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How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
How I learned to stop worrying and love Oracle
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How I learned to stop worrying and love Oracle

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Keynote presentation at the AUSOUG 20/20 conference series, Perth/Melbourne November 2009

Keynote presentation at the AUSOUG 20/20 conference series, Perth/Melbourne November 2009

Published in: Technology, Business
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  • Apologies, I’m a database type.....Quest is best known for toad, but we also have enterprise monitoring across all levels of the stackIn Melbourne, SQL Navigator + the spotlights. It’s not a complete co-incidence about the star trek theme.
  • I’m worried about the Toad in the red shirt – we all know that red-shirt crewmen die in Star Trek!
  • I know what you’re thinking ... Why the big glassesI wondered to, until I found this picture... I was subliminally role-modeling on BillLarry, of course, doesn’t have the same geeky look.... Or does he. Recently discovered high school photo’s suggest he may have been a big-glassed nerd as well.So we might call 1987 the year of the big glassed geek
  • In 1987 I was working for the Australian Govt. We were moving from centralized mainframe in canberra to minicomputers in each state office (VAX VMS and oracle)
  • That’s a predictable linear growth curve. Gets much worse for unpredictable or cyclic demand So I think it’s real, and it excites me because it represents the realization of a more industrialized model for providing computing resources. In the early days of electricity everybody had thier own power sources and every company needed engineers as a result. Nowdays, few companies need that...
  • Data warehouses doubling every three years.
  • Mention columnar compression Mention flash cache in the database In memory ParalelismMPP styles similar to whats happening in M-R
  • So while I worry about the red-shirt TOAD, I’m not really worried about Oracle. Oracle remains a highly technically innovative company as well as a skilled in the business of software. I’ve certainly got no regrets specializing in Oracle technology all those years ago. Quest is a fairly diversified company and has no vested interest in Oracle per see. We aim to be a strategic partner across all of your technologies: Oracle, Microsoft, Vmware and in emerging technologies.
  • Transcript

