rack Full Rack

Raw Disk DataBandwidth 1,4

SAS 1.5 GB/s 4.5 GB/s 10.5 GB/s 21 GB/sSATA 0.85 GB/s 2.5 GB/s 6 GB/s 12 GB/s

Raw Flash Data Bandwidth 1,4 3.6 GB/s 11 GB/s 25 GB/s 50 GB/s

Max User Data Bandwidth 2,4(10x compression & Flash)

36 GB/s 110 GB/s 250 GB/s 500 GB/s

Disk IOPS 3,4 SAS 3,600 10,800 25,000 50,000

SATA 1,440 4,300 10,000 20,000

Flash IOPS 3,4 75,000 225,000 500,000 1,000,000

Data Load Rate 4 0.65 TB/hr 1 TB/hr 2.5 TB/hr 5 TB/hr

1 Bandwidth is peak physical disk scan bandwidth, assuming no compression.

2 - Max User Data Bandwidth assumes scanned data is compressed by factor of 10 and is on Flash.

3 IOPs Based on IO requests of size 8K

4 - Actual performance will vary by application.

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Exadata Architecture and Features

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Exadata Configuration

Each Exadata Cell is a self-contained server which houses diskstorage and runs the Exadata software

Oracle Databases are deployed across multiple Exadata Cells Oracle Database enhanced to work in cooperation with Exadata

Storage Server No practical limit to number of Cells that can be in the grid

Exadata Cell

InfiniBand Switch/Network

Single-InstanceDatabase

RACDatabase

Exadata Cell Exadata Cell

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Exadata Architecture

DB Server

DB Instance DBRM

ASM

Single-Instance

Database

RAC

DatabaseDB Server

DB Instance DBRM

ASM

DB Server

DB Instance DBRM

ASM

OEL CELLSRV MS

RS

IORM

iDB Protocol overInfiniBand with

Path FailoverInfiniBand Switch/Network

CellControl

CLI

EnterpriseManager

OEL CELLSRV MS

RS

IORM

OEL CELLSRV MS

RS

IORM

Exadata Cell

Exadata Cell

Exadata Cell

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Exadata Software Features Exadata Smart Scans

10X or greater reduction in data sent to database servers Exadata Storage Indexes

Eliminate unnecessary I/Os to disk Hybrid Columnar Compression (HCC)

Efficient compression increases effective storage capacity and increasesuser data scan bandwidths by a factor of 10X

Exadata Smart Flash Cache Breaks random I/O bottleneck by increasing IOPs by 20X Doubles user data scan bandwidths

I/O Resource Manager (IORM) Enables storage grid by prioritizing I/Os to ensure predictable performance

Inter-leaved Grid Disks Enables storage grid that allows multiple applications to place frequently

accessed data on faster portions of the disk

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Exadata Smart Scan

Exadata cells implement scan offload to greatlyreduce the data sent to database servers

Row filtering based on where predicate Column filtering Join filtering

Incremental backup filtering Scans on encrypted data Data Mining model scoring

10x data reduction is common

Completely application transparent Even if cell or disk fails during a query

11.2

11.2

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Traditional Scan Processing Smart Scan Example:

Telco wants to identifycustomers that spend

more than $200 on asingle phone call The information about

these premium customersoccupies 2MB in a 1terabyte table

With traditional storage, alldatabase intelligence residesin the database hosts

Very large percentage of datareturned from storage isdiscarded by database

servers Discarded data consumesvaluable resources, andimpacts the performance of other workloads

I/Os Executed:

1 terabyte of datareturned to hosts

DB Host reduces

terabyte of data to 1000customer names thatare returned to client

Rows Returned

SELECT

customer_nameFROM calls

WHERE amount > 200;

Table

ExtentsIdentified

I/Os Issued

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Exadata Smart Scan Processing

Only the relevant columns

customer_nameand required rows where amount>200are are returned to hosts

CPU consumed by predicate

evaluation is offloaded toExadata

Moving scan processing off thedatabase host frees host CPUcycles and eliminates massive

amounts of unproductivemessaging Returns the needle, not the

entire hay stack

2MB of datareturned to server

Rows Returned

Smart Scan

Constructed AndSent To Cells

Smart Scanidentifies rows and

columns withinterabyte table that

match request

Consolidated

Result SetBuilt From All

Cells

SELECT

customer_nameFROM calls

WHERE amount > 200;

