Abstract: The session will breakdown the Model clause into its fundamental components and provides some basic real-world examples to demonstrate its greater potential. Though most developers have heard of the SQL Model clause in 10g, many may baulk at the idea of using it - daunted by seemingly foreign syntax that might well have come out of a FORTRAN program. Look a little closer and you'll find it's just like building a spreadsheet. Concise, easy to read syntax that provides the functionality for demanding calculations that would normally require elaborate joins, unions, analytics or PL/SQL. In addition to the development and maintenance burden, we are also faced with the all too familiar problem of business customers duplicating data to an Excel spreadsheet that is shared and erroneously modified around the workplace. This session uses the Model clause as a high performance tool that can simplify approaches to every day problems. It demonstrates that Model is an extension to SQL that forms multi-dimensional arrays with inter-row & inter-array calculations that automatically resolves formula dependencies.

1. The Model Clause
Spreadsheet Techniques
using SQL
Scott Wesley

2. Agenda
• Concepts
• Cell Referencing
• Focus
• Performance
• Final thoughts…

3. Any Model experts?
SELECT c, p, m, pp, ip
FROM mortgage
MODEL
REFERENCE R ON
(SELECT customer, fact, amt
FROM mortgage_facts
MODEL DIMENSION BY (customer, fact)
MEASURES (amount amt) RULES
(amt[any, 'PaymentAmt']= (amt[CV(),'Loan']*
Power(1+ (amt[CV(),'Annual_Interest']/100/12),amt[CV(),'Payments'])*
(amt[CV(),'Annual_Interest']/100/12)) /
(Power(1+(amt[CV(),'Annual_Interest']/100/12), amt[CV(),'Payments'])
- 1)))
DIMENSION BY (customer cust, fact)
MEASURES (amt)
MAIN amortization
PARTITION BY (customer c)
DIMENSION BY (0 p)
MEASURES (principalp pp, interestp ip, mort_balance m, customer mc)
RULES ITERATE(1000) UNTIL (ITERATION_NUMBER+1 =r.amt[mc[0],'Payments'])
(ip[ITERATION_NUMBER+1] = m[CV()-1] * r.amt[mc[0],
'Annual_Interest']/1200
,pp[ITERATION_NUMBER+1] = r.amt[mc[0], 'PaymentAmt'] - ip[CV()]
,m[ITERATION_NUMBER+1] = m[CV()-1] - pp[CV()])
ORDER BY c, p;

4. FizzBuzz
• Write a program that prints the numbers
from 1 to 100.
• But for multiples of three print “Fizz”
instead of the number and for the
multiples of five print “Buzz”.
• For numbers which are multiples of both
three and five print “FizzBuzz”
http://tkyte.blogspot.com/2007/02/what-is-your-fizzbuzz-factor.html

5. SQL> WITH data AS (SELECT LEVEL AS n
FROM DUAL CONNECT BY LEVEL <= 100)
SELECT n, NVL(CASE WHEN MOD(n,3) = 0 THEN 'Fizz' END ||
CASE WHEN MOD(n,5) = 0 THEN 'Buzz' END,
TO_CHAR(n)) AS answer
FROM data;
N ANSWER
---- -----------
1 1
2 2
3 Fizz
4 4
5 Buzz
6 Fizz
7 7
8 8
9 Fizz
10 Buzz
11 11
12 Fizz
13 13
14 14
15 FizzBuzz
16 16
17 17
18 Fizz
...

6. Anthony Wilson said....
So... do we win points by using MODEL, or get thrown out of the interview on
mental health grounds?
Nobody in their right mind actually uses this stuff, right?
:)
select id, n
from all_objects
where object_id = 1
model
dimension by (object_id id)
measures (object_name n)
rules (
n[for id from 1 to 100 increment 1] = to_char(cv(id)),
n[mod(id, 3) = 0] = 'fizz',
n[mod(id, 5) = 0] = 'buzz',
n[mod(id, 15) = 0] = 'fizzbuzz'
)
order by id

7. SQL Spreadsheets
Craft

8. Basic Syntax
<prior clauses of SELECT statements>
MODEL [main] [RETURN {ALL|UPDATED} ROWS]
[reference models]
[PARTITION BY (<cols>)]
DIMENSION BY (<cols>)
MEASURES (<cols>)
[IGNORE NAV] | [KEEP NAV]
[RULES
[UPSERT | UPDATE]
[AUTOMATIC ORDER | SEQUENTIAL ORDER]
[ITERATE (n) [UNTIL <condition>] ]
( <cell_assignment> = <expression> ... )

9. SELECT country
,year
,sales
,previous_year
,before_that
FROM sales_view
GROUP BY year, country
-- Define model structure/options
MODEL RETURN ALL ROWS
PARTITION BY (country)
DIMENSION BY (year)
MEASURES (SUM(sales) AS sales
-- Define 'calculated columns'
,CAST(NULL AS NUMBER) previous_year
,CAST(NULL AS NUMBER) before_that)
-- Define rule options
RULES AUTOMATIC ORDER (
-- Define actual rules
previous_year[ANY] = sales[CV()-1]
,before_that [ANY] = sales[CV()-2]
)
-- Define order of entire result set
ORDER BY country, year;
Apply model
clause to
this query

10. Concepts
Country Product Year Sales
Partition Dimension Dimension Measure
AUS HAMMER 2006 10
AUS NAIL 2006 200
NZ NAIL 2006 300
NZ DRILL 2007 50

11. Dimension
Country Product Year Sales
Partition Dimension Dimension Measure
<prior clauses of SELECT statements>
MODEL [main] [RETURN {ALL|UPDATED} ROWS]
[reference models]
[PARTITION BY (<cols>)]
DIMENSION BY (product p, year y)
MEASURES (<cols>)
[IGNORE NAV] | [KEEP NAV]
[RULES …

