extreme querying with_analytics

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Aggregate functions are the basis of many Analytics

All the standard aggregates (MIN, MAX, COUNT, SUM, etc) can be used with analytic clauses.

Min / Max (with added KEEP)

KEEP means keep the column value for the highest ranked record.

Which of their cities has the most potential slaves ?

SYDNEY and X both have a population of 2 million

MIN or MAX only makes a difference if there are multiple entries of the same ORDER BY rank

Min / Max (with added KEEP) Collect

Create an collection of all the individual values

A list of large cities …

Min / Max (with added KEEP) Collect XMLAgg (in four steps)

Collect the column(s) into an XML document

Min / Max (with added KEEP) Collect XMLAGG ListAgg

11g function to create a single VARCHAR2 value from a collection of individual VARCHAR2s

Wrap the aggregate around a CASE statement to give more aggregation possibilities.

SELECT SUM(case when state='VIC' then pop end)

vic_pop, SUM(case when state='NSW' then pop end)

nsw_pop FROM cities;

(at last)

Dense Rank / Rank / Row Number

Smithers,Bring me a list of our highest paid employees…and the poisoned donuts.

select name, wage, sector, row_number() over (partition by sector order by wage

desc) rn, rank() over (partition by sector order by wage desc)

rnk, dense_rank() over (partition by sector order by wage desc)

drnkfrom emporder by sector, wage desc;

Using ROW_NUMBER with other analytics can confuse…

select name, wage, cum_wage from (select name, wage, sum(wage) over (order by wage desc) cwage, row_number() over (order by wage desc) rn from emp where sector = '7G') where rn < 3

NAME WAGE CUM_WAGE Homer 2OO 2OO Lenny 1OO 4OO

Dense Rank / Rank / Row Number NTILE

The "Snobs" and "Yobs" function

Ignore the outliers and extremes Or ignore the 'huddled masses'

Exclude the most common 90%

Focus on the most common 10%

Dense Rank / Rank / Row Number NTILE Lag / Lead

Look around for the previous or next row

MONTH AMOUNT PREV_AMT PERC January 340 February 340 340 .00 March 150 340 -55.88 April 130 150 -13.33 May 170 130 30.77 June 210 170 23.53 July 350 210 66.67 August 270 350 -22.86 September 380 270 40.74

MON AMOUNT PREV_AMT ---------- ---------- ---------- January 340 February 340 340 March 150 340 April 130 150 May 170 130 June 170 July 350 170 August 270 350 September 380 270

Dense Rank / Rank / Row Number Percent Rank Lag / Lead First / Last

Look further ahead or behind

select to_char(period,'Month') mon, amount, first_value(amount) over (partition by trunc(period,'Q') order by period) prev_amt from sales order by period

MON AMOUNT PREV_AMT ---------- ---------- ---------- January 340 340 February 340 340 March 150 340 April 130 130 May 170 130 June 210 130 July 350 350 August 270 350 September 380 350

Rarely needed in practice Partition By and Order By normally

enough

If you omit the PARTITION clause, especially with in-line views , the results can be BAD

In the inline view, the SUM analytic applies to ALL the Orders in the table.

(if we have time)

Rollup Grouping sets Cube

Rollup Cube

CUBE allows combinations of columns to be totaled

Rollup Cube Grouping sets

Perform grouping across multiple columns Without the lower level totals of CUBE

If you think you have a problem which the MODEL clause solves then Go have a coffee Go have a bar of chocolate Go have a beer Go have a lie down

BUT do something else until the feeling wears off