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Smart Scan Transparency Smart scans are transparent to the application

No application or SQL changes required Returned data is fully consistent and transactional If a cell dies during a smart scan, the uncompleted portions

of the smart scan are transparently routed to other cellscontaining a replica of the data

Smart Scans correctly handle complex cases including Uncommitted data and locked rows Chained rows Compressed tables National Language Processing

Date arithmetic Regular expression searches Partitioned tables

High Throughput, Reduced Overhead, No Complex Tuning

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Exadata Smart Scans:Offloaded data mining scoring

Data mining scoring executed in Exadata:

All data mining scoring functions offloaded to Exadata Up to 10x performance gains

Reduced CPU utilization on Database Server

select cust_idfrom customerswhere region = US and prediction_probability(churnmod, Y using *) > 0.8;

Scoring functionexecuted in

Exadata

11.2

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Exadata Storage IndexTransparent I/O Elimination with No Overhead

Exadata Storage Indexes maintain summaryinformation about table data in memory

Store MIN and MAX values of columns Typically one index entry for every MB of disk

Eliminates disk I/Os if MIN and MAX can never match where clause of a query

Completely automatic and transparent

A B C D

1

35

5

8

3

Min B = 1Max B =5

Table Index

Min B = 3Max B =8

Select * from Table where B

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Data Growth Challenges

IT must support exponentiallygrowing amounts of data Without hurting performance

Without growing cost

Powerful and efficientcompression is key

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E d H b id C l C i

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Exadata Hybrid Columnar CompressionHow it works

Tables are organized into sets of a few thousandrows called Compression Units (CUs)

Within Compression Unit, data is Organized byColumn and then compressed

Column organization brings similar values closetogether, enhancing compression

Useful for data that is bulk loaded and queried Update activity is light

ReducesTable Size4x to 40x

4x to 50xReduction

CompressionUnit

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Exadata Hybrid Columnar CompressionWarehousing and Archiving

Warehouse Compression

10x average storage savings 10x Scan I/O reduction

Archive Compression

15x average storagesavings

Up to 50x on some data Some access overhead For cold or historical data

Optimized for Speed Optimized for Space

Smaller WarehouseFaster Performance Reclaim 93% of DisksKeep Data Online

Can mix compression types by partition for ILM

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10 10 10 1116 19 19 19

20 2129

43

05

101520253035404550

S i z e

R e d

u c

t i o n

F a c

t o r

b y

T a

b l e

OLTP Compression (avg=3.3)

Query Compression (avg=14.6)

Archive Compression (avg=22.6)

Real-World Compression RatiosOracle Production E-Business Suite Tables

Columnar compression ratios Query = 14.6X Archive = 22.6X Vary by application and table

52

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The Disk Random I/O Bottleneck

Disk drives hold vast amounts of data

But are limited to about 300 I/Os per second

Flash technology holds much lessdata

But can run tens of thousands of I/Osper second

Ideal Solution Keep most data on disk for low cost Transparently move hot data to flash Use flash cards instead of flash disks to avoid disk

controller limitations Flash cards in Exadata storage

High bandwidth, low latency interconnect

300 I/O per Sec

Tens of Thousands of I/Os per Second

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Exadata Smart Flash Cache

Caches Hot Data Transparently inthe 4 Flash Cards

Use PCI Express based FlashCards for greater throughput andIOPs and avoid disk controller limitations

Smart Caching Smarter than basic LRU algorithm

Knows when to skip caching objects toavoid polluting or flushing the cache

Allows applications to explicitlyoptimize caching

11.2

4 x 96 GB Flash Cards

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Exadata I/O Resource ManagementMixed Workload Environments

With traditional storage,creating and

managing shared storage ishampered by the inability to balancethe work between users on the samedatabase or on multiple databasessharing the storage subsystem

Hardware isolation is the

approach to ensure separation Exadata I/O resource managementensures different users and taskswithin a database are allocated thecorrect relative amount of I/Oresources

For example: Interactive: 50% of I/O resources Reporting: 30% of I/O resources ETL: 20% of I/O resources

DatabaseServer

Exadata Cell



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Exadata I/O Resource ManagementMulti-Database Environment

Ensure different databases areallocated the correct relative amount of I/O bandwidth

Database A: 33% I/O resources Database B: 67% I/O resources

Ensure different users and tasks withina database are allocated the correctrelative amount of I/O bandwidth