12. Measure
Country Product Year Sales
Partition Dimension Dimension Measure
<prior clauses of SELECT statements>
MODEL [main] [RETURN {ALL|UPDATED} ROWS]
[reference models]
[PARTITION BY (<cols>)]
DIMENSION BY (<cols>)
MEASURES (sales)
[IGNORE NAV] | [KEEP NAV]
[RULES …

13. Partition
Country Product Year Sales
Partition Dimension Dimension Measure
<prior clauses of SELECT statements>
MODEL [main] [RETURN {ALL|UPDATED} ROWS]
[reference models]
[PARTITION BY (country)]
DIMENSION BY (<cols>)
MEASURES (<cols>)
[IGNORE NAV] | [KEEP NAV]
[RULES …

14. Array
20
15 5
35 15
Hammer Drill
Product
2006
2007
2008
C
ountry
Year
Aus
N
Z

15. Rules
<prior clauses of SELECT statements>
MODEL [main] [RETURN {ALL|UPDATED} ROWS]
[reference models]
[PARTITION BY (<cols>)]
DIMENSION BY (<cols>)
MEASURES (<cols>)
[IGNORE NAV] | [KEEP NAV]
[RULES
[UPSERT | UPDATE]
[AUTOMATIC ORDER | SEQUENTIAL ORDER]
[ITERATE (n) [UNTIL <condition>] ]
( <cell_assignment> = <expression> ... )

16. Rules
COUNTRY PRODUCT YEAR SALES
Partition Dimension Dimension Measure
AUS Hammer 2006 20
AUS Hammer 2007 15
AUS Drill 2007 5
NZ Hammer 2006 15
NZ Hammer 2007 10
NZ Drill 2007 10
AUS Hammer 2008 35
AUS Drill 2008 15
NZ Hammer 2008 25
NZ Drill 2008 30
Original
Data
Rule
Results
Sales[Hammer,2008] = Sales[Hammer,2006]+Sales[Hammer,2007]
Sales[Drill ,2008] = Sales[Drill,2007]*3

17. Spreadsheet
Sales[Hammer,2008] = Sales[Hammer,2006]+Sales[Hammer,2007]
Sales[Drill ,2008] = Sales[Drill,2007]*3

18. Return Rows
<prior clauses of SELECT statements>
MODEL [main] [RETURN {ALL|UPDATED} ROWS]
[reference models]
[PARTITION BY (<cols>)]
DIMENSION BY (<cols>)
MEASURES (<cols>)
[IGNORE NAV] | [KEEP NAV]
[RULES
[UPSERT | UPDATE]
[AUTOMATIC ORDER | SEQUENTIAL ORDER]
[ITERATE (n) [UNTIL <condition>] ]
( <cell_assignment> = <expression> ... )

19. Rules
COUNTRY PRODUCT YEAR SALES
Partition Dimension Dimension Measure
AUS Hammer 2006 20
AUS Hammer 2007 15
AUS Drill 2007 5
NZ Hammer 2006 15
NZ Hammer 2007 10
NZ Drill 2007 10
AUS Hammer 2008 35
AUS Drill 2008 15
NZ Hammer 2008 25
NZ Drill 2008 30
RETURN
ALL
ROWS
RETURN
UPDATED
ROWS
Sales[Hammer,2008] = Sales[Hammer,2006]+Sales[Hammer,2007]
Sales[Drill ,2008] = Sales[Drill,2007]*3

20. Positional Cell Referencing
SELECT country, product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES (
sales['Bounce', 2000] = 10
,sales['Bounce', 2008] = 20 );
COUNTRY PROD YEAR SALES
------------ ------------ ----- ----------
Australia Bounce 2000 10
Australia Bounce 2008 20

21. Symbolic Cell Referencing
SELECT country, product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES (
sales[prod = 'Bounce', year > 1999] = 10 )
ORDER BY country, product, year;
COUNTRY PRODUCT YEAR SALES
------------ ------------ ----- ----------
Australia Bounce 2000 10
Australia Bounce 2001 10

22. Multi-Cell Access on Right Side
SELECT country, product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES (
sales['Bounce',2008] = 100 + MAX(sales)
['Bounce', year BETWEEN 1998 and 2002]
,sales['Y Box',2008] = 1.3 * PERCENTILE_DISC(0.5)
WITHIN GROUP (ORDER BY sales)
[product in ('Bounce', 'Y Box'), year < 2003]);
COUNTRY PRODUCT YEAR SALES
---------- -------- ----- -------
Australia Bounce 2008 3861
Australia Y Box 2008 4889

23. Multi-Cell Access on Right Side
SELECT country, product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES (
sales['Bounce',2008] = 100 + MAX(sales)
['Bounce', year BETWEEN 1998 and 2002]
,sales['Y Box',2008] = 1.3 * PERCENTILE_DISC(0.5)
WITHIN GROUP (ORDER BY sales)
[product in ('Bounce', 'Y Box'), year < 2003]);
COUNTRY PRODUCT YEAR SALES
---------- -------- ----- -------
Australia Bounce 2008 3861
Australia Y Box 2008 4889

24. CV()

25. SELECT country, product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES (
sales['Bounce', year BETWEEN 1995 AND 2002] =
sales['Mouse Pad', CV(year)] +
0.2 * sales['Y Box', CV()]);
COUNTRY PRODUCT YEAR SALES
---------- -------- ----- -------
Australia Bounce 1999 6061
Australia Bounce 2000 8154
Australia Bounce 2001 15211

26. SELECT country, product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES (
sales['Bounce', year BETWEEN 1995 AND 2002] =
sales['Mouse Pad', CV(year)] +
0.2 * sales['Y Box', CV()]);
COUNTRY PRODUCT YEAR SALES
---------- -------- ----- -------
Australia Bounce 1999 6061
Australia Bounce 2000 8154
Australia Bounce 2001 15211

