how i learned to stop worrying and love oracle
DESCRIPTION
Keynote presentation at the AUSOUG 20/20 conference series, Perth/Melbourne November 2009TRANSCRIPT
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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
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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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