Database A: Reporting: 60% of I/O resources ETL: 40% of I/O resources

Database B: Interactive: 30% of I/O resources Batch: 70% of I/O resources

Exadata Cell


Database A Database B


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Exadata Scale-Out Storage Grid

Dynamic virtualized storageresources using Automatic StorageManagement (ASM)

Simple and non-intrusive resourceallocation, and reallocation, enablingtrue enterprise grid storage

Database work spread acrossstorage resources for optimalperformance

Powerful storage allocation optionsand management

Flexible configuration for performance and availability

Exadata Cell


Single-InstanceDatabase

RACDatabase


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Exadata Storage Layout

Cell

Disk

Sys Area Sys AreaGrid Disk n

Grid Disk 1

ASM disk

ASM disk

Physical disks map to Cell Disks

Cell Disks partitioned into one or multiple Grid Disks Grid Disks created in order of hottest first to coldest portion of the

disk last ASM diskgroups created from Grid Disks Transparent above the ASM layer

Physical

Disk

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Exadata Storage Layout ExampleASM Mirroring and Failure Groups

Example shows cell disks divided into two grid disks hot and cold

Two ASM disk groups created across the two sets of grid disks ASM striping evenly distributes I/O across the disk groups ASM mirroring is used to protect against disk failures ASM failure groups are used to protect against cell failures


Hot Hot Hot

Cold Cold Cold

ASMDisk Group

ASMFailure Group

Hot Hot Hot

Cold Cold Cold

ASMFailure Grou

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Interleaved Grid Disks

Grid disks are optionally split andinterleaved to place frequently accesseddata in all grid disks on higher performingouter tracks

All applications benefit from higher performance outer tracks of disks

Grid Disk 2Hot Data, Cold Data

Grid Disk 1 Hot Data, Cold Data

11.2

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Exadata Storage Management & Administration

Enterprise Manager Manage & administer Database and ASM

Exadata Storage Plug-in Enterprise Manager Grid Control Plug-in to monitor & manage

Exadata Storage Cells

Comprehensive CLI Local Exadata Storage cell management Distributed shell utility to execute CLI across multiple cells

Sun Embedded Integrated Lights Out Manager (ILOM) Remote management and administration of hardware

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Exadata Storage Plug-in

Enterprise Manager Grid Control Plug-in to monitor &

manage Exadata Storage Cells Works with Enterprise Manager Grid Control 10.2.0.3and later versions

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Data Protection Solutions

All single points of failure eliminated by the Exadata Storage architecture

Hardware Assisted Resilient Data (HARD) built in to Exadata Storage Prevent data corruption before it happens

Data Guard provides disaster protection and data corruption protection Automatically maintains one or more copies of the database

Flashback provides human error protection Snapshot-like capabilities to rewind database to before error

Recovery Manager (RMAN) provides backup to disk Archiving and corruption protection Can be used with Oracle Secure Backup (OSB) or third party tape backup

software These work just as they do for traditional non-Exadata storage

Users and database administrator use familiar tools

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Exadata Co-Existence and Migration

Databases can be concurrentlydeployed on Exadata andtraditional storage Tablespaces can exist on Exadata

storage, traditional storage, or acombination of the two, and is

transparent to database applications SQL offload processing requires allpieces of a tablespace reside onExadata

Online migration if currently using

ASM and ASM redundancy Migration can be done usingRMAN or Data Guard

Non-Exadata

DatabaseServer

Online Migration

Exadata

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Best Data Warehouse Machine

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Best Data Warehouse Machine

Massively parallel high volume hardware toquickly process vast amounts of data

Exadata runs data intensive processingdirectly in storage

Most complete analytic capabilities OLAP, Statistics, Spatial, Data Mining, Real-timetransactional ETL, Efficient point queries

Powerful warehouse specific optimizations Flexible Partitioning, Bitmap Indexing, Join indexing, Materialized Views, Result

Cache

Dramatic new warehousing capabilitiesData Mining

OLAP

ETL

New

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Exadata Storage Features Exadata Smart Scans

10X or greater reduction in data sent todatabase servers

Exadata Storage Indexes Eliminate unnecessary I/Os to disk

Hybrid Columnar Compression Efficient compression increases user data scan

rates

Flash doubles scan rates Combined with Hybrid Columnar Compression,

scan rate is 20X more

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In-Memory Parallel Execution

