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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

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How I learned to stop worrying and love Oracle

  1. 1. How I learned to stop worrying and love Oracle<br />Guy Harrison<br />Director Research and Development, Melbourne<br /><br /><br />
  2. 2. Introductions<br />
  3. 3.<br />
  4. 4.
  5. 5. Looking back to 1987…..<br /><br />
  6. 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. 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. 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. 9. 2005: After the gold rush<br /><ul><li>TCO and ROI
  10. 10. Cost not capability
  11. 11. SQL Server gains share
  12. 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. 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. 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. 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 />&quot;It&apos;s worse than stupidity: it&apos;s a marketing hype campaign.&quot; <br />Larry Ellison (Sep 09):<br />“It’s this nonsense ... Water vapour”<br />
  16. 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 /><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. 17. Elastic provisioning<br />Capacity / Demand<br />Demand<br />Hardware upgrade<br />Under provisioned<br />Capacity<br />Over provisioned<br />Time<br />
  18. 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 />*<br />
  19. 19. Big data 1: Google <br />
  20. 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. 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. 22. Big Data 2: Twitter (and Web 2.0)<br />
  23. 23. The fail whale<br />
  24. 24. Twitter 2009<br />
  25. 25. Memcached and Sharding<br />Web Servers<br />Memcached servers<br />Database Servers<br />Master<br />Slave<br />Slave<br />
  26. 26. The NoSQL movement<br />
  27. 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. 28. Non-Relational DBs<br /><ul><li>Document oriented
  29. 29. CouchDb
  30. 30. MongoDb
  31. 31. Scalaris
  32. 32. Persevere
  33. 33. Key Value:
  34. 34. MemcacheDb
  35. 35. Voldemort
  36. 36. Tokyo Cabinet
  37. 37. Dynamo/Dynamite
  38. 38. Redis</li></ul>Column oriented:<br />BigTable<br />HyperTable<br />Hbase<br />SimpleDb<br />Azure Table Services<br />Cassandra<br />
  39. 39. Big Data 3: Data Warehousing <br />
  40. 40. Data warehousing and Oracle<br />
  41. 41. DATAllegro architecture<br />
  42. 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. 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. 44. Big Data vs. Fast Data<br />
  45. 45. Economics of SSD<br />
  46. 46. Hierarchical storage management <br />$/GB<br />$/IOP<br />
  47. 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. 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 />