1. Oracle8i Analytical SQL Features

2. Analytical SQL Features Overview
Available in Oracle and above
Analytical Features/Enhancements
GROUP BY Extensions
SQL GROUP BY clause has been augmented to make querying and reporting easier
Analytical SQL Functions
Analytical functions enabling rankings, moving window calculations, lead/lag analysis
CASE Expressions
Increased/Efficient ‘if-then-else’ capabilities provided
Willie Albino
11/28/2018
Willie Albino

3. Oracle8i Analytical SQL Features
GROUP BY
Extensions

4. GROUP BY Extensions ROLLUP CUBE
Calculates subtotals at increasing levels of aggregation from the most detail to a grand total
used to generate simple cross tabular reports
CUBE
Calculates all possible combinations of subtotals
used to generate full cross tabular reports
Willie Albino
11/28/2018

5. GROUP BY Extensions Syntax
ROLLUP:
SELECT <column list>
FROM <table…>
GROUP BY ROLLUP(column_list);
CUBE:
GROUP BY CUBE(column_list);
Willie Albino
11/28/2018
Willie Albino

6. Standard GROUP BY Example
select region, product,
SUM(amount)
from region, product,
product_sales
where region_id = reg_id
and region = 'East'
and product_id = prod_id
group by region,product
REGION PRODUCT SUM(AMOUNT)
East Hats
East Jackets
East Pants
East Shirts
East Shoes
East Suits
East Sweaters
East T-Shirts
East Ties
Willie Albino
11/28/2018

7. GROUP BY with ROLLUP select region, product, SUM(amount)
from region, product,
product_sales
where region_id = reg_id
and region = 'East'
and product_id = prod_id
group by
ROLLUP(region,product)
REGION PRODUCT SUM(AMOUNT)
East Hats
East Jackets
East Pants
East Shirts
East Shoes
East Suits
East Sweaters
East T-Shirts
East Ties
East
Willie Albino
11/28/2018
Willie Albino

8. GROUP BY with CUBE select region, product, SUM(amount)
from region, product,
product_sales
where region_id = reg_id
and region = 'East'
and product_id = prod_id
group by
CUBE(region,product)
REGION PRODUCT SUM(AMOUNT)
: : :
East Ties
East
Hats
Jackets
Pants
Shirts
Shoes
Suits
Sweaters
T-Shirts
Ties
Willie Albino
11/28/2018

9. Comments about ROLLUP/CUBE
ROLLUP: creates subtotals at n+1 levels where n equals the number of grouping columns
CUBE: creates 2n combinations of subtotals where n equals the number of grouping columns
Sub-total generation more efficient than equivalent SQL code (a 4 column CUBE grouping, has a 93.75% reduction in table access, ROLLUP has 80%)
Willie Albino
11/28/2018

10. Comments about ROLLUP/CUBE
Partial rollups/cubes can be specified
GROUP BY exp1, CUBE(exp2, exp3, ....)
ROLLUP/CUBE can be used with all aggregating functions (MAX, MIN, AVG, etc.)
HAVING clause applies to all the data returned
NULLs are generated for dimensions at subtotal levels
Willie Albino
11/28/2018

11. GROUPING() Function
Used to distinguish between NULLs in data and NULLs generated by ROLLUP/CUBE extensions
GROUPING() return values:
1 for extension-generated NULLs, 0 for NULL data values
Can be passed to DECODE for custom interpretation
SYNTAX:
SELECT .. GROUPING(column name) .. GROUP BY ..
SELECT .. DECODE(GROUPING(col), 1, ‘Sub’, col)) …
Willie Albino
11/28/2018

12. GROUPING() Function Example
select
DECODE(GROUPING(region),
1, 'All Regions',
0, region) region,
DECODE(GROUPING(product),
1, 'All Products',
0, product) product,
SUM(amount)
from region, product,
product_sales
where region_id = reg_id
and region = 'East'
and product_id = prod_id
group by
CUBE(region,product)
REGION PRODUCT SUM(AMOUNT)
: : :
East Ties
East All Products
All Regions Hats
All Regions Jackets
All Regions Pants
All Regions Shirts
All Regions Shoes
All Regions Suits
All Regions Sweaters
All Regions T-Shirts
All Regions Ties
All Regions All Products
Willie Albino
11/28/2018