27. SELECT country, product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES (
sales['Bounce', year BETWEEN 1995 AND 2002] =
sales['Mouse Pad', CV(year)] +
0.2 * sales['Y Box', CV()]);
COUNTRY PRODUCT YEAR SALES
---------- -------- ----- -------
Australia Bounce 1999 6061
Australia Bounce 2000 8154
Australia Bounce 2001 15211

28. SELECT country, product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES (
sales['Bounce', 1999] =
sales['Mouse Pad',1999]+0.2*sales['Y Box',1999]
sales['Bounce', 2000] =
sales['Mouse Pad',2000]+0.2*sales['Y Box',2000]
sales['Bounce', 20001] =
sales['Mouse Pad',2001]+0.2*sales['Y Box',2001]);

29. CV(year)-1

30. SELECT country, product, year, sales, p_year, growth_pct
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales, 0 p_year, 0 growth_pct)
RULES (
p_year[product IN ('Bounce','Y Box'), year BETWEEN 1998 and 2001]
= sales[CV(product), CV(year) -1]
,growth_pct[product IN ('Bounce','Y Box'), year BETWEEN 1998 and
2001]
= 100* (sales[CV(product), CV(year)] -
sales[CV(product), CV(year) -1] ) /
sales[CV(product), CV(year) -1])
ORDER BY country, product, year;
COUNTRY PRODUCT YEAR SALES P_YEAR GROWTH_PCT
---------- --------- ----- ------- ------- ----------
Australia Bounce 1999 1878
Australia Bounce 2000 3151 1878 67.83
Australia Bounce 2001 3761 3151 19.33
Australia Y Box 1999 13773
Australia Y Box 2000 27007 13773 96.09
Australia Y Box 2001 59291 27007 119.54

31. SELECT country, product, year, sales, p_year, growth_pct
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales, 0 p_year, 0 growth_pct)
RULES (
p_year[product IN ('Bounce','Y Box'), year BETWEEN 1998 and 2001]
= sales[CV(product), CV(year) -1]
,growth_pct[product IN ('Bounce','Y Box'), year BETWEEN 1998 and
2001]
= 100* (sales[CV(product), CV(year)] -
sales[CV(product), CV(year) -1] ) /
sales[CV(product), CV(year) -1])
ORDER BY country, product, year;
COUNTRY PRODUCT YEAR SALES P_YEAR GROWTH_PCT
---------- --------- ----- ------- ------- ----------
Australia Bounce 1999 1878
Australia Bounce 2000 3151 1878 67.83
Australia Bounce 2001 3761 3151 19.33
Australia Y Box 1999 13773
Australia Y Box 2000 27007 13773 96.09
Australia Y Box 2001 59291 27007 119.54

32. SELECT country, product, year, sales, p_year, growth_pct
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales, 0 p_year, 0 growth_pct)
RULES (
p_year[product IN ('Bounce','Y Box'), ANY]
= sales[CV(product), CV(year) -1]
,growth_pct[product IN ('Bounce','Y Box'), year IS ANY]
= 100* (sales[CV(product), CV(year)] -
sales[CV(product), CV(year) -1] ) /
sales[CV(product), CV(year) -1])
ORDER BY country, product, year;
COUNTRY PRODUCT YEAR SALES P_YEAR GROWTH_PCT
---------- --------- ----- ------- ------- ----------
Australia Bounce 1999 1878
Australia Bounce 2000 3151 1878 67.83
Australia Bounce 2001 3761 3151 19.33
Australia Y Box 1999 13773
Australia Y Box 2000 27007 13773 96.09
Australia Y Box 2001 59291 27007 119.54

33. Analytics vs Model
LAG(sales,1) OVER
(PARTITION BY prod
ORDER BY year) p_year
p_year[prod IS ANY, year IS ANY]
= sales[CV(prod), CV(year)-1]
SAME!
Or is it…?

34. Model method:
COUNTRY PRODUCT YEAR SALES P_YEAR GROWTH_PCT
---------- --------- ----- ------------ ------- ----------
Australia Bounce 1999 1,878
Australia Bounce 2000 3,151 1878 67.83
Australia Bounce 2001 3,761 3151 19.33
Australia Bounce 2003 8,790
Analytic method:
COUNTRY PRODUCT YEAR SALES P_YEAR GROWTH_PCT
---------- --------- ----- ------------ ------- ----------
Australia Bounce 1999 1,878
Australia Bounce 2000 3,151 1878 67.83
Australia Bounce 2001 3,761 3151 19.33
Australia Bounce 2003 8,790 3761 133.73

35. Rule Repetition
SELECT product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
DIMENSION BY (product, year)
MEASURES (sales)
RULES UPSERT (
sales['Mouse Pad', 2008] = 1.3 * sales['Mouse Pad', 2001]
,sales['Bounce', 2008] = 1.3 * sales['Bounce', 2001]
,sales['Y Box', 2008] = 1.3 * sales['Y Box', 2001]
);
PRODUCT YEAR SALES
---------- ----- -------
Y Box 2008 77078
Bounce 2008 4888
Mouse Pad 2008 4358
3 rows selected.

36. Symbolic Reference
SELECT product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
DIMENSION BY (product, year)
MEASURES (sales)
RULES UPSERT (
sales[product IN ('Mouse Pad', 'Bounce', 'Y Box')
,2008] = 1.3 * sales[CV(product), 2001] );
no rows selected

37. Positional Reference
SELECT product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
DIMENSION BY (product, year)
MEASURES (sales)
RULES UPSERT (
sales[FOR product IN ('Mouse Pad', 'Bounce', 'Y Box')
,2008] = 1.3 * sales[CV(product), 2001] );
PRODUCT YEAR SALES
---------- ----- -------
Y Box 2008 77078
Bounce 2008 4888
Mouse Pad 2008 4358
3 rows selected.

