How I learned to stop worrying and love Oracle

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© 2009 Quest Software, Inc. ALL RIGHTS RESERVED

How I learned to stop worrying and love Oracle

Guy Harrison

Director Research and Development, Melbourne

guy.harrison@quest.com

www.guyharrison.net

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Introductions

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http://www.motivatedphotos.com/?id=17760

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Blue

Yellow

Red

0 10 20 30 40 50 60 70 80

Star trek shirt fatality analysis

Pct

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Looking back to 1987…..

http://www.yearbookyourself.com/

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1987: RDBMS/Minicomputer revolution • IBM-based MVS

mainframes giving way to Minicomputer architectures

• Era of Big glasses• 32-bit computers such as

DEC VAX• Still dumb terminals• Oracle vs

IMS/Adabas/DB2

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1992: Client server revolution • IBM PC allows for off

loading of some processing to the client

• Richer Character mode interfaces

• First graphical interfaces: Windows 3.0

• Oracle vs Sybase/Ingres/dBase III

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1999: Internet/Y2K gold rush• Massive IT budgets• Scalability at all costs• Java• 3-tier applications• Oracle unchallenged

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2005: After the gold rush• TCO and ROI

• Cost not capability

• SQL Server gains share

• Oracle responds with XE (low end), automation (TCO)

and RAC (high end)

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2009: Big Data and Clouds • Volumes of data strain

commercial RDBMS • Cloud computing mania

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Why worry?• Dominant players often

fail quickly• Being on the wrong

side of a paradigm shift hurts

• Theory of disruptive innovation helps explain rapid shifts

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Disruptive Innovation

Time

Fun

ctio

nalit

y

Functionality demanded at high end of market

Functionality demanded at low end of market

Sustaining

Innovation

Disruptive

Innovation

The Innovators Dilemma, Clayton Christensen, Harvard University Press

Oracle

9i

Oracle

10g

Oracle RAC

OracleXE

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Larry, Richard and the cloud • the provision of virtualized application software,

platforms or infrastructure across the network, in particular the internet.

• Larry Ellison (Sep 08):– “we’ve redefined cloud computing to include

everything that we already do … It’s complete gibberish. It’s insane. When is this idiocy going to stop?:

• Richard Stallman (Oct 08):– "It's worse than stupidity:

it's a marketing hype campaign." • Larry Ellison (Sep 09):

– “It’s this nonsense ... Water vapour”

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Cloud Ingredients and recipes

SaaS

Software as a Service

Salesforce.com

Gmail

IaaS

Infrastructure as a Service

Amazon Web Services

Joyent

PaaS

Platform as a Service

Google App Engine

Azure

Clustering

Single workload

across

multiple host

Virtualization

Multiple workloads

on

Single host

Grid management

Allocate resources on

demand

Utility

Computing

AKA

Private

Cloud

InternetCloud

Computing

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Elastic provisioning

Over provisioned

Under provisioned

Capacity /

Demand

Time

Demand

Hardware upgrade

Capacity

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Big Data• The Industrial Revolution of data*

– User generated data:• Twitter, Facebook, Amazon

– Machine generated data:• RFID, POS, cell phones, GPS

• Traditional RDBMS neither economic or capable

* http://radar.oreilly.com/2008/11/the-commoditization-of-massive.html

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Big data 1: Google

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Map Reduce

Start ReduceMapMap

MapMap

MapMap

MapMap

MapMap

MapMap

Map

MapMap

MapMap

MapMap

MapMap

MapMap

MapMap

MapMap

MapMap

MapMap

MapMap

MapMap

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Hadoop: Open source Map-reduce

• Yahoo! Hadoop cluster:– 4000 nodes– 16PB disk– 64 TB of RAM– 32,000 Cores

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Big Data 2: Twitter (and Web 2.0)

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The fail whale

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Twitter 2009

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Memcached and Sharding

Web Servers

Memcached servers

Database Servers

Master

Slave

Slave

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The NoSQL movement

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CAP Theorem: You can’t have it all

Consistency: ACID

transactions

Availability (Total

redundancy)RAC

Partition Tolerance:

Infinite scaleout

No GO

NoSQL DB

Eventual consistency:

“when no updates occur for a long period

of time, eventually all updates will

propagate through the system and all the

replicas will be consistent.”

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Non-Relational DBs

• Column oriented:– BigTable – HyperTable– Hbase– SimpleDb– Azure Table Services– Cassandra

• Document oriented

– CouchDb

– MongoDb

– Scalaris

– Persevere• Key Value:

• MemcacheDb

• Voldemort

• Tokyo Cabinet

• Dynamo/Dynamite

• Redis

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Big Data 3: Data Warehousing

1996 1998 2000 2002 2004 2006 2008 20100

100

200

300

400

500

600

TB

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Data warehousing and Oracle

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DATAllegro architecture

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Column Databases (Vertica)

• Data is stored together in columns

• Very fast answers to analytic aggregate queries

• Better compression• Not write optimized

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Oracle EXADATA

• RAC clusters provide MPP• Dedicated storage servers• High Speed infiniband

channels • Smart storage reduces data

transfer requirements

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Big Data vs. Fast Data

Solid State Disk DDR-RAM

Solid State Disk Flash

Magnetic Disk

0 1,000 2,000 3,000 4,000 5,000

15

200

4,000

microseconds

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Economics of SSD

Capacity HDDs

Performance HDDs

Flash SSDs (read only)

DRAM SSDs

$0 $1 $10 $100 $1,000

$13.30

$16.60

$1.40

$0.50

$3.00

$28.00

$100.00

$400.00

$/GB$/IOPs

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Hierarchical storage management

Main Memory

DDR SSD

Flash SSD

Disk

Tape

$/IO

P$/G

B

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Oracle 2009 innovations

• Sun Oracle database machine

• Exadata flash cache• Database flash cache

(coming soon)• Hybrid Columnar

compression

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Not worrying, just wondering...• How will Oracle deal respond

to Hadoop?• Will Oracle play in the

NoSQL database world?• What will happen to MySQL?• What will happen to red-shirt

TOAD?

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© 2009 Quest Software, Inc. ALL RIGHTS RESERVED

너를 감사하십시요 Thank You Danke Schön

Gracias 有難う御座いました Merci

Grazie Obrigado 谢谢