13. Oracle8i Analytical SQL Features
Functions

14. Analytical SQL Functions
Analytical Function Categories:
Ranking Functions
Windowing Functions
Reporting Functions
Lag/Lead Functions
Statistics Functions
Functions are applied after all joins, WHERE, GROUP BY and HAVING clauses are performed, but before the ORDER BY clause is applied
Willie Albino
11/28/2018

15. Basic Analytical Function Syntax
<function_name>() OVER (
[PARTITION BY <exp1> [, …]]
ORDER BY <exp2> [ASC|DESC]
[NULLS FIRST|NULLS LAST] )
Example:
SELECT RANK(amount) OVER
(PARTITION BY region ORDER BY amount)
FROM REG_SALES;
Willie Albino
11/28/2018

16. Function Syntax Comments
PARTITION BY <exp> [, …] - this clause divides the query result into groups within which the analytical function operates
If the PARTITION BY clause is missing, the function operates over the entire dataset
<exp> can be any valid expression involving column references
Willie Albino
11/28/2018

17. Function Syntax Comments
ORDER BY <exp>[ASC|DESC] [NULLS FIRST|NULLS LAST]
specifies how the data is ordered within a group (partition)
ASC|DESC specifies the sorting order for the grouping. The default sorting order is ASC.
The presence of ORDER BY affects the outcome of analytical functions
With ORDER BY, the set of rows used is the current row and all preceding rows in the partition (a growing window)
Without ORDER BY, all the rows in the partition will be used
The ORDER BY clause can be used to resolve ties between repeated values in a set.
Willie Albino
11/28/2018

18. Function Syntax Comments
ORDER BY <exp>[ASC|DESC] [NULLS FIRST|NULLS LAST]
The NULLS FIRST|NULLS LAST clause determines the position of NULLs in the ordered sequence. If omitted, the position depends on the ASC, DESC arguments.
NULLs are considered to be larger than any other values.
It is not guaranteed that the data will be sorted on the measures. Use the ORDER BY clause to specify the ordering sequence.
Willie Albino
11/28/2018

19. Ranking Functions
Computes the rank of a record with respect to other records in the dataset based on the values of a set of measures
Ranking Functions:
RANK() and DENSE_RANK()
CUME_DIST() and PERCENT_RANK()
NTILE()
ROW_NUMBER()
Willie Albino
11/28/2018

20. RANK() and DENSE_RANK() Functions
The RANK() and DENSE_RANK() functions allow you to rank items in a dataset or sub-group.
The RANK() function leaves gaps in the ranking sequence when there are ties in the rankings.
The DENSE_RANK() function does not leave gaps in the ranking sequence when there are ties in the rankings.
Willie Albino
11/28/2018

21. Example of RANK()/DENSE_RANK()
select amount,
RANK() OVER
(ORDER BY amount)
AS rank_asc,
DENSE_RANK() OVER
AS dense_rank
from product_sales,
product, region
where prod_id=product_id
and region_id=reg_id
and region='East'
order by amount;
AMOUNT RANK_ASC DENSE_RANK
NOTES:
- Ranking value will repeat (leaving gaps) when the same data values are found in the dataset
- The order or the rows with repeated values is non-deterministic
- DENSE_RANK() does not leave gaps in the rank values for repeated data values (RANK() does)
- The largest rank value produced by DENSE_RANK equals the number of distinct values in the dataset
Willie Albino
11/28/2018

22. Using RANK() For Top-N Values List
select * from (
select region, product,SUM(amount) amt,
SUM(profit) profit,
RANK() OVER (PARTITION BY region
ORDER BY SUM(amount) DESC) AS rank
from product_sales, product, region
where prod_id=product_id
and region_id=reg_id
GROUP BY region, product )
where rank_sum_amt < 4
Region Product Amt Profit Rank
Central Sweaters
Central Shirts
Central Pants
East Shoes
East Jackets
East Pants
West Shoes
West Jackets
West T-Shirts
NOTES:
- Using RANK() or DENSE_RANK(), you can get the top N ranks within a dataset
- DENSE_RANK() could yield different results than RANK() depending on repeated values within the dataset. (The function ROW_NUMBER() would provide more accurate results.)
- The bottom N rankings can be generated by changing the ordering sequence within the rank expression (e.g., ORDER BY SUM(amount) ASC))
Willie Albino
11/28/2018