38. Constants, Cell References,
Queries, et al…
RULES UPSERT ( -- Constants
sales[FOR product IN ('Mouse Pad', 'Bounce', 'Y Box')
,2002] = 1.3 * sales[CV(product), CV(year)-1] );
RULES UPSERT ( -- Cell References
sales[FOR (product, year) IN (
('Mouse Pad', 2002), ('Bounce',2002), ('Y Box',2002))]
= 1.3 * sales[CV(product), CV(year)-1] );
RULES UPSERT ( -- Queries
sales[FOR product IN (SELECT product FROM my_products)
,FOR year IN (2002)]
= 1.3 * sales[CV(product), CV(year)-1] );
PRODUCT YEAR SALES
---------- ----- -------
Y Box 2002 77078
Bounce 2002 4888
Mouse Pad 2002 4358

39. et allia – en trappe!
RULES UPSERT SEQUENTIAL ORDER (
sales['Bounce',
FOR year FROM 2004 TO 2001 DECREMENT 1]
= 1.3 * sales[CV(), CV()-1] )
PRODUCT YEAR SALES
-------- ----- -------
Bounce 2001 4097
Bounce 2002 4889
Bounce 2003
Bounce 2004
4 rows selected.

40. Ordering FOR loops
RULES UPSERT AUTOMATIC ORDER (
sales['Bounce',
FOR year FROM 2004 TO 2001 DECREMENT 1]
= 1.3 * sales[CV(), CV()-1] )
PRODUCT YEAR SALES
-------- ----- -------
Bounce 2001 4097
Bounce 2002 5326
Bounce 2003 6924
Bounce 2004 9001
4 rows selected.

41. ORDER BY in rules
RULES SEQUENTIAL ORDER (sales[ANY, ANY] = sales[cv(),cv()-1] )
ORA-32637: Self cyclic rule in sequential order MODEL
RULES (sales[ANY, ANY] ORDER BY YEAR ASC = sales[cv(),cv()-1])
PRODUCT YEAR SALES NEW_SALES
--------- ----- ------------ ----------
Bounce 1999 1,878 0
Bounce 2000 3,151 0
Bounce 2001 3,761 0
RULES (sales[ANY, ANY] ORDER BY YEAR DESC = sales[cv(),cv()-
1])
PRODUCT YEAR SALES NEW_SALES
--------- ----- ------------ ----------
Bounce 1999 1,878 0
Bounce 2000 3,151 1877.67
Bounce 2001 3,761 3151.35

42. NULL Measures
• Cells that exist in array but are NULL
• Cells not in the array at all
(treated as NULL)
<prior clauses of SELECT
statements>
MODEL [main] [RETURN {ALL|
UPDATED} ROWS]
[reference models]
[PARTITION BY (<cols>)]
DIMENSION BY (<cols>)
MEASURES (<cols>)
[IGNORE NAV] | [KEEP NAV]
[RULES...

43. KEEP NAV
SELECT product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
DIMENSION BY (product, year)
MEASURES (sales)
KEEP NAV
RULES UPSERT
(sales['Widget', 2003] = sales['Bounce', 2002]
+ sales['Bounce', 2001]
,sales['Widget', 2002] = sales['Bounce', 2002]
,sales['Widget', 2001] = sales['Bounce', 2001]);
PRODUCT YEAR SALES
-------- ----- ----------
Widget 2001 3760.51
Widget 2002
Widget 2003
3 rows selected.

44. IGNORE NAV
SELECT product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
DIMENSION BY (product, year)
MEASURES (sales)
IGNORE NAV
RULES UPSERT
(sales['Widget', 2003] = sales['Bounce', 2002]
+ sales['Bounce', 2001]
,sales['Widget', 2002] = sales['Bounce', 2002]
,sales['Widget', 2001] = sales['Bounce', 2001]);
PRODUCT YEAR SALES
-------- ----- ----------
Widget 2001 3760.51
Widget 2002 0
Widget 2003 3760.51
3 rows selected.

45. NULL Defaults
• 0 for numeric data
• Empty string '' for character/string data
• '01-JAN-2001' for date type data
• NULL for all other data types

46. NULL Cell Reference
• Positional
– sales [ANY]
– sales [NULL]
• Symbolic
– sales [product IS ANY]
– sales [product IS NULL]
– sales [product = NULL] -- FALSE

47. PRESENTV, PRESENTNNV
SELECT product, year, sales
FROM sales_view
WHERE country = 'Australia'
MODEL RETURN UPDATED ROWS
DIMENSION BY (product, year)
MEASURES (sales)
--IGNORE NAV -- Makes no difference
RULES UPSERT
(sales['Widget', 2003] = sales['Bounce', 2002]
+ sales['Bounce', 2001]
,sales['Widget', 2002] = PRESENTV(sales['Bounce', 2002],1,0)
,sales['Widget', 2001] = PRESENTNNV(sales['Bounce', 2001],1,0));
PRODUCT YEAR SALES
-------- ----- ----------
Widget 2001 1
Widget 2002 0
Widget 2003 3760.51
3 rows selected.

48. Reference Models
scott@sw10g> select * from exch_rates;
COUNTRY EXCHANGE_RATE
---------------- -------------
Poland .25
France .14
Australia .93
New Zealand 1.12
United Kingdom .45
5 rows selected.
…
COUNTRY YEAR SALES DOLLAR_SALES
---------------- ----- ------------ ------------
Australia 2001 1,164,871 1,083,330
France 2001 1,025,157 143,522
United Kingdom 2001 1,674,752 753,638

49. Reference Models
<prior clauses of SELECT statements>
MODEL [main] [RETURN {ALL|UPDATED} ROWS]
[reference models]
[PARTITION BY (<cols>)]
DIMENSION BY (<cols>)
MEASURES (<cols>)
[IGNORE NAV] | [KEEP NAV]
[RULES
[UPSERT | UPDATE]
[AUTOMATIC ORDER | SEQUENTIAL ORDER]
[ITERATE (n) [UNTIL <condition>] ]
( <cell_assignment> = <expression> ... )

