1. Chapter 11 Group Functions (up to p.402)
Jason C. H. Chen, Ph.D.
Professor of MIS
School of Business
Gonzaga University
Spokane, WA USA

2. Objectives Differentiate between single-row and multiple-row functions
Use the SUM and AVG functions for numeric calculations
Use the COUNT function to return the number of records containing non-NULL values
Use COUNT(*) to include records containing NULL values
Use the MIN and MAX functions with nonnumeric fields

3. Objectives (continued)
Determine when to use the GROUP BY clause to group data
Identify when the HAVING clause should be used
List the order of precedence for evaluating WHERE, GROUP BY, and HAVING clauses
State the maximum depth for nesting group functions
Nest a group function inside of a single-row function

4. Objectives (continued)
Calculate the standard deviation and variance of a set of data, using the STDDEV and VARIANCE functions
Explain the concept of multidimensional analysis
Perform enhanced aggregation grouping with the GROUPING SETS, CUBE, and ROLLUP
Use composite columns and concatenated groupings in grouping operations

5. 1. Download chapter 11 files from Bb to c:\oradata\chapter11\
Refresh the Database
1. Download chapter 11 files from Bb to c:\oradata\chapter11\
2. Run the following script file
Start c:\oradata\chapter11\JLDB_Build_11.sql

6. Group Functions
Return one result per group of rows processed
Are also called multiple-row and aggregate functions
All group functions ignore NULL values except COUNT(*)
Use DISTINCT to suppress duplicate values

7. Added Clauses
Figure SELECT statement syntax

8. Calculates total amount stored in a numeric column for a group of rows
SUM Function
Calculates total amount stored in a numeric column for a group of rows
Figure Using the SUM function to calculate order profit

9. Calculates the average of numeric values in a specified column
AVG Function
Calculates the average of numeric values in a specified column
Figure Using the AVG function to calculate average profit

10. -- chapter 11, Figure 11-5(B); p.389
Figure Using the AVG function to calculate average profit
-- chapter 11, Figure 11-5(B); p.389
SELECT TO_CHAR(AVG( retail - cost), '$999.99') "Average Profit"
FROM books
WHERE category = 'COMPUTER';

11. COUNT Function Two purposes Count non-NULL values
Count total records, including those with NULL values

12. COUNT Function – Non-NULL Values
Include column name in argument to count number of occurrences
Figure Using the COUNT function with the DISTINCT option

13. COUNT Function – NULL Values
Include asterisk in argument to count number of rows
Figure Using the COUNT(*) function to include NULL values

14. MAX Function Returns largest value
Figure Using the MAX function on numeric data

15. Returns the smallest value
MIN Function
Returns the smallest value
Figure Using the MIN function on date data

16. Datatypes
The COUNT, MIN, and MAX functions can be used on values with character, numeric, and date datatypes

17. Grouping Data GROUP BY clause Used to group data
Must be used for any individual column in the SELECT clause with a group function
Cannot reference column aliases

18. GROUP BY Example
Figure Adding the GROUP BY clause

19. Common Error
A common error is missing a GROUP BY clause for nonaggregated columns in the SELECT clause
Figure Flawed query: Including both aggregate and nonaggregate columns requires a GROUP BY clause

20. It is an individual record (just “Profit” not “Highest Profit”)
Figure Inappropriate use of GROUP BY

21. Figure 11-20 Calculate the total amount due by each customer and order

22. Restricting Aggregated Output
HAVING clause serves as the WHERE clause for grouped data.
It is used to eliminate certain groups from further consideration.
4 records are eliminated
Figure Using a HAVING clause to restrict which groups are displayed

23. Restricting Aggregated Output (continued)
When included in the same SELECT statement, the clauses are evaluated in the order of:
WHERE
GROUP BY
HAVING

24. Restricting Aggregated Output (continued)
Figure Using the WHERE, GROUP BY, and HAVING clauses

25. Figure 11-23 Using a HAVING clause to restrict grouped output

26. Figure 11-24 Filtering correctly with the WHERE and HAVING clauses

27. It is quite inefficient and considered poor SQL programming practice
It is quite inefficient and considered poor SQL programming practice. The statement must process ALL rows in the BOOKS table with the aggregated calculation and then eliminate categories.
Figure Filtering incorrectly with the HAVING clauses
Figure Filtering correctly with the WHERE and HAVING clauses

28. Inner function is resolved first Maximum nesting depth: 2
Nesting Functions
Inner function is resolved first
Maximum nesting depth: 2
Figure Nesting group functions

29. Exercises
Practice all the examples in the text.
A Script file is available on the Bb (file name: ch11Queries.sql)
After completing all examples, do the HW.
In-class Exercise
#7 (p.424)

30. Homework - Hands-On Assignments
me with one attachment
(Oracle_ch11_Spool_Lname_Fname.) to:
with subject title of
Bmis441-01_Oracle_ch11 (or Bmis441-02_Oracle_ch11)
Read and Practice all examples on Chapters 11
1. Run the script files (in the folder \oradata\chapter11\): JLDB_Build_11.sql
2. Read Oracle assignment and create a script file Oracle_ch11_Lname_Fname.sql for questions (ALL EVEN problems; pp ) on “Hands-on Assignments”. Use appropriate COLUMN or other SQL commands to produce readable outputs (or your grade will be discounted –see a sample output on the Bb)
3. Execute and test one problem at a time and make sure they are all running successfully.
4. When you done, spool the script files (see next slide for spooling instructions) and the file (Oracle_ch11_Spool_Lname_Fname.txt) to me by the midnight before the next class.

