This document discusses OLAP functions in Informix 12.1. It provides an overview of OLAP and what it is used for in business intelligence. It then describes the OVER clause and how it defines the domain of OLAP function calculation using optional PARTITION BY, ORDER BY, and WINDOW FRAME clauses. Several examples of ranking, aggregation, and analytic OLAP functions like RANK, SUM, and LAG are shown. The document concludes by noting how OLAP functions can be accelerated by the Informix Warehouse Accelerator.

1. OLAP Functions Support in
Informix 12.1
Bingjie Miao
IBM
1

2. Agenda
•What is OLAP
•OLAP functions in Informix
– the OVER clause
– supported OLAP functions
•Questions?

3. What is OLAP?
• On-Line Analytical Processing
• Commonly used in Business
Intelligence (BI) tools
– ranking products, salesmen, items, etc
– exposing trends in sales from historic data
– testing business scenarios (forecast)
– sales breakdown or aggregates on multiple
dimensions (Time, Region, Demographics, etc)

4. OLAP Functions in Informix
• Supports subset of commonly used
OLAP functions
• Enables more efficient query
processing from BI tools such as
Cognos

5. Example query with group by
select customer_num, count(*)
from orders
where customer_num <= 110
group by customer_num;
customer_num (count(*))
101 1
104 4
106 2
110 2
4 row(s) retrieved.

6. Example query with OLAP function
select customer_num, ship_date, ship_charge,
count(*) over (partition by customer_num)
from orders
where customer_num <= 110;
customer_num ship_date ship_charge (count(*))
101 05/26/2008 $15.30 1
104 05/23/2008 $10.80 4
104 07/03/2008 $5.00 4
104 06/01/2008 $10.00 4
104 07/10/2008 $12.20 4
106 05/30/2008 $19.20 2
106 07/03/2008 $12.30 2
110 07/06/2008 $13.80 2
110 07/16/2008 $6.30 2
9 row(s) retrieved.

7. Where does OLAP function fit?
Joins, group by,
having, aggregation
OLAP functions
Final order by

8. OLAP function as predicates
• Use derived table query block to compute
OLAP function first
select * from
(select customer_num, ship_date,
ship_charge,
count(*) over (partition by
customer_num) as cnt
from orders
where customer_num <= 110)
where cnt >= 3;

9. OLAP function example
• Running 3-month average sales for a
particular product during a particular period
select product_name,
avg(sales) over (
partition by region
order by year, month
rows between 1 preceding and 1 following
)
from total_sales
where product_id = 105
and year between 2001 and 2010;

10. The over() Clause
olap_func(arg) over(partition by clause
order by clause window frame clause)
• Defines the “domain” of OLAP function
calculation
– partition by: divide into partitions
– order by: ordering within each partition
– window frame: sliding window within each
partition
– all clauses optional

11. Partition By
sum(x) over (
partition by a, b
order by c, d
rows between 2 preceding and 2 following)
a=1, b=1
a=2, b=2
a=1, b=2
a=2, b=1

12. Order By
sum(x) over (
partition by a, b
order by c, d
rows between 2 preceding and 2 following)
partition a=1, b=2
c=1,d=1
c=1,d=2
c=1,d=3
c=2,d=2
c=2,d=4
c=3,d=1
c=4,d=1
c=4,d=2

13. Window Frame
c=1,d=1
c=1,d=2
c=1,d=3
c=2,d=2
c=2,d=4
c=3,d=1
c=4,d=1
c=4,d=2
sum(x) over (
partition by a, b
order by c, d
rows between 2 preceding and 2 following)

14. Partition By
• Divide result set of query into partitions for
computing of an OLAP function
• If partition by clause is not specified, then
entire result set is a single partition
max(salary) over (partition by dept_id)
sum(sales) over (partition by region)
avg(price) over ()

15. Order By
• Ordering within each partition
• Required for some OLAP functions
–ranking, window frame clause
• Support ASC/DESC, NULLS FIRST/NULLS LAST
rank() over (partition by dept
order by salary desc)
dense_rank() over(order by total_sales
nulls last)