50. Reference Models
SELECT country, year,
ROUND(sales) sales, ROUND(dollar_sales) dollar_sales
FROM sales_view
GROUP BY country, year
MODEL RETURN UPDATED ROWS
REFERENCE conv_ref ON
(SELECT country, exchange_rate FROM exch_rates)
DIMENSION BY (country) MEASURES (exchange_rate)
MAIN conversion
DIMENSION BY (country, year)
MEASURES (SUM(sales) sales, SUM(sales) dollar_sales)
RULES
(dollar_sales[FOR conversion.country IN
('France','Australia','United Kingdom'), 2001]
= sales[CV(country), CV(year)] * 1.0 *
conv_ref.exchange_rate[CV(country)]);
COUNTRY YEAR SALES DOLLAR_SALES
---------------- ----- ------------ ------------
Australia 2001 1,164,871 1,083,330
France 2001 1,025,157 143,522
United Kingdom 2001 1,674,752 753,638
3 rows selected.

51. Reference Models
SELECT country, year,
ROUND(sales) sales, ROUND(dollar_sales) dollar_sales
FROM sales_view
GROUP BY country, year
MODEL RETURN UPDATED ROWS
REFERENCE conv_ref ONREFERENCE conv_ref ON
(SELECT country, exchange_rate FROM exch_rates)(SELECT country, exchange_rate FROM exch_rates)
DIMENSION BY (country) MEASURES (exchange_rate)DIMENSION BY (country) MEASURES (exchange_rate)
MAINMAIN conversionconversion
DIMENSION BY (country, year)
MEASURES (SUM(sales) sales, SUM(sales) dollar_sales)
RULES
(dollar_sales[FOR conversion.country IN
('France','Australia','United Kingdom'), 2001]
= sales[CV(country), CV(year)] * 1.0 *
conv_ref.exchange_rate[CV(country)]conv_ref.exchange_rate[CV(country)]);
COUNTRY YEAR SALES DOLLAR_SALES
---------------- ----- ------------ ------------
Australia 2001 1,164,871 1,083,330
France 2001 1,025,157 143,522
United Kingdom 2001 1,674,752 753,638
3 rows selected.

52. Reference Models
SELECT country, year,SELECT country, year,
ROUND(sales) sales, ROUND(dollar_sales) dollar_salesROUND(sales) sales, ROUND(dollar_sales) dollar_sales
FROM sales_viewFROM sales_view
GROUP BY country, yearGROUP BY country, year
MODEL RETURN UPDATED ROWSMODEL RETURN UPDATED ROWS
REFERENCE conv_ref ON
(SELECT country, exchange_rate FROM exch_rates)
DIMENSION BY (country) MEASURES (exchange_rate)
MAIN conversion
DIMENSION BY (country, year)DIMENSION BY (country, year)
MEASURES (SUM(sales) sales, SUM(sales) dollar_sales)MEASURES (SUM(sales) sales, SUM(sales) dollar_sales)
RULESRULES
(dollar_sales[FOR conversion.country IN
('France','Australia','United Kingdom'), 2001]
= sales[CV(country), CV(year)] * 1.0 *
conv_ref.exchange_rate[CV(country)]);
COUNTRY YEAR SALES DOLLAR_SALES
---------------- ----- ------------ ------------
Australia 2001 1,164,871 1,083,330
France 2001 1,025,157 143,522
United Kingdom 2001 1,674,752 753,638
3 rows selected.
Australia
0.93

53. Eh?
SELECT v.country, v.year
,ROUND(SUM(sales)) sales
,ROUND(SUM(v.sales * r.exchange_rate)) dollar_sales
FROM sales_view v, exch_rates r
WHERE v.country IN
('France', 'Australia', 'United Kingdom')
AND v.year = 2001
AND v.country = r.country
GROUP BY v.country, v.year
ORDER BY v.country;
COUNTRY YEAR SALES DOLLAR_SALES
---------------- ----- ------------ ------------
Australia 2001 1,164,871 1,083,330
France 2001 1,025,157 143,522
United Kingdom 2001 1,674,752 753,638
3 rows selected.

54. Amortization
SELECT c, p, m, pp, ip
FROM mortgage
MODEL
REFERENCE R ON
(SELECT customer, fact, amt
FROM mortgage_facts
MODEL DIMENSION BY (customer, fact)
MEASURES (amount amt) RULES
(amt[any, 'PaymentAmt']= (amt[CV(),'Loan']*
Power(1+ (amt[CV(),'Annual_Interest']/100/12),amt[CV(),'Payments'])*
(amt[CV(),'Annual_Interest']/100/12)) /
(Power(1+(amt[CV(),'Annual_Interest']/100/12), amt[CV(),'Payments'])
- 1)))
DIMENSION BY (customer cust, fact)
MEASURES (amt)
MAIN amortization
PARTITION BY (customer c)
DIMENSION BY (0 p)
MEASURES (principalp pp, interestp ip, mort_balance m, customer mc)
RULES ITERATE(1000) UNTIL (ITERATION_NUMBER+1 =r.amt[mc[0],'Payments'])
(ip[ITERATION_NUMBER+1] = m[CV()-1] * r.amt[mc[0],
'Annual_Interest']/1200
,pp[ITERATION_NUMBER+1] = r.amt[mc[0], 'PaymentAmt'] - ip[CV()]
,m[ITERATION_NUMBER+1] = m[CV()-1] - pp[CV()])
ORDER BY c, p;

55. SELECT country, year,
ROUND(sales) sales, ROUND(dollar_sales)
dollar_sales
FROM sales_view
GROUP BY country, year
MODEL RETURN UPDATED ROWS
REFERENCE conv_ref ON
(SELECT country, exchange_rate FROM exch_rates)
DIMENSION BY (country) MEASURES (exchange_rate)
IGNORE NAV
MAIN conversion
DIMENSION BY (country, year)
MEASURES (SUM(sales) sales, SUM(sales)
dollar_sales) IGNORE NAV
RULES
(conv_ref.country['Australia'] = 0.97);
ERROR at line 1:
ORA-03113: end-of-file on communication channel
Reference models are read only

