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Extreme querying with_analytics

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Presentation given to the Sydney Oracle meetup on June 30th 2010.
Covering Oracle analytics and advanced aggregate functions

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Extreme querying with_analytics

  1. 2. <ul><li>blah blah NOT LIABLE blah blah blah, I NEVER SAID THAT blah blah READ THE DOCUMENTATION blah blah blah NO PROMISES blah I GET PAID BY THE WORD blah blah </li></ul>Read my blog at HTTP://BLOG.SYDORACLE.COM
  2. 6. <ul><li>Aggregate functions are the basis of many Analytics </li></ul><ul><li>All the standard aggregates (MIN, MAX, COUNT, SUM, etc) can be used with analytic clauses. </li></ul>
  3. 7. <ul><li>Min / Max (with added KEEP) </li></ul><ul><li>KEEP means keep the column value for the highest ranked record. </li></ul>
  4. 8. Which of their cities has the most potential slaves ?
  5. 9. SYDNEY and X both have a population of 2 million
  6. 10. MIN or MAX only makes a difference if there are multiple entries of the same ORDER BY rank
  7. 11. <ul><li>Min / Max (with added KEEP) </li></ul><ul><li>Collect </li></ul><ul><ul><li>Create an collection of all the individual values </li></ul></ul><ul><ul><li>A list of large cities … </li></ul></ul>
  8. 13. <ul><li>Min / Max (with added KEEP) </li></ul><ul><li>Collect </li></ul><ul><li>XMLAgg (in four steps) </li></ul><ul><ul><li>Collect the column(s) into an XML document </li></ul></ul>
  9. 18. <ul><li>Min / Max (with added KEEP) </li></ul><ul><li>Collect </li></ul><ul><li>XMLAGG </li></ul><ul><li>ListAgg </li></ul><ul><ul><li>11g function to create a single VARCHAR2 value from a collection of individual VARCHAR2s </li></ul></ul>
  10. 20. <ul><li>Wrap the aggregate around a CASE statement to give more aggregation possibilities. </li></ul><ul><li>SELECT </li></ul><ul><li>SUM(case when state='VIC' then pop end) vic_pop, </li></ul><ul><li>SUM(case when state='NSW' then pop end) nsw_pop </li></ul><ul><li>FROM cities; </li></ul>
  11. 21. (at last)
  12. 22. <ul><li>Dense Rank / Rank / Row Number </li></ul>
  13. 23. Smithers, Bring me a list of our highest paid employees … and the poisoned donuts.
  14. 24. <ul><li>select name, wage, sector, </li></ul><ul><li>row_number () over </li></ul><ul><li>( partition by sector order by wage desc) rn, </li></ul><ul><li>rank () over </li></ul><ul><li>(partition by sector order by wage desc) rnk, </li></ul><ul><li>dense_rank () over </li></ul><ul><li>(partition by sector order by wage desc) drnk </li></ul><ul><li>from emp </li></ul><ul><li>order by sector, wage desc; </li></ul>
  15. 27. <ul><li>Using ROW_NUMBER with other analytics can confuse… </li></ul><ul><li>select name, wage, cum_wage from </li></ul><ul><li>(select name, wage, </li></ul><ul><li>sum(wage) over (order by wage desc) cwage, </li></ul><ul><li>row_number() over (order by wage desc) rn </li></ul><ul><li>from emp </li></ul><ul><li>where sector = '7G') </li></ul><ul><li>where rn < 3 </li></ul><ul><li>NAME WAGE CUM_WAGE </li></ul><ul><li>Homer 2OO 2OO </li></ul><ul><li>Lenny 1OO 4OO </li></ul>