    • 1. How I learned to stop worrying and love Oracle<br />Guy Harrison<br />Director Research and Development, Melbourne<br />guy.harrison@quest.com<br />www.guyharrison.net<br />
    • 2. Introductions<br />
    • 3. http://www.motivatedphotos.com/?id=17760<br />
    • 4.
    • 5. Looking back to 1987…..<br />http://www.yearbookyourself.com/<br />
    • 6. 1987: RDBMS/Minicomputer revolution <br />IBM-based MVS mainframes giving way to Minicomputer architectures<br />Era of Big glasses<br />32-bit computers such as DEC VAX<br />Still dumb terminals<br />Oracle vs IMS/Adabas/DB2<br />
    • 7. 1992: Client server revolution <br />IBM PC allows for off loading of some processing to the client<br />Richer Character mode interfaces<br />First graphical interfaces: Windows 3.0<br />Oracle vs Sybase/Ingres/dBase III <br />
    • 8. 1999: Internet/Y2K gold rush<br />Massive IT budgets<br />Scalability at all costs<br />Java<br />3-tier applications<br />Oracle unchallenged <br />
    • 9. 2005: After the gold rush<br /><ul><li>TCO and ROI
    • 10. Cost not capability
    • 11. SQL Server gains share
    • 12. Oracle responds with XE (low end), automation (TCO) and RAC (high end)</li></li></ul><li>2009: Big Data and Clouds <br />Volumes of data strain commercial RDBMS <br />Cloud computing mania<br />
    • 13. Why worry?<br />Dominant players often fail quickly<br />Being on the wrong side of a paradigm shift hurts<br />Theory of disruptive innovation helps explain rapid shifts <br />
    • 14. Functionality demanded at high end of market <br />Functionality<br />Sustaining<br />Innovation<br />Functionality demanded at low end of market <br />Disruptive<br />Innovation<br />Time<br />Disruptive Innovation <br />Oracle RAC<br />Oracle10g<br />Oracle9i<br />OracleXE<br />The Innovators Dilemma, Clayton Christensen, Harvard University Press<br />
    • 15. Larry, Richard and the cloud <br />the provision of virtualized application software, platforms or infrastructure across the network, in particular the internet. <br />Larry Ellison (Sep 08):<br />“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?:<br />Richard Stallman (Oct 08):<br />&amp;quot;It&amp;apos;s worse than stupidity: it&amp;apos;s a marketing hype campaign.&amp;quot; <br />Larry Ellison (Sep 09):<br />“It’s this nonsense ... Water vapour”<br />
    • 16. Cloud Ingredients and recipes <br />Utility <br />Computing <br />AKA <br />Private <br />Cloud<br />Clustering<br />Single workload <br />across <br />multiple host<br />SaaS<br />Software as a Service<br />Salesforce.com<br />Gmail <br />Internet<br />Cloud<br />Computing<br />Virtualization<br />Multiple workloads <br />on<br />Single host<br />IaaS<br />Infrastructure as a Service<br />Amazon Web Services<br />Joyent<br />Grid management<br />Allocate resources on <br />demand<br />PaaS<br />Platform as a Service<br />Google App Engine<br />Azure<br />
    • 17. Elastic provisioning<br />Capacity / Demand<br />Demand<br />Hardware upgrade<br />Under provisioned<br />Capacity<br />Over provisioned<br />Time<br />
    • 18. Big Data<br />The Industrial Revolution of data* <br />User generated data:<br />Twitter, Facebook, Amazon <br />Machine generated data:<br />RFID, POS, cell phones, GPS<br />Traditional RDBMS neither economic or capable<br />* http://radar.oreilly.com/2008/11/the-commoditization-of-massive.html<br />
    • 19. Big data 1: Google <br />
    • 20. Map Reduce <br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Start<br />Reduce<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />Map<br />
    • 21. Hadoop: Open source Map-reduce <br />Yahoo! Hadoop cluster:<br /> 4000 nodes<br /> 16PB disk<br /> 64 TB of RAM<br /> 32,000 Cores<br />
    • 22. Big Data 2: Twitter (and Web 2.0)<br />
    • 23. The fail whale<br />
    • 24. Twitter 2009<br />
    • 25. Memcached and Sharding<br />Web Servers<br />Memcached servers<br />Database Servers<br />Master<br />Slave<br />Slave<br />
    • 26. The NoSQL movement<br />
    • 27. CAP Theorem: You can’t have it all<br />Eventual consistency:<br />“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.”<br />Availability (Total redundancy)<br />Consistency: ACID transactions<br />RAC<br />No GO<br />NoSQL DB<br />Partition Tolerance: Infinite scaleout<br />
    • 28. Non-Relational DBs<br /><ul><li>Document oriented
    • 29. CouchDb
    • 30. MongoDb
    • 31. Scalaris
    • 32. Persevere
    • 33. Key Value:
    • 34. MemcacheDb
    • 35. Voldemort
    • 36. Tokyo Cabinet
    • 37. Dynamo/Dynamite
    • 38. Redis</li></ul>Column oriented:<br />BigTable<br />HyperTable<br />Hbase<br />SimpleDb<br />Azure Table Services<br />Cassandra<br />
    • 39. Big Data 3: Data Warehousing <br />
    • 40. Data warehousing and Oracle<br />
    • 41. DATAllegro architecture<br />
    • 42. Column Databases (Vertica)<br />Data is stored together in columns<br />Very fast answers to analytic aggregate queries<br />Better compression<br />Not write optimized<br />
    • 43. Oracle EXADATA<br />RAC clusters provide MPP<br />Dedicated storage servers<br />High Speed infiniband channels <br />Smart storage reduces data transfer requirements <br />
    • 44. Big Data vs. Fast Data<br />
    • 45. Economics of SSD<br />
    • 46. Hierarchical storage management <br />$/GB<br />$/IOP<br />
    • 47. Oracle 2009 innovations<br />Sun Oracle database machine<br />Exadata flash cache<br />Database flash cache (coming soon)<br />Hybrid Columnar compression<br />
    • 48. Not worrying, just wondering...<br />How will Oracle deal respond to Hadoop?<br />Will Oracle play in the NoSQL database world?<br />What will happen to MySQL?<br />What will happen to red-shirt TOAD?<br />

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