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Reduce Data Sprawl

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Reduce Data Sprawl. Add Intelligence. Using Graphics to Tune Oracle. Monday, June 9, 2014 Kyle Hailey Kyle.Hailey@oracle.com. *** Overview ***. Target Audience: Production DBA. A Developer would have a different view. Performance Anxiety.

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Reduce Data Sprawl

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  12. Reduce Data Sprawl

  13. Add Intelligence

  14. Using Graphics to Tune Oracle Monday, June 9, 2014 Kyle Hailey Kyle.Hailey@oracle.com

  15. *** Overview ***

  16. Target Audience: Production DBA A Developer would have a different view

  17. Performance Anxiety When the manager is asking why is the … • Response is slow • Database is hung • Batch job behind schedule How do you respond?

  18. Respond Quickly and Confidently Like the Doctor: • Test – collect Data • Diagnosis - assessment • Cure – fix problems

  19. A New Approach to Oracle Tuning • Old OEM Design Approach • New OEM Design Approach • Macintosh Design Approach • Result of Good Design • Our Goal

  20. Old Design Method: • List All Tuning data • Create screens for all data • Verify that all data is displayed => Overwhelming and confusing

  21. OEMs New Design Method • Start with Typical Problems • Identify Solutions • Design a Visual Representation => Put intelligence into the interface

  22. Macintosh Method • What do people do • How do they do it • Design to support it => can be used without reading a manual

  23. Without Reading a Manual • Handspring’s site crashed Nov 25, 1999 • Biggest Sales Day of the Year • Library cache latch contention • No DBAs • Downloaded Quest’s Spotlight • Installed and Identified problem with Minutes • Solution in code fix

  24. Goal Make Oracle Tuning easy for Anyone

  25. *** Manageability *** Obstacles and Approaches

  26. What Data to Collect ? On NT 9iR2 Oracle Tuning Data in 9iR2 NT • Stats 248 • Waits 361 • Latches 239 • Parameters 257 • Hidden Parameters 540 Total 1645 As well as • 259 V$ tables • 394 X$ tables And machine data

  27. Statspack Collects the Data • Standard for Data Collection • Selects from 44 x$, v$ and dba tables and views • 1000-1500 lines output

  28. But, How to Analyze the Statspack Data? • One page Summary • Waits are Emphasized to Guide Analysis • Despite that, Data can be confusing to Analyze • Oracle Press book is 600+ pages • Users still get led astray • Recent mail: • IO waits dominate • one SQL was 99% of the IO • still concerned about parsing.

  29. Origin of Problem: Tuning Rules Lists • Originated from Oracle Version 6 • Calculate ratios to deduce bottlenecks • Often lead down the wrong path • Result: tuning inconsequential aspects

  30. Wait Interface – The Guide • Introduced in Version 7 • Pinpoints performance Bottlenecks • Used by Statspack • Needs a clearer presentation than Statspack

  31. Silver Bullet Myth • _go_faster=true • Coping with overwhelming amount of data • Oracle Expert type products • Not All Solutions are Known

  32. Combine Analysis with Diagnostics • Known Solutions • Diagnostics Methods • Powerful Graphic Interface • Flexibility to Grow with Changes

  33. *** Graphics *** the Advantage

  34. Why Use Graphics You can't imagine how many times I was told that nobody wanted or would use graphics … now everybody uses them. -- Jef Raskin, the creator of the Macintosh

  35. Macintosh Paradigm • Introduced Graphical Windows • Introduced the use of the “mouse” • Revolutionized the Interface to the computer • First computer I used without Reading the Manual • Manageable computer

  36. Oracle and Manageability • Microsoft's perceived ease of use • Competition Brings Innovation • Oracle is now tackling manageability • BMC, Quest, Precise improve the manageability perception of Oracle

  37. Why Use Graphics in Oracle Tuning “The human nervous system … is exceptionally good at parsing visual information, especially when that information is coded by color and/or motion.” Knowledge representation in cognitive science. Westbury, C. & Wilensky, U. (1998)

  38. Why Use Graphics in Oracle Tuning Increase comprehension of Data by • Correlating • Increasing data Density • Providing known Solutions • Guiding Investigation

  39. “Parsability of Graphics” Infocus – (overhead projectors) sited a study that humans can parse graphical information 400,000 times faster than textual data