23. CUME_DIST() and PERCENT_RANK()
CUME_DIST() computes the position of a specified value relative to the set of values (also known as inverse of percentile in statistics books)
CD = (# values different from or equal to x)/(total # of values)
Return values are between 0 and 1
PERCENT_RANK() returns the percent rank of a value relative to a group of values
PR = (rank of row in partition - 1)/(# of rows in the partition - 1)
Willie Albino
11/28/2018

24. Example of CUME_DIST() & PERCENT_RANK()
select region, product, SUM(amount),
CUME_DIST() OVER (PARTITION BY region ORDER BY SUM(amount) ASC) AS cume_dist,
PERCENT_RANK() OVER (PARTITION BY region ORDER BY SUM(amount) ASC) AS pct_rnk
from product_sales, product, region
where prod_id=product_id
and region_id=reg_id
GROUP BY region, product
Region Product Amt Cume_Dist Pct_Rnk
Central Belts
Central Suits
Central Ties
Central Hats
Central Pants
Central Shirts
Central Sweaters
East T-Shirts
East Ties
East Hats
East Shirts
East Sweaters
East Suits
East Jackets
East Pants
East Shoes
: : : : :
Willie Albino
11/28/2018

25. NTILE(n) and ROW_NUMBER()
Divides dataset into a specified number of buckets
Takes the number of buckets as an argument
ROW_NUMBER()
assigns a unique number to each row within a partition
row numbers start with 1 and increase sequentially within each partition
better than RANK() or DENSE_RANK() for top-N queries
Rows with rankings that are ties will not necessarily be assigned to the same bucket (if they span buckets) or the same row number in subsequent runs of the query using the same dataset
Willie Albino
11/28/2018

26. Example of NTILE() & ROW_NUMBER()
select region, product, SUM(amount),
NTILE(3) OVER (PARTITION BY region ORDER BY SUM(amount) ASC) AS bucket,
ROW_NUMBER() OVER (PARTITION BY region ORDER BY SUM(amount) ASC) AS row
from product_sales, product, region
where prod_id=product_id
and region_id=reg_id
GROUP BY region, product
Region Product Amt Bucket Row
Central Belts
Central Suits
Central Ties
Central Hats
Central Pants
Central Shirts
Central Sweaters
East T-Shirts
East Ties
East Hats
East Shirts
East Sweaters
East Suits
East Jackets
East Pants
East Shoes
Willie Albino
11/28/2018

27. Oracle8i Analytical SQL Features
Windowing Functions

28. Windowing Functions
Used to compute cumulative, moving or centered aggregates
Returns a value for each row in a dataset which depends on other rows in the corresponding window
Windowing functions include moving sum, moving average, moving min/max, cumulative sum and statistical functions, first and last value in window
Willie Albino
11/28/2018

29. Windowing Function Syntax
{SUM|AVG|MAX|MIN|COUNT|FIRST_VALUE|LAST_VALUE} OVER
( PARTITION BY <exp1> [, …]
ORDER BY <exp2> [ASC|DESC] [NULLS FIRST|NULLS LAST]
ROWS | RANGE
{ UNBOUNDED PRECEDING | <exp3> PRECEDING} |
BETWEEN
{UNBOUNDED PRECEDING | <exp4> PRECEDING}
AND
{CURRENT ROW | <exp5> FOLLOWING} } )
Willie Albino
11/28/2018

30. Windowing Function Syntax Comments
<exp>
Must be a constant or an expression which evaluates to a positive value
If ROWS was specified, it’s a physical offset which represents number of rows in the window
If RANGE was specified, it’s a logical offset (value or interval literal)
An interval literal is specified as follows:
RANGE INTERVAL n DAYS|MONTHS|YEARS
RANGE x PRECEDING|FOLLOWING
Willie Albino
11/28/2018

