- Sams Teach Yourself SQL in 24 Hours, Third Edition
- Table of Contents
- Copyright
- About the Authors
- Acknowledgments
- Tell Us What You Think!
- Introduction
- Part I: A SQL Concepts Overview
- Hour 1. Welcome to the World of SQL
- SQL Definition and History
- SQL Sessions
- Types of SQL Commands
- An Introduction to the Database Used in This Book
- Summary
- Q&A
- Workshop
- Part II: Building Your Database
- Hour 2. Defining Data Structures
- What Is Data?
- Basic Data Types
- Summary
- Q&A
- Workshop
- Hour 3. Managing Database Objects
- What Are Database Objects?
- What Is a Schema?
- A Table: The Primary Storage for Data
- Integrity Constraints
- Summary
- Q&A
- Workshop
- Hour 4. The Normalization Process
- Normalizing a Database
- Summary
- Q&A
- Workshop
- Hour 5. Manipulating Data
- Overview of Data Manipulation
- Populating Tables with New Data
- Updating Existing Data
- Deleting Data from Tables
- Summary
- Q&A
- Workshop
- Hour 6. Managing Database Transactions
- What Is a Transaction?
- What Is Transactional Control?
- Transactional Control and Database Performance
- Summary
- Q&A
- Workshop
- Part III: Getting Effective Results from Queries
- Hour 7. Introduction to the Database Query
- What Is a Query?
- Introduction to the <tt>SELECT</tt> Statement
- Examples of Simple Queries
- Summary
- Q&A
- Workshop
- Hour 8. Using Operators to Categorize Data
- What Is an Operator in SQL?
- Comparison Operators
- Logical Operators
- Conjunctive Operators
- Negating Conditions with the <tt>NOT</tt> Operator
- Arithmetic Operators
- Summary
- Q&A
- Workshop
- Hour 9. Summarizing Data Results from a Query
- What Are Aggregate Functions?
- Summary
- Q&A
- Workshop
- Hour 10. Sorting and Grouping Data
- Why Group Data?
- The <tt>GROUP BY</tt> Clause
- <tt>GROUP BY</tt> Versus <tt>ORDER BY</tt>
- The <tt>HAVING</tt> Clause
- Summary
- Q&A
- Workshop
- Hour 11. Restructuring the Appearance of Data
- The Concepts of ANSI Character Functions
- Various Common Character Functions
- Miscellaneous Character Functions
- Mathematical Functions
- Conversion Functions
- The Concept of Combining Character Functions
- Summary
- Q&A
- Workshop
- Hour 12. Understanding Dates and Times
- How Is a Date Stored?
- Date Functions
- Date Conversions
- Summary
- Q&A
- Workshop
- Part IV: Building Sophisticated Database Queries
- Hour 13. Joining Tables in Queries
- Selecting Data from Multiple Tables
- Types of Joins
- Join Considerations
- Summary
- Q&A
- Workshop
- Hour 14. Using Subqueries to Define Unknown Data
- What Is a Subquery?
- Embedding a Subquery Within a Subquery
- Summary
- Q&A
- Workshop
- Hour 15. Combining Multiple Queries into One
- Single Queries Versus Compound Queries
- Why Would I Ever Want to Use a Compound Query?
- Compound Query Operators
- Using an <tt>ORDER BY</tt> with a Compound Query
- Using <tt>GROUP BY</tt> with a Compound Query
- Retrieving Accurate Data
- Summary
- Workshop
- Q&A
- Part V: SQL Performance Tuning
- Hour 16. Using Indexes to Improve Performance
- What Is an Index?
- How Do Indexes Work?
- The <tt>CREATE INDEX</tt> Command
- Types of Indexes
- When Should Indexes Be Considered?
- When Should Indexes Be Avoided?
- Summary
- Q&A
- Workshop
- Hour 17. Improving Database Performance
- What Is SQL Statement Tuning?
- Database Tuning Versus SQL Tuning
- Formatting Your SQL Statement
- Full Table Scans
- Other Performance Considerations
- Performance Tools
- Summary
- Q&A
- Workshop
- Part VI: Using SQL to Manage Users and Security
- Hour 18. Managing Database Users
- Users Are the Reason
- The Management Process
- Tools Utilized by Database Users
- Summary
- Q&A
- Workshop
- Hour 19. Managing Database Security
- What Is Database Security?
- How Does Security Differ from User Management?
- What Are Privileges?
- Controlling User Access
- Controlling Privileges Through Roles
- Summary
- Q&A
- Workshop
- Part VII: Summarized Data Structures
- Hour 20. Creating and Using Views and Synonyms
- What Is a View?
- Creating Views
- Dropping a View
- What Is a Synonym?
- Summary
- Q&A
- Workshop
- Hour 21. Working with the System Catalog
- What Is the System Catalog?
- How Is the System Catalog Created?
- What Is Contained in the System Catalog?
- Examples of System Catalog Tables by Implementation
- Querying the System Catalog
- Updating System Catalog Objects
- Summary
- Q&A
- Workshop
- Part VIII: Applying SQL Fundamentals in Today's World
- Hour 22. Advanced SQL Topics
- Advanced Topics
- Cursors
- Stored Procedures and Functions
- Triggers
- Dynamic SQL
- Call-Level Interface
- Using SQL to Generate SQL
- Direct Versus Embedded SQL
- Summary
- Q&A
- Workshop
- Hour 23. Extending SQL to the Enterprise, the Internet, and the Intranet
- SQL and the Enterprise
- Accessing a Remote Database
- Accessing a Remote Database Through a Web Interface
- SQL and the Internet
- SQL and the Intranet
- Summary
- Q&A
- Workshop
- Hour 24. Extensions to Standard SQL
- Various Implementations
- Examples of Extensions from Some Implementations
- Interactive SQL Statements
- Summary
- Q&A
- Workshop
- Part IX: Appendixes
- Appendix A. Common SQL Commands
- SQL Statements
- SQL Clauses
- Appendix B. Using MySQL for Exercises
- Windows Installation Instructions
- Linux Installation Instructions
- Appendix C. Answers to Quizzes and Exercises
- Hour 1, "Welcome to the World of SQL"
- Hour 2, "Defining Data Structures"
- Hour 3, "Managing Database Objects"
- Hour 4, "The Normalization Process"
- Hour 5, "Manipulating Data"
- Hour 6, "Managing Database Transactions"
- Hour 7, "Introduction to the Database Query"
- Hour 8, "Using Operators to Categorize Data"
- Hour 9, "Summarizing Data Results from a Query"
- Hour 10, "Sorting and Grouping Data"
- Hour 11, "Restructuring the Appearance of Data"
- Hour 12, "Understanding Dates and Time"
- Hour 13, "Joining Tables in Queries"
- Hour 14, "Using Subqueries to Define Unknown Data"
- Hour 15, "Combining Multiple Queries into One"
- Hour 16, "Using Indexes to Improve Performance"
- Hour 17, "Improving Database Performance"
- Hour 18, "Managing Database Users"
- Hour 19, "Managing Database Security"
- Hour 20, "Creating and Using Views and Synonyms"
- Hour 21, "Working with the System Catalog"
- Hour 22, "Advanced SQL Topics"
- Hour 23, "Extending SQL to the Enterprise, the Internet, and the Intranet"
- Hour 24, "Extensions to Standard SQL"
- Appendix D. <tt>CREATE TABLE</tt> Statements for Book Examples
- <tt>EMPLOYEE_TBL</tt>
- <tt>EMPLOYEE_PAY_TBL</tt>
- <tt>CUSTOMER_TBL</tt>
- <tt>ORDERS_TBL</tt>
- <tt>PRODUCTS_TBL</tt>
- Appendix E. <tt>INSERT</tt> Statements for Data in Book Examples
- <tt>INSERT</tt> Statements
- Appendix F. Glossary
- Appendix G. Bonus Exercises
What Are Aggregate Functions?
Functions are keywords in SQL used to manipulate values within columns for output purposes. A function is a command always used in conjunction with a column name or expression. There are several types of functions in SQL. This hour covers aggregate functions. An aggregate function is used to provide summarization information for an SQL statement, such as counts, totals, and averages. |
The aggregate functions discussed in this hour are
- COUNT
- SUM
- MAX
- MIN
- AVG
The following queries show the data used for most of this hour's examples:
SELECT * FROM PRODUCTS_TBL; PROD_ID PROD_DESC COST ---------- ------------------------------ ------ 11235 WITCHES COSTUME 29.99 222 PLASTIC PUMPKIN 18 INCH 7.75 13 FALSE PARAFFIN TEETH 1.1 90 LIGHTED LANTERNS 14.5 15 ASSORTED COSTUMES 10 9 CANDY CORN 1.35 6 PUMPKIN CANDY 1.45 87 PLASTIC SPIDERS 1.05 119 ASSORTED MASKS 4.95 1234 KEY CHAIN 5.95 2345 OAK BOOKSHELF 59.99 11 rows selected.
Some employees do not have a pager number in the results of the following query:
SELECT EMP_ID, LAST_NAME, FIRST_NAME, PAGER FROM EMPLOYEE_TBL; EMP_ID LAST_NAM FIRST_NA PAGER --------- -------- -------- ---------- 311549902 STEPHENS TINA 442346889 PLEW LINDA 213764555 GLASS BRANDON 3175709980 313782439 GLASS JACOB 8887345678 220984332 WALLACE MARIAH 443679012 SPURGEON TIFFANY 6 rows selected.
The COUNT Function
The COUNT function is used to count rows or