16. Window Frame
• Defines a sliding window within a partition
• OLAP function value computed from rows in the
sliding window
• Order by clause is required

17. Physical vs. Logical Window Frame
• Physical window frame
– ROWS keyword
– count offset by position
– fixed window size
– order by one or more column expressions
• Logical window frame
– RANGE keyword
– count offset by value
– window size may vary
– order by single column (numeric, date or datetime type)

18. Window Frame Examples
avg(price) over (order by year, day
rows between 6 preceding and current row)
count(*) over (order by ship_date
range between 2 preceding and 2 following)
• Current row can be physically outside the window
avg(sales) over (order by month
range between 3 preceding and 1 preceding)
sum(sales) over (order by month
rows between 2 following and 5 following)

19. Order By – Special Semantics
• “cumulative” semantics in absence of window
frame clause
– for OLAP function that allows window frame clause
– equivalent to “ROWS between unbounded preceding
and current row”
select sales, sum(sales) over (order by quarter)
from sales where year = 2012
sales (sum)
120 120
135 255
127 382
153 535

20. Supported OLAP Functions
• Ranking functions
– RANK, DENSE_RANK (DENSERANK)
– PERCENT_RANK, CUME_DIST, NTILE
– LEAD, LAG
• Numbering functions
– ROW_NUMBER (ROWNUMBER)
• Aggregate functions
– SUM, COUNT, AVG, MIN, MAX
– STDEV, VARIANCE, RANGE
– FIRST_VALUE, LAST_VALUE
– RATIO_TO_REPORT (RATIOTOREPORT)

21. Ranking Functions
• Partition by clause is optional
• Order by clause is required
• Window frame clause is NOT allowed
• Duplicate value handling is different between
rank() and dense_rank()
– same rank given to all duplicates
– next rank used “skips” ranks already covered by
duplicates in rank(), but uses next rank for
dense_rank()

22. RANK vs DENSE_RANK
select emp_num, sales,
rank() over (order by sales) as rank,
dense_rank() over (order by sales) as dense_rank
from sales;
emp_num sales rank dense_rank
101 2,000 1 1
102 2,400 2 2
103 2,400 2 2
104 2,500 4 3
105 2,500 4 3
106 2,650 6 4

23. PERCENT_RANK and CUME_DIST
• Calculates ranking information as a percentile
• Returns value between 0 and 1
select emp_num, sales,
percent_rank() over (order by sales) as per_rank,
cume_dist() over (order by sales) as cume_dist
from sales;
emp_num sales per_rank cume_dist
101 2,000 0 0.166666667
102 2,400 0.2 0.500000000
103 2,400 0.2 0.500000000
104 2,500 0.6 0.833333333
105 2,500 0.6 0.833333333
106 2,650 1.0 1.000000000

24. NTILE
• Divides the ordered data set into N
number of tiles indicated by the
expression.
• Number of tiles needs to be exact
numeric with scale zero

25. NTILE Example
select name, salary,
ntile(5) over (partition by dept order by salary)
from employee;
name salary (ntile)
John 35,000 1
Jack 38,400 1
Julie 41,200 2
Manny 45,600 2
Nancy 47,300 3
Pat 49,500 4
Ray 51,300 5

26. LEAD and LAG
LEAD(expr, offset, default)
LAG(expr, offset, default)
• Gives LEAD/LAG value of the expression at the
specified offset
• offset is optional, default to 1 if not specified
• default is optional, NULL if not specified
– default used when offset goes beyond current partition
boundary
• NULL handling
– RESPECT NULLS (default)
– IGNORE NULLS

27. LEAD/LAG Example
select name, salary, lag(salary)
over (partition by dept order by salary),
lead(salary, 1, 0)
over (partition by dept order by salary)
from employee;
name salary (lag) (lead)
John 35,000 38,400
Jack 38,400 35,000 41,200
Julie 41,200 38,400 45,600
Manny 45,600 41,200 47,300
Nancy 47,300 45,600 49,500
Pat 49,500 47,300 51,300
Ray 51,300 49,500 0