A single database machine has over 400GB of memory usable for caching

Database release 11.2 introduces parallelquery processing on memory cached data

Harnesses memory capacity of entire database cluster for queries

Foundation for world record 1TB TPC-H

Exadata Hybrid Columnar Compressionenables multi-terabyte tables or partitionsto be cached in memory

315,842

1,018,321

1,166,976

ParAccel Exasol Oracle

QphH: 1 TB TPC-H

Faster than in-memoryspecialized startups

Memory has 100x more bandwidth than Disk

Source: Transaction Processing Council, as of 9/14/2009:Oracle on HP Bladesystem c-Class 128P RAC, 1,166,976 QphH@1000GB, $5.42/QphH@1000GB, available 12/1/09.Exasol on PRIMERGY RX300 S4, 1,018,321 QphH@1000GB, $1.18/QphH@1000GB, available 08/01/08.ParAccel on SunFire X4100 315 842 hH 1000GB $4.57 / hH 1000GB available 10/29/07.

11.2

f l l
http://www.tpc.org/tpch/results/tpch_result_detail.asp?id=107102903http://www.tpc.org/tpch/results/tpch_result_detail.asp?id=107102903

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Benefits Multiply

1 TBwith compression

10 TB of user dataRequires 10 TB of IO

100 GBwith partition pruning

20 GBwith Storage Indexes

5 GBwith Smart Scans

Subsecond On Database

Machine

Data is 10x Smaller, Scans are 2000x faster

S S l bl Sh d il S

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DBFS - Scalable Shared File System

Database Machine comes with DBFS shared Linux file system Shared storage for ETL staging, scripts, reports and other application files

Files stored as SecureFile LOBs in database tables stored inExadata

Protected like any DB data mirroring, DataGuard, Flashback, etc.

5 to 7 GB/sec file system I/O throughput

ETL Files in DBFS

Load into databaseusing External Tables

ETL

More File Throughput than High-End NAS Filer

11.2

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Best OLTP Machine

B OLTP M hi

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Best OLTP Machine

Only Oracle runs real-world businessapplications on the Grid

Unique fault-tolerant scale-out OLTPdatabase

RAC, Data Guard, Online Operations

Unique fault-tolerant scale-out storagesuitable for OLTP

ASM, Exadata

Dramatic New OLTP Capabilities

Exadata Flash

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Exadata FlashSolves the Random I/O Bottleneck

Oracle is the First FlashOptimized Database

11.2

Sun Oracle Database Machine has

5+ TB of flash storage Exadata Smart Cache caches hotdata

Database Machine achieves: 20x more random I/Os

Over 1 million per second 2x faster sequential query I/O

50 GB/sec 10x better I/O response time

Sub-millisecond Greatly Reduced Cost 10x fewer disks for IOPS Lower Power

C l t O I t t d A il bilit

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WAN

Complete, Open, Integrated AvailabilityMaximum Availability Architecture

Protection from Server Failures Storage Failures Network Failures Site Failures

Real-time remote standby open for queries

Human error correction Database, table, row, transaction level

Online indexing and table redefinition Online patching and upgrades

RealApplication

Clusters

ASM

FastRecovery Area

ActiveData Guard

SecureBackup

C l t O I t t d S it

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Complete, Open, Integrated Security

DataMasking

AdvancedSecurity SecureBackup

Encryption and Masking

DatabaseVault

LabelSecurity

Access Control

ConfigurationManagement

AuditVault TotalRecall

Monitoring

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Best Consolidation Machine

C lid ti

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Consolidation

Biggest driver of ongoingcost is

Multitudes of special-purpose systems

Consolidate onto DatabaseMachine

High performance for all applications Low cost platform for all applications Predictable response times in a shared

environment Handles all data management needs Complete, Open, Integrated

ERP

CRM

Warehouse

Data MartHR

CRMERP

Warehouse Data Mart

HR

B t C lid ti M hi

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Best Consolidation Machine

Consolidation mixes many different

workloads in one system Warehouse oriented bulk data processing OLTP oriented random updates Multimedia oriented streaming files

The Sun Oracle Database Machinehandles any combination of workloadswith extreme performance

And predictable response times

Dramatic new consolidationcapabilities

ERP

CRM

Warehouse

Data Mart

HR

Consolidate Storage

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Consolidate Storage

Exadata and ASM allow all storage servers to beshared across databases

Shared Configuration Advanced data striping spreads every database across

all storage servers

Eliminates hot-spots and captive unused space Full storage grid performance available to all databases Database or cluster level storage security