31. How to Spool your Script and Output Files
After you tested the script file of Oracle_ch11_Lname_Fname.sql successfully, follow the instructions below to spool both script and output files:
Step 0. Run the following script file from SQL*Plus (since you have created JLDB tables)
Start c:\oradata\chapter11\JLDB_Build_11.sql
1. type the following on SQL>
Spool c:\oradata\Oracle_ch11_Spool_Lname_Fname.txt (make sure your name is entered)
2. open Oracle_ch11_Lname_Fname.sql that you already tested
3. copy and paste all the SQL commands (including all comments) to the SQL*PLUS
4. type Spool Off on the SQL>
The output should contain your personal information, all SQL commands and their solution on the .txt file and saved in C: drive (oradata\ folder)
me with the spooled file (.txt) with attachment to:
with subject title of
Bmis441-01_Oracle_ch11 (or Bmis441-02_Oracle_ch11)

32. Summary
The AVG, SUM, STDDEV, and VARIANCE functions are used only with numeric fields
The COUNT, MAX, and MIN functions can be applied to any datatype
The AVG, SUM, MAX, MIN, STDDEV, and VARIANCE functions all ignore NULL values
By default, the AVG, SUM, MAX, MIN, COUNT, STDDEV, and VARIANCE functions include duplicate values

33. Summary (continued)
The GROUP BY clause is used to divide table data into groups
If a SELECT clause contains both an individual field name and a group function, the field name must also be included in a GROUP BY clause
The HAVING clause is used to restrict groups in a group function
Group functions can be nested to a depth of only two. The inner function is always performed first, using the specified grouping. The results of the inner function are used as input for the outer function.

34. Summary (continued)
The STDDEV and VARIANCE functions are used to perform statistical analyses on a set of data
GROUPING SETS operations can be used to perform multiple GROUP BY aggregations with a single query
The CUBE extension of the GROUP BY calculates aggregations for all possible combinations or groupings of columns included
The ROLLUP extension of the GROUP BY calculates increasing levels of accumulated subtotals for the column list provided
Composite columns and concatenated groupings can be used in GROUPING SETS, CUBE, and ROLLUP operations
The GROUP_ID function helps eliminate duplicate grouping results

35. STDDEV Function
Figure Using the STDDEV function

36. Determines data dispersion within a group
VARIANCE Function
Determines data dispersion within a group
Figure Using the VARIANCE function

37. Enhanced Aggregation for Reporting
Oracle provides extensions to the GROUP BY clause, which allow both aggregation across multiple dimensions or the generation of increasing levels of subtotals with a single SELECT statement
A dimension is a term used to describe any category used in analyzing data, such as time, geography, and product line
Each dimension could contain various levels of aggregation; for example, the time dimension may include aggregation by month, quarter, and year; the product dimension may include product type, sales, store, region and month.

38. Figure Slicing a data cube
REGION
Hoffer’s text (chapter 11)
CUSTOMER

39. Excel Pivot Table Example
Figure A pivot table with two dimensions on a row

40. Excel Pivot Table Example (continued)
Figure A pivot table with one row and one column dimension

41. GROUPING SETS
The grouping sets expression is the component on which the other GROUP BY extensions, ROLLUP and CUBE, are built.
With this extension, you can use a single query statement to perform multiple GROUP BY clauses.
The single query in Figure produces the average retail price for books in four groupings: 1) publisher (the Name column) and category, 2) category, 3) publisher, and 4) overall average.
-- chapter 11, Figure 11-32; p.408
SELECT name, category, COUNT(isbn),
TO_CHAR(AVG(retail), '99.99') "Avg Retail"
FROM publisher JOIN books USING (pubid)
WHERE pubid IN (2,3,5)
GROUP BY GROUPING SETS (name, category, (name, category), ())
Try without GROUING SETS

42. Without GROUPING SETS -- chapter 11, Figure 11-32(a); p.408
SELECT name, category, COUNT(isbn),
TO_CHAR(AVG(retail), '99.99') "Avg Retail"
FROM publisher JOIN books USING (pubid)
WHERE pubid IN (2,3,5)
GROUP BY name, category;

43. Grouping Sets
Figure Using a GROUPING SETS expression in a GROUP BY clause

44. The CUBE Extension
The CUBE extension of GROUP BY instructs Oracle to perform aggregations for all possible combinations of the specified columns (e.g., two columns: Name (publisher name) and Category).
The outputs matches Figure
If you need only a subset of the four aggregate levels calculated, you must use the GROUPING SETS expression because the CUBE extension always performs all aggregation levels.
Adding a GROUPING function to the CUBE extension (Fig ) to identify subtotal rows in the results (helpful in labeling sorting, and restricting output).

45. CUBE
Figure Using the CUBE extension of GROUP BY

46. Figure 11-35 The GROUPING function returns a 1 to identify subtotal rows

47. The ROLLUP Extension
The ROLLUP extension of GROUP BY calculates cumulative subtotals for the specified columns.
If multiple columns are indicated, subtotals are performed for each column in the argument list, except the one on the far right. A grand total is also calculated.

48. ROLLUP
Figure Using the ROLLUP extension of GROUP BY

49. Category is the column outside the ROLLUP and is considered the aggregate value.
A subtotal is calculated for the aggregate value as well as for each unique value of the ROLLUP column in the aggregate value – i.e., by Category and each Name in each Category .
Figure Using a partial ROLLUP