31. Windowing Function Syntax Comments
ROWS | RANGE
ROWS specifies the window in physical units
RANGE specifies the window as a logical offset
BETWEEN … AND …
Specifies the start and end point of the window
If BETWEEN is omitted and an end point is specified, that point will be considered the start point and the current row will be used as the end point
Willie Albino
11/28/2018

32. Windowing Function Syntax Comments
UNBOUNDED PRECEDING
Specifies that the window starts at the first row of the partition, (or the start of the dataset, if the PARTITION BY clause is omitted)
UNBOUNDED FOLLOWING
Specifies that the window ends at the last row of the partition, (or the last row of the dataset, if the PARTITION BY clause is omitted)
Willie Albino
11/28/2018

33. Windowing Function Syntax Comments
CURRENT_ROW
As a start point:
If ROWS was specified, makes the current row the start of the window.
If RANGE was specified, then the current value is the start of the window
As an end point:
If ROWS was specified, makes the current row the end of the window.
If RANGE was specified, then the current value is the end of the window
Willie Albino
11/28/2018

34. Windowing Function Syntax Comments
<exp> FOLLOWING
If this is the start point, then the end point must be <exp> FOLLOWING or UNBOUNDED FOLLOWING
<exp> PRECEDING
If this is the end point, then the start point must be <exp> PRECEDING or UNBOUNDED PRECEDING
This applies whether ROWS or RANGE was specified
Willie Albino
11/28/2018

35. Example of a Partition (Sub-Grouping) Based Moving Window
QUERY: Calculate a running total of the amount of sales by region
select region, amount,
SUM(amount) OVER
(PARTITION BY region
ORDER BY amount
ROWS UNBOUNDED PRECEDING)
as mov_amt_sum
from product_sales, product, region
where prod_id=product_id
and region_id=reg_id
REGION AMOUNT MOV_AMT_SUM
Central
Central
Central
Central
Central
Central
Central
East
East
East
East
East
East
East
East
East
Willie Albino
11/28/2018

36. Example of Date Based Moving Window Summaries
select cust_id, trans_dt, amt,
sum(amt) over (partition by cust_id order by trans_dt
range interval '1' month preceding) sum_1_mnth,
range between interval '1' month preceding
and interval '1' month following) sum_2_mnth,
sum(amt) over (partition by cust_id order by trans_dt
range between interval '7' DAY preceding
and interval '7' DAY following) sum_wk
from cust_daily_summary
order by cust_id, trans_dt
cust_id Trans_dt Amt Sum_1_mth Sum_2_mth Sum_wk
/15/
/15/
/11/
/12/
/13/
/14/
/15/
/11/
/12/
/13/
/14/
/15/
/11/
/12/
/13/
/14/
/15/
Willie Albino
11/28/2018

37. Oracle8i Analytical SQL Features
Reporting Functions

38. Reporting Functions
Allow for the calculation of aggregate values within a data partition
Return the same aggregate value for every row in a partition
Syntax:
{SUM | AVG | MAX | MIN | COUNT | STDDEV | VARIANCE}
([ALL | DISTINCT] {<value expression1> | *})
OVER ([PARTITION BY <value expression2>[,...]])
Willie Albino
11/28/2018

39. Example of Using Reporting Functions
Query: Find the region where
each product was best seller
SELECT product, region, sum_amt
FROM
(SELECT product, region,
SUM(amount) AS sum_amt,
MAX(SUM(amount)) OVER
(PARTITION BY product)
AS max_sum_amt
FROM product_sales,
region, product
WHERE region_id=reg_id
AND product_id=prod_id
GROUP BY product, region)
WHERE sum_amt = max_sum_amt
REGION REGION SUM_AMT
BELTS Central
HATS Central
JACKETS East
JEANS West
PANTS Central
SHIRTS Central
SHOES East
SOCKS West
SUITS Central
SWEATERS Central
T-SHIRTS West
TIES Central
Willie Albino
11/28/2018

40. New Reporting Functions
RATIO_TO_REPORT(exp)
Computes the ratio of a value to the sum of a set of values
LEAD() and LAG()
Useful for comparing values in different time period
Allows access to more than one row in a table without
a self-join
LAG() provides access to a prior row (at a given offset)
LEAD() provides access to a row after the current position
These functions are position, not value based
Willie Albino
11/28/2018