Predictable Performance Exadata I/O resource manager prioritizes I/Os to

ensure predictable performance At user, job, application, or database level

No need for isolated storage islands

ERP

CRM

Warehouse

Data Mart

HR

Consolidate Servers

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Consolidate Servers

Many databases can run on DatabaseMachine servers

Shared Configuration Applications connect to a database service that

runs on one or more database nodes Services can grow, shrink, & move

dynamically Large databases can span nodes using RAC Multiple small databases can run on a single node

Predictable performance Instance caging provides predictable CPU

resources when multiple databases run on thesame node

Restricts a database to subset of processors

ERP CRM

Warehouse

Data

Mart

HR

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The Architecture of the FutureMassively Parallel Grid

Best for Data WarehousingBest for OLTPBest for Consolidation

Resources

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Resources

Oracle.com:http://www.oracle.com/exadata

Oracle Exadata Technology Portal on OTN:http://www.oracle.com/technology/products/bi/db/exadata

Oracle Exadata white papers:http://www.oracle.com/technology/products/bi/db/exadata/pdf/exadata-technical-whitepaper.pdf

http://www.oracle.com/technology/products/bi/db/exadata/pdf/migr ation-to-exadata-whitepaper.pdf

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Customer Tests on Oracle Exadata

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On average 26x faster than DB2

Benchmark workload with 21Business Objects queries

Longest running query on DB2runs 47x faster on a DatabaseMachine

Less tuning, DB2 schema with200 indexes, DB Machine withonly 12 indexes

Information & Technology US

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Up to 10x faster for queries than Sybase IQ

A query using where clausesfor de-duplication ran 10x faster on a Database Machine

A query using Soundexcomparisons ran 10x faster ona Database Machine

Marketing US

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Up to 11x faster than customer set targets

Database Machine purchased byone of the largest Teradatacustomers in APAC

Target time for a set of CDRqueries is 3 hours. DatabaseMachine Half Rack ran workloadin 47 minutes, 3.8x faster

Data load rates of ~3 TB/hour

Telecommunications APAC

l

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80x faster than Oracle-on-IBM for cross-selling analysis

A complex PL/SQL analysistaking 12 hours or more ran in12 minutes on a Half Rack

Database Machine, 80x faster

Index builds were on average16x faster

Retail EMEA

T l i i EMEA

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Full rack DB Machine is 5x faster than Oracle onSun M-series

One of the most run queries is19x faster on a DatabaseMachine

An ETL batch job that requiredwell over 3 hours runs in 50minutes, 3.6x faster

Telecommunications EMEA

R il APAC

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A full rack DB Machine delivers on average 3x better performance than Netezzas 2 rack NPS10200

A full rack database machineoutperforms a NetezzaNPS10200 by 9x on analyzing3TB for cross selling analysis

Database machine loads dataon average 25% faster

Retail APAC

T i EMEA

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On average 6x faster than an 8 node Teradata 5450*

50 queries on 3TB that run 8hours on Teradata, run justover 1 hour on a DatabaseMachine, 6x faster

Scale out test query on 6xdata volume (18TB) did notcomplete on Teradatas 5550 ,query ran in 3 minutes on afull rack database machine

Transportation EMEA

* Two Teradata systems were involved, a 5450 and a 5550 (see notes for details)

T l i i APAC

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Up to 80x faster than Greenplum on a Sun X4500

177M rows in daily SMS queryruns in 1 minute, compared to16 minutes on Greenplum,16x faster

Capabilities to run real-timeinserts of 37M rows withindividual commits in 27minutes. Impossible on

Greenplum .