56. SELECT country, year, SUM(sales) sales
FROM sales_view
GROUP BY country, year
MODEL RETURN UPDATED ROWS
MAIN conversion
DIMENSION BY (country, year)
MEASURES (SUM(sales) sales) IGNORE NAV
RULES
(sales[FOR country IN
('France','Australia','United Kingdom'),
2002]
= sales[CV(country), CV()-1] * 1.2);
ERROR at line 1:
ORA-00934: group function is not allowed here
Aggregate can’t be in SELECT/ORDER BY

57. SELECT *
FROM sales_view
WHERE country = 'Australia'
MODEL DIMENSION BY (product, year)
MEASURES (sales)
RULES UPSERT
(sales['Bounce', 2003] =
sales[CV(), CV()-1] +
(SELECT SUM(sales) FROM sales_view));
ERROR at line 1:
ORA-32620: illegal subquery within MODEL rules
MEASURES (sales,
(SELECT SUM(sales) FROM sales_view) AS grand_total)
RULES UPSERT
(sales['Bounce', 2003] = sales[CV(), CV()-1]
+ grand_total[CV(), CV()-1]);
Sub-queries

58. RULES UPSERT
(sales[FOR product IN (
WITH kludge_with AS
(SELECT prod_name FROM sh.products)
SELECT prod_name FROM kludge_with) , 2003]
= sales[CV(), CV()-1]);
ERROR at line 1:
ORA-32632: incorrect subquery in MODEL FOR cell index
RULES UPSERT
(sales[FOR cust_id IN
(SELECT cust_id FROM sh.customers), 2003]
= sales[CV(), CV()-1]);
ERROR at line 1:
ORA-32633: MODEL subquery FOR cell index returns too
many rows
FOR Construct Limitations

59. SELECT id, n FROM all_objects
WHERE object_id=1
MODEL
DIMENSION BY (object_id id)
MEASURES (object_name n)
RULES ITERATE (:v)
(n[iteration_number] =iteration_number)
ERROR at line 5:
ORA-32607: invalid ITERATE value in MODEL clause
SELECT id, n FROM all_objects
WHERE object_id=1
MODEL
DIMENSION BY (object_id id)
MEASURES (object_name n)
RULES ITERATE (100) UNTIL :v
(n[iteration_number] =iteration_number)
ITERATE UNTIL

60. Performance
• Replaces multiple joins/unions
• Scalable in size & parallelism
• Explain plan

61. Fill Dates
scott@sw10g> select * from customer;
NAME AMT DT
------ ---------- -----------
Scott 117 17-jan-2008
Scott 250 17-feb-2008
Scott 300 17-apr-2008
Scott 50 17-jun-2008
Wade 1231 17-mar-2008
Wade 2321 17-apr-2008
Wade 3122 17-sep-2008
Wade 59 17-oct-2008
8 rows selected.

62. Fill Dates
NAME MTH AMT CUM_AMT
------ --------- ---------- ----------
Scott January 117 117
Scott February 250 367
Scott March 0 367
Scott April 300 667
Scott May 0 667
Scott June 50 717
Scott July 0 717
Scott August 0 717
Scott September 0 717
Scott October 0 717
Scott November 0 717
Scott December 0 717
Wade January 0 0
Wade February 0 0
Wade March 1231 1231
Wade April 2321 3552
Wade May 0 3552
Wade June 0 3552
Wade July 0 3552
Wade August 0 3552
Wade September 3122 6674
Wade October 59 6733
Wade November 0 6733
Wade December 0 6733
24 rows selected.
NAME AMT DT
------ ---------- -----------
Scott 117 17-jan-2007
Scott 250 17-feb-2007
Scott 300 17-apr-2007
Scott 50 17-jun-2007
Wade 1231 17-mar-2007
Wade 2321 17-apr-2007
Wade 3122 17-sep-2007
Wade 59 17-oct-2007
8 rows selected.

63. Analytic Solution
SELECT date_fill.name, TO_CHAR(real_dt,'Month') mth, NVL(amt,0) amt
,NVL(SUM(amt) OVER (PARTITION BY date_fill.name
ORDER BY real_dt ),0) cum_amt
FROM (SELECT name, TRUNC(dt,'mm') dt, SUM(amt) amt
FROM customer
GROUP BY name, TRUNC(dt,'mm')
) actual_data,
(SELECT name, real_dt
FROM (SELECT DISTINCT name
FROM customer)
,(WITH mths AS (SELECT TRUNC(SYSDATE,'YYYY') real_dt
FROM DUAL CONNECT BY LEVEL <= 12)
SELECT ADD_MONTHS(real_dt,ROWNUM-1) real_dt FROM mths)
) date_fill
WHERE date_fill.real_dt = actual_data.dt(+)
AND date_fill.name = actual_data.name(+)
ORDER BY date_fill.name, date_fill.real_dt
/
Actual Data
Distinct list joined with conjured dates
Outer join actual data
with full date list