41. New Reporting Function Syntax
RATIO_TO_REPORT(<exp1>) OVER
([PARTITION BY <exp2> [,…]])
LEAD | LAG
(<exp1> [,<offset> [, <default>]]) OVER
ORDER BY <exp3> [ASC|DESC]
[NULLS FIRST | NULLS LAST] [,…])
<offset> is optional and defaults to 1
<default> is optional and is the value returned if the <offset> falls outside the bounds of the dataset
Willie Albino
11/28/2018

42. Example of RATIO_TO_REPORT()
Query: Find ratio of total
sales per product to total
sales
SELECT product,
SUM(amount) AS sum_amt,
SUM(SUM(amount)) OVER()
AS total_amt,
RATIO_TO_REPORT(SUM(amount))
OVER () AS ratio
FROM product_sales,
product, region
WHERE prod_id = product_id
AND region='East'
GROUP BY product
PRODUCT SUM_AMT TOTAL_AMT RATIO
Belts
Hats
Jackets
Jeans
Pants
Shirts
Shoes
Socks
Suits
Sweaters
T-Shirts
Ties
Willie Albino
11/28/2018

43. Example of LAG Function
Query: Compare cust’s present
amount to the amount 2 days ago
SELECT cust_id, acct_date,
sum(amt_usd) amount,
LAG(SUM(amt_usd),2,-999) OVER
(PARTITION BY cust_id
ORDER BY acct_trans_date )
AS old_amt
FROM acct
WHERE acct_date > '01-NOV-00'
GROUP BY cust_id, acct_date
cust_ID ACCT_DATE AMOUNT OLD_AMT
/11/
/12/
/13/
/14/
/15/
/11/
/12/
/13/
/14/
/15/
/11/
/12/
/13/
Willie Albino
11/28/2018

44. Oracle8i Analytical SQL Features
CASE Expressions

45. CASE Expressions Used for bucketing data
allows for differently sized buckets
Very similar to DECODE statement
Provides more flexibility and logical power
Offers better performance and is easier to read
Syntax:
CASE WHEN <cond1> THEN <v1>
WHEN <cond2> THEN <v2> …
[ ELSE <vn> ] END
Willie Albino
11/28/2018

46. Example #1 of Using CASE Expressions
SELECT
SUM(CASE WHEN SUM(amount)
BETWEEN 0 AND 50
THEN 1 ELSE 0 END) AS "0-50",
BETWEEN 51 AND 150
THEN 1 ELSE 0 END) AS "51-150",
SUM(CASE WHEN SUM(amount) BETWEEN 151 AND 250
THEN 1 ELSE 0 END) AS " ",
SUM(CASE WHEN SUM(amount) > 251
THEN 1 ELSE 0 END) "251+"
FROM product_sales,
product, region
WHERE prod_id = product_id
AND region='East'
GROUP BY product
Willie Albino
11/28/2018

47. Example #2 of Using CASE Expressions
SELECT CASE WHEN amount BETWEEN 0 AND 50
THEN ' 0-50'
WHEN amount BETWEEN 51 AND 150
THEN ' '
WHEN amount BETWEEN 151 AND 250
THEN ' '
WHEN amount > 250 THEN '251+' END bucket,
COUNT(*) cnt,
SUM(amount) amt
FROM product_sales, product, region
WHERE prod_id = product_id
AND region='East'
GROUP BY
CASE WHEN amount BETWEEN 0 AND 50 THEN ’ 0-50'
WHEN amount BETWEEN 51 AND 150 THEN ’ '
WHEN amount BETWEEN 151 AND 250 THEN ' '
WHEN amount > 250 THEN '251+' END
ORDER BY bucket
BUCKET CNT AMT
Willie Albino
11/28/2018

48. Summary New analytical functionality in Oracle 8.1.6 (+)
Makes it easier to code certain types of SQL
Allows for more efficient SQL code when compared to the equivalent pure SQL implementation
Enhancements include
SQL GROUP BY clause has been augmented to make querying and reporting easier
Analytical functions enabling rankings, moving window calculations, lead/lag analysis
Better ‘if-then-else’ capabilities provided through CASE
Willie Albino
11/28/2018