  40. Trends: What are the Trends?

  41. Trends: With Graphics I II III IV

  42. Trends: Waits Over Time date 17:13 17:13 17:14 17:14 17:15 17:15 17:16 17:16 17:17 17:17 17:18 17:18 17:19 17:19 17:20 17:20 17:21 17:21 17:22 17:22 17:23 17:23 17:24 17:24 17:25 17:25 17:26 17:26 17:27 17:27 17:28 17:28 17:29 17:29 17:30 17:30 17:31 17:31 17:32 17:32 17:33 17:33 17:34 17:34 17:35 17:35 17:36 17:36 17:37 17:37 17:38 17:38 17:39 17:39 17:40 17:40 17:41 17:41 17:42 log_file_switch_completion_3 0 0 0 0 2982936 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 237642 0 0 0 0 0 0 0 0 0 0 0 0 2100419 0 0 0 0 log_buffer_space_3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6478221 0 14945538 5219836 0 0 QL*Net_break/reset_to_client_3 8501199 11385539 7044359 8234690 6183237 2333297 7776659 6118198 6495648 7518489 11455326 7702865 11085762 7634300 10582833 13260400 8544422 13172524 8502041 12010958 9208533 9384118 10208957 7399676 6747457 6231611 13441429 7344615 8761884 6642950 7256315 7073623 8442915 7060399 7485662 6601072 6349018 9929440 4902230 6079359 5751021 9455790 9087866 9341209 7664906 16499162 10234535 10588829 8638014 8918795 4312962 8056957 4944408 7195059 3332565 3154348 5530740 4965315 7011341 5248675 6846116 6740378 5817539 9012238 7464656 9055304 8781313 9115954 9858761 latch_free_3 4306495 1931327 3327222 2181023 1764023 3238736 1637877 1922399 1786672 2312507 23083793 1091835 1862666 6949805 6453080 781313 2767384 1164596 2249654 440342 2076414 4062214 2408959 2886800 2177722 3332627 1237003 2098221 5986478 828048 2570183 2104943 2686288 2259440 2388341 2044871 1462208 2306024 1635624 2132461 783102 2677123 4743636 1292606 1499421 2439544 2753717 1905407 3017933 1651176 3686294 1088209 922402 2319624 2645539 1260079 4316997 1311242 2087917 rdbms_ipc_reply_3 27920139 31888722 29909917 27903331 27939736 29891410 29901186 29953756 27895419 29879828 28086221 29906822 27903067 29915873 29896307 31878918 28271932 29883910 29885870 29906113 27933209 27794807 29879997 28842040 30138759 12102954 0 0 0 0 0 0 0 0 0 0 0 4681562 0 0 1018 22981 0 0 0 383 0 0 0 0 0 0 0 0 0 0 0 3079 0 700 0 604 0 0 42240 0 0 0 508 2702 log_file_sync_3 4897094 5984818 8773859 6732708 2528699 21640100 4667088 9349937 6102384 5648418 16238557 9202562 4214425 12911555 8677281 1982443 5549138 2978201 4361914 2924674 3969540 4608851 4499043 5563593 7480457 8736114 4087525 7916623 9531711 4204114 4002465 4230277 4795701 6324440 4740469 5123660 2891044 8931372 4254477 6177218 5119836 4701245 9094181 6751040 6969569 8678671 10627609 8452116 13505152 9052070 9199762 6939591 39153732 18974176 71305938 25005388 6781095 3927165 enqueue_3 39067 87598 58429 93754 38807 270867 64623 76158 125199 39092426 73909741 82618 78157 12192579 30084139 30154100 30426042 30034564 30058775 30062114 27062239 27585615 30040241 23741759 139008 75112 46778 78246 30794 93934 850847 83964 91959 572765 18938 214037 112043 93392 513358 80052 121984 148309 1683718 73709 33330 73894 476811 2072430 34138 40273 88209 512731 460956 492492 2979781 50846 80892 84755 248836 db_file_sequential_read_3 107039051 109566587 127008942 97033615 74615747 70728654 109231682 93755676 86966703 117638046 76065788 88137834 66759488 89709570 80277614 50762622 58207938 61815450 65610580 51599402 74260828 78138965 49867005 21069074 17680159 11460495 12467891 33786092 33211896 40736046 49212320 41162084 45786601 24122201 55651100 34316957 11773008 62455231 48897870 67248119 64077462 88899277 89622175 34483526 57402401 35319735 30619562 61707668 34514340 39504714 37681062 34165368 24952296 30172023 25510144 40260331 83215876 78450352 56963813 33846491 28869234 52964974 38161443 43878778 31095295 26018747 58239638 19793917 7914975 itch_(checkpoint_incomplete)_3 0 0 0 0 285950959 247915390 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

  43. Trends: Waits over Time

  44. Quantity: Map Alone is 20,000 points

  45. Quantity: Extent Mapping

  46. Quantity: Extent Mapping with Mouse

  47. Quantity: Seeing Patterns: Extent Anomalies

  48. Quantity: Zoom Out, Over 8000 data pts

  49. Correlating Data

  50. Perspective:Tufte

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