64. Analytic ExplainExecution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=13 Card=8 Bytes=31
1 0 SORT (ORDER BY) (Cost=13 Card=8 Bytes=312)
2 1 WINDOW (SORT) (Cost=13 Card=8 Bytes=312)
3 2 HASH JOIN (OUTER) (Cost=11 Card=8 Bytes=312)
4 3 VIEW (Cost=6 Card=8 Bytes=104)
5 4 MERGE JOIN (CARTESIAN) (Cost=6 Card=8 Bytes=104)
6 5 VIEW (Cost=2 Card=1 Bytes=6)
7 6 COUNT
8 7 VIEW (Cost=2 Card=1 Bytes=6)
9 8 CONNECT BY (WITHOUT FILTERING)
10 9 FAST DUAL (Cost=2 Card=1)
11 5 BUFFER (SORT) (Cost=6 Card=8 Bytes=56)
12 11 VIEW (Cost=4 Card=8 Bytes=56)
13 12 SORT (UNIQUE) (Cost=4 Card=8 Bytes=56)
14 13 TABLE ACCESS (FULL) OF 'CUSTOMER' (TABLE)
15 3 VIEW (Cost=4 Card=8 Bytes=208)
16 15 SORT (GROUP BY) (Cost=4 Card=8 Bytes=232)
17 16 TABLE ACCESS (FULL) OF 'CUSTOMER' (TABLE) (Cost=
Statistics
----------------------------------------------------
9 recursive calls
30 consistent gets
6 sorts (memory)

65. Model Solution
SELECT name, TO_CHAR(dt,'DD-MM-YYYY') dt, amt, cum_amt -- Model results
FROM (
SELECT name, TRUNC(dt, 'MM') dt, SUM(amt) amt
FROM customer
GROUP BY name, TRUNC(dt, 'MM')
)
MODEL
PARTITION BY (name)
DIMENSION BY (dt)
MEASURES (amt, cast(NULL AS NUMBER) cum_amt) -- Define calculated col
IGNORE NAV
RULES SEQUENTIAL ORDER(
amt[FOR dt FROM TO_DATE('01-01-2007', 'DD-MM-YYYY')
TO TO_DATE('01-12-2007', 'DD-MM-YYYY')
INCREMENT NUMTOYMINTERVAL(1, 'MONTH')
] = amt[CV(dt)] -- Apply amt for given date, if found
,cum_amt[ANY] = SUM(amt)[dt <= CV(dt)] -- Calculate cumulative
)
ORDER BY name, dt
/
Essentially
apply model to
this data set
Conjure dates

66. Model Explain
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=5 Card=8 Bytes=232
1 0 SORT (ORDER BY) (Cost=5 Card=8 Bytes=232)
2 1 SQL MODEL (ORDERED) (Cost=5 Card=8 Bytes=232)
3 2 SORT (GROUP BY) (Cost=5 Card=8 Bytes=232)
4 3 TABLE ACCESS (FULL) OF 'CUSTOMER' (TABLE) (Cost=3 Ca
5 2 WINDOW (IN SQL MODEL) (SORT)
Statistics
----------------------------------------------------
5 recursive calls
15 consistent gets
4 sorts (memory)
…and no joins

67. Also noteworthy
cum_amt[any] = sum(amt)[dt <= cv(dt)] -- Sum dates before iteration date
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=5 Card=8 Bytes=232
1 0 SORT (ORDER BY) (Cost=5 Card=8 Bytes=232)
2 1 SQL MODEL (ORDERED) (Cost=5 Card=8 Bytes=232)
3 2 SORT (GROUP BY) (Cost=5 Card=8 Bytes=232)
4 3 TABLE ACCESS (FULL) OF 'CUSTOMER' (TABLE) (Cost=3 Ca
5 2 WINDOW (IN SQL MODEL) (SORT)
cum_amt[any] = sum(amt)[any] -- Aggregate all dates, no window sort

68. Performance
• Replaces multiple joins/unions
• Scalable in size & parallelism
• Explain plan

69. 216
+ 1
SELECT COUNT(*) FROM (
SELECT * FROM sales_mth_view
MODEL RETURN ALL ROWS
PARTITION BY (country)
DIMENSION BY (product, year, month)
MEASURES (sales)
RULES UPSERT SEQUENTIAL ORDER
(sales[ANY,ANY,ANY] = sales[CV(), CV(), CV()]*1.2
-- Plus forecast sales...
,sales[FOR product IN (SELECT prod_name FROM sh.products)
,FOR year FROM 2008 TO 2015 INCREMENT 1
,FOR month FROM 1 TO 12 INCREMENT 1
] = sales[CV(), CV()-8, CV()]*1.5)
)
sw10g> /
COUNT(*)
----------
156157
1 row selected.
Try that
in Excel…

70. Performance
• Replaces multiple joins/unions
• Scalable in size & parallelism
• Explain plan

71. Sequential Automatic
Cyclic
yn
Ordered
Fast Acyclic Cyclic
Fast
Order

72. Sequential Automatic
Cyclic
yn
Ordered
Fast Acyclic Cyclic
Fast
Order
SELECT country, product, year, sales
FROM sales_view
WHERE country IN ('Australia','Japan')
MODEL UNIQUE DIMENSION
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
RULES UPSERT SEQUENTIAL ORDER
(sales['Bounce',2003] = AVG(sales)[ANY,2002] * 1.5
,sales['Y Box', 2003] = sales['Bounce',2003] * .25);
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=ALL_ROWS
1 0 SQL MODEL (ORDERED FAST)
2 1 SORT (GROUP BY)
3 2 HASH JOIN
4 3 TABLE ACCESS (FULL) OF 'TIMES' (TABL
5 3 HASH JOIN
6 5 TABLE ACCESS (FULL) OF 'PRODUCTS'
7 5 HASH JOIN
8 7 HASH JOIN
9 8 TABLE ACCESS (FULL) OF 'COUNTR
10 8 TABLE ACCESS (FULL) OF 'CUSTOM
11 7 PARTITION RANGE (ALL)
12 11 TABLE ACCESS (FULL) OF 'SALES'