  16. 29. <ul><li>Dense Rank / Rank / Row Number </li></ul><ul><li>NTILE </li></ul><ul><ul><li>The &quot;Snobs&quot; and &quot;Yobs&quot; function </li></ul></ul><ul><ul><li>Ignore the outliers and extremes </li></ul></ul><ul><ul><li>Or ignore the 'huddled masses' </li></ul></ul>
  17. 31. Exclude the most common 90% Focus on the most common 10%
  18. 32. <ul><li>Dense Rank / Rank / Row Number </li></ul><ul><li>NTILE </li></ul><ul><li>Lag / Lead </li></ul><ul><ul><li>Look around for the previous or next row </li></ul></ul>
  19. 33. <ul><li>MONTH AMOUNT PREV_AMT PERC </li></ul><ul><li>January 340 </li></ul><ul><li>February 340 340 .00 </li></ul><ul><li>March 150 340 -55.88 </li></ul><ul><li>April 130 150 -13.33 </li></ul><ul><li>May 170 130 30.77 </li></ul><ul><li>June 210 170 23.53 </li></ul><ul><li>July 350 210 66.67 </li></ul><ul><li>August 270 350 -22.86 </li></ul><ul><li>September 380 270 40.74 </li></ul>
  20. 34. <ul><li>MON AMOUNT PREV_AMT </li></ul><ul><li>---------- ---------- ---------- </li></ul><ul><li>January 340 </li></ul><ul><li>February 340 340 </li></ul><ul><li>March 150 340 </li></ul><ul><li>April 130 150 </li></ul><ul><li>May 170 130 </li></ul><ul><li>June 170 </li></ul><ul><li>July 350 170 </li></ul><ul><li>August 270 350 </li></ul><ul><li>September 380 270 </li></ul>
  21. 35. <ul><li>Dense Rank / Rank / Row Number </li></ul><ul><li>Percent Rank </li></ul><ul><li>Lag / Lead </li></ul><ul><li>First / Last </li></ul><ul><ul><li>Look further ahead or behind </li></ul></ul>
  22. 36. <ul><li>select to_char(period,'Month') mon, </li></ul><ul><li>amount, </li></ul><ul><li>first_value (amount) over </li></ul><ul><li>( partition by trunc(period,'Q') </li></ul><ul><li>order by period) prev_amt </li></ul><ul><li>from sales </li></ul><ul><li>order by period </li></ul>
  23. 37. <ul><li>MON AMOUNT PREV_AMT </li></ul><ul><li>---------- ---------- ---------- </li></ul><ul><li>January 340 340 </li></ul><ul><li>February 340 340 </li></ul><ul><li>March 150 340 </li></ul><ul><li>April 130 130 </li></ul><ul><li>May 170 130 </li></ul><ul><li>June 210 130 </li></ul><ul><li>July 350 350 </li></ul><ul><li>August 270 350 </li></ul><ul><li>September 380 350 </li></ul>
  24. 38. <ul><li>Rarely needed in practice </li></ul><ul><li>Partition By and Order By normally enough </li></ul>
  25. 39. <ul><li>If you omit the PARTITION clause, especially with in-line views , the results can be BAD </li></ul>
  26. 42. In the inline view, the SUM analytic applies to ALL the Orders in the table.
  27. 44. (if we have time)
  28. 45. <ul><li>Rollup </li></ul><ul><li>Grouping sets </li></ul><ul><li>Cube </li></ul>
  29. 48. <ul><li>Rollup </li></ul><ul><li>Cube </li></ul><ul><ul><ul><li>CUBE allows combinations of columns to be totaled </li></ul></ul></ul>
  30. 50. <ul><li>Rollup </li></ul><ul><li>Cube </li></ul><ul><li>Grouping sets </li></ul><ul><ul><li>Perform grouping across multiple columns </li></ul></ul><ul><ul><li>Without the lower level totals of CUBE </li></ul></ul>
  31. 52. <ul><li>If you think you have a problem which the MODEL clause solves then </li></ul><ul><ul><li>Go have a coffee </li></ul></ul><ul><ul><li>Go have a bar of chocolate </li></ul></ul><ul><ul><li>Go have a beer </li></ul></ul><ul><ul><li>Go have a lie down </li></ul></ul><ul><li>BUT do something else until the feeling wears off </li></ul>

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