Telecommunications APAC

R t il US

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Exadata improved IBM DB performance by more

than 20 X The move to Exadata allowed

daily PoS analysis to becompleted by 7:00 am, instead of 12 Noon

Projected to result in $ 25Mannual revenue growth andbottom line improvement of $ 1.8 M

Also projected to reduce inventoryinvestment capital by $ 4.5 M

CIO quote Exadata is as

advertised, and then some

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R t il US

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Exadata improved query performance by

approximately 170 X The move to Exadata will allow

this retailer to scale Point-of-Saledata from 1TB to 100TB

Gathering daily instead of weekly

PoS data will allow better inventory replenishment andreduction of out-of-stockoccurrences

Exadata completes queries thatcurrently take 6 hours in a little

over 2 minutes

Retail - US

High T h M f t i g US

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In benchmark against Netezza, Exadata ran 4.7 X

faster Application was analysis of QA

data in cell phone manufacturing

Parametric queries ran 4.7 Xfaster on Exadata

Benchmark was executed with2.9 TB of data, with a full rack, andSAS drives

High Tech Manufacturing US

Ph rm ce tic ls US

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Exadata beat the current client timings by 218 X

Exadata beat current clienttimings by 218 X, 19.8 minutes tocurrent 72 hours

Client target was 24 hours, whichExadata handily beat by 73X

This was executed with 50GB,1.6 B rows across 17 tables

Pharmaceuticals US

Insurance US

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Multiple queries were executed, up to 2.6 X faster

than Teradata In this competive benchmark,

Exadata beat both Netezza andTeradata

This was executed with 5.0 TB of data, with a full rack, and SATAdrives

Insurance US

Telecommunications US

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Exadata was 4.5 X faster than in-house for total

batch cycle, savings of 33 hours Exadata outperformed in-house

benchmarks in all areas

initial data load, by 2.3 X

daily run, by 12X

large DB query, 18.7 X

overall, 4.5 X (9.5 hours to 42.5 hours)

Telecommunications - US

Telecommunications US

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Average speed-up due to Exadata was 186 X faster

Exadata executed one test queryin 10 seconds

Customer executed same queryin 4,162 seconds

Speed-up of 416 X

Telecommunications - US

Healthcare US

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On average, Exadata was 7.9 X faster for a national

healthcare research institution 26 "Reverse POV" queries were

run

Exadata performance rangedfrom 3.3 X faster to 26.0 X faster

Healthcare - US

Healthcare State Government

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Overall, Exadata was 10 X faster than client

26 "Reverse POV" queries wererun

Exadata performance rangedfrom 2X faster to 34X faster

Healthcare State Government

Financial US

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Exadata ranged from 14 X to 752 X faster than client

for query sets One query within a set that ran

in 4 hours and 40 minutes, wasrun by Exadata in 17 seconds,988 X faster

Largest single table created andloaded was 330GB

Financial - US

Federal Financial US

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Exadata ranged from 15 X to 186 X faster than client

Application was a datawarehouse application

Exadata completed one particular query in 49 seconds , thecustomer completed the samequery in 3.5 hours

Speed-up of 256X

Federal Financial - US

Healthcare - US

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Exadata 8X faster than IBM P570

For one query, Exadata was 26X faster than IBM

Over 26 queries, Exadataaveraged 8X faster than IBM

All 26 queries faster on Exadata

POV was a Reverse POV, with200GB

Healthcare - US

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Entertainment (V2) US

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Exadata was 74X faster than current environment(combination of Oracle and SQL Server)

One query that could not finish in5 hours , Exadata completed in 52seconds

Exadata queries were 29x faster

than SQL Server One query currently takes 81minutes, Exadata completed in 19seconds ( 256x faster)

Current environment is Oracle10gand SQL Server 2005

POV simulated 25 concurrentusers, on Exadata V2

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Retail (V2) US

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Exadata V2 up to 13.5X faster than Teradata

A series of queries that Teradatacompleted in 4 hours, Exadatacompleted in 17.6 minutes, Exadata13.5X faster

Exadata outperformed Teradata in allthree benchmark tests

o 49 sequential queries 3.0X faster o 695 concurrent queries 1.6X faster o 5 surprise ad hoc queries 13.5X faster

Teradata 13TB compressed to 5.3TBusing Exadata Advanced Compression

POV database was 5.3TB, 62 tables, 84billion rows

17.6

238.0

0.0

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DB Machine Teradata

Retail (V2) US

Financial (V2 - OLTP) US

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In an OLTP execution, Exadata is 1.5X faster than IBM

Exadata achieved over 6,000 inserts per second during an OLTP execution, IBMreached only 4,000, Exadata 1.5X moreinserts over the same period

POV database had 6 tables. One tablehad 256M rows. Other tables were 20Mand 10M rows

HCC compression was used in POV

Customer initially tuned machine,however Exadata performed better when allowed to self-tune

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DB Machine IBM Pseries

Financial (V2 OLTP) US

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