73. Cyclic
y
Cyclic
Order
SELECT country, product, year, sales
FROM sales_view
WHERE country in ('Australia','Japan')
MODEL UNIQUE DIMENSION
PARTITION BY (country)
DIMENSION BY (product, year)
MEASURES (sales)
IGNORE NAV RULES UPSERT AUTOMATIC ORDER
(sales['Y Box',2003] = 0.25 * sales['Bounce', 2003]
,sales['Bounce',2003] = sales['Y Box',2003]
+ sales['Bounce',2002]);
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=ALL_ROWS
1 0 SQL MODEL (CYCLIC)
2 1 SORT (GROUP BY)
3 2 HASH JOIN
4 3 TABLE ACCESS (FULL) OF 'TIMES' (TABLE)
5 3 HASH JOIN
6 5 TABLE ACCESS (FULL) OF 'PRODUCTS' (TABL
7 5 HASH JOIN
8 7 HASH JOIN
9 8 TABLE ACCESS (FULL) OF 'COUNTRIES'
...
Automatic

74. Why?
When?
1. New language?
2. Array/Sets
3. Recursion
4. Flexibility & Power
5. Can it be done in SQL?

75. sales[cv()-1]
• DECODE
• Analytics
• Model
– CV()
– Reference Model

76. DECODE
SELECT country
,SUM(DECODE(year,2007,sales,NULL)) current_year
,SUM(DECODE(year,2006,sales,NULL)) previous_year
,SUM(DECODE(year,2005,sales,NULL)) before_that
FROM sales_view
WHERE year BETWEEN 2005 AND 2007
GROUP BY country;
COUNTRY CURRENT_YEAR PREVIOUS_YEAR BEFORE_THAT
-------- ------------ ------------- -----------
Greece 27540 25067 20835
Italy 23738 20831 22867

77. DECODE
SELECT country, year
,SUM(DECODE(year,2007,sales,NULL)) current_year
,SUM(DECODE(year,2006,sales,NULL)) previous_year
,SUM(DECODE(year,2005,sales,NULL)) before_that
FROM sales_view
WHERE year BETWEEN 2005 AND 2007
GROUP BY country, year;
COUNTRY YEAR CURRENT_YEAR PREVIOUS_YEAR BEFORE_THAT
-------- ----- ------------ ------------- -----------
Italy 2005 22867
Italy 2006 20831
Italy 2007 23738
Greece 2005 20835
Greece 2006 25067
Greece 2007 27540

78. Analytics
SELECT country, year, current_year, previous_year, before_that
FROM (
SELECT year, country, tot_sales current_year
,LAG(tot_sales) OVER
(PARTITION BY country ORDER BY year) previous_year
,LAG(tot_sales,2) OVER
(PARTITION BY country ORDER BY year) before_that
FROM (
SELECT country
,year
,SUM(sales) tot_sales
FROM sales_view
WHERE year BETWEEN 2005 AND 2007
GROUP BY year, country)
) WHERE year = 2007;
COUNTRY YEAR CURRENT_YEAR PREVIOUS_YEAR BEFORE_THAT
---------- ----- ------------ ------------- -----------
Greece 2007 27540 25067 20835
Italy 2007 23738 20831 22867

79. Analytics
SELECT country, year, current_year, previous_year, before_that
FROM (
SELECT year, country, tot_sales current_year
,LAG(tot_sales) OVER
(PARTITION BY country ORDER BY year) previous_year
,LAG(tot_sales,2) OVER
(PARTITION BY country ORDER BY year) before_that
FROM (
SELECT country
,year
,SUM(sales) tot_sales
FROM sales_view
WHERE year BETWEEN 2005 AND 2007
GROUP BY year, country)
) WHERE year >= 2005;
COUNTRY YEAR CURRENT_YEAR PREVIOUS_YEAR BEFORE_THAT
---------- ----- ------------ ------------- -----------
Greece 2005 20835
Greece 2006 25067 20835
Greece 2007 27540 25067 20835
Italy 2005 22867
Italy 2006 20831 22867
Italy 2007 23738 20831 22867

80. Model – CV()
SELECT country, year, sales, previous_year, before_that
FROM sales_view
GROUP BY year, country
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (year)
MEASURES (SUM(sales) AS sales
,CAST(NULL AS NUMBER) previous_year
,CAST(NULL AS NUMBER) before_that)
RULES (
previous_year[2007] = sales[CV()-1]
,before_that [2007] = sales[CV()-2]
);
COUNTRY YEAR SALES PREVIOUS_YEAR BEFORE_THAT
-------- ----- ------------ ------------- -----------
Greece 2007 27,540 25067 20835
Italy 2007 23,738 20831 22867

81. Model – CV()
SELECT country, year, sales, previous_year, before_that
FROM sales_view
GROUP BY year, country
MODEL RETURN UPDATED ROWS
PARTITION BY (country)
DIMENSION BY (year)
MEASURES (SUM(sales) AS sales
,CAST(NULL AS NUMBER) previous_year
,CAST(NULL AS NUMBER) before_that)
RULES (
previous_year[ANY] = sales[CV()-1]
,before_that [ANY] = sales[CV()-2]
);
COUNTRY YEAR SALES PREVIOUS_YEAR BEFORE_THAT
-------- ----- ------------ ------------- -----------
Greece 2005 20,835
Greece 2006 25,067 20835
Greece 2007 27,540 25067 20835
Italy 2005 22,867
Italy 2006 20,831 22867
Italy 2007 23,738 20831 22867

82. Model - Reference
SELECT country, year, sales, prev, bef
FROM sales_view
GROUP BY country, year
MODEL
REFERENCE r ON
(WITH years AS (SELECT TO_CHAR(SYSDATE,'YYYY')-LEVEL+1 y
FROM DUAL CONNECT BY LEVEL <=10)
SELECT y, y-1 prev, y-2 bef FROM years)
DIMENSION BY (y)
MEASURES (prev, bef)
MAIN calc
PARTITION BY (country)
DIMENSION BY (year)
MEASURES (SUM(sales) AS sales
,CAST(NULL AS NUMBER) prev, 0 bef)
RULES (
prev[ANY] = sales[r.prev[CV(year)]]
,bef [ANY] = sales[r.bef [CV(year)]]
);

83. Give it time, it will grow on you…