28. LEAD/LAG NULL handling
select price,
lag(price ignore nulls, 1) over (order by day),
lead(salary, 1) ignore nulls over (order by day)
from stock_price;
price (lag) (lead)
18.25 18.37
18.37 18.25 19.03
18.37 19.03
18.37 19.03
19.03 18.37 18.59
18.59 19.03 18.21
18.21 18.59

29. Numbering Functions
• Partition by clause and order by clause are
optional
• Window frame clause is NOT allowed
• Provides sequential row number to result set
– regardless of duplicates when order by is specified

30. ROW_NUMBER Example
select row_number() over (order by sales),
emp_num, sales
from sales;
(row_number) emp_num sales
1 101 2,000
2 102 2,400
3 103 2,400
4 104 2,500
5 105 2,500
6 106 2,650

31. Aggregate Functions
• Partition by, order by and window frame
clauses are all optional
– window frame clause requires order by clause
• All currently supported aggregate functions
– SUM, COUNT, MIN, MAX, AVG, STDEV, RANGE,
VARIANCE
• New aggregate functions
– FIRST_VALUE/LAST_VALUE
– RATIO_TO_REPORT

32. Aggregate Function Example
select price,
avg(price) over (order by day
rows between 1 preceding and 1 following)
from stock_price;
price (avg)
18.25 18.31
18.37 18.31
18.37
19.03
19.03 18.81
18.59 18.61
18.21 18.40

33. DISTINCT handling
• DISTINCT is supported, however DISTINCT is mutually
exclusive with order by clause or window frame
clause
select emp_id, manager_id,
count(distinct manager_id)
over (partition by department)
from employee;
emp_id manager_id (count)
101 103 3
102 103 3
103 100 3
104 110 3
105 110 3

34. FIRST_VALUE and LAST_VALUE
• Gives FIRST/LAST value of current
partition
• NULL handling
–RESPECT NULLS (default)
–IGNORE NULLS

35. FIRST_VALUE/LAST_VALUE Example
select price, price – first_value(price)
over (partition by year order by day)
as diff_price
from stock_price;
price diff_price
18.25 0
18.37 0.12
19.03 0.78
18.59 0.34
18.21 -0.04

36. RATIO_TO_REPORT
• Computes the ratio of current value to
sum of all values in current partition or
window frame.
select emp_num, sales,
ratio_to_report(sales) over (partition by
year order by sales)
from sales;

37. RATIO_TO_REPORT Example
select year, sales, ratio_to_report(sales)
over (partition by year)
from sales;
year sales (ratio_to_report)
1998 2400 0.2308
1998 2550 0.2452
1998 2650 0.2548
1998 2800 0.2692
1999 2450 0.2311
1999 2575 0.2429
1999 2725 0.2571
1999 2850 0.2689

38. Nested OLAP Functions
• OLAP function can be nested inside another
OLAP function
select emp_id, salary, salary – first_value(salary)
over (order by rank() over (order by salary))
as diff_salary
from employee;
select sum(ntile(10) over (order by salary))
over (partition by department)
from employee;

39. OLAP functions and IWA
• Queries containing OLAP functions can be
accelerated by Informix Warehouse
Accelerator (IWA)
• IWA processes majority of the query block
– scan, join, group by, having, aggregation
• Informix server processes OLAP functions
based on query result from IWA

40. For more information
• Links to OLAP function in Informix 12.1
documentation
http://pic.dhe.ibm.com/infocenter/informix/v121/inde
x.jsp?topic=%2Fcom.ibm.sqls.doc
%2Fids_sqs_2583.htm
http://pic.dhe.ibm.com/infocenter/informix/v121/inde
x.jsp?topic=%2Fcom.ibm.acc.doc
%2Fids_acc_queries1.htm

41. Questions?
Bingjie Miao
bingjie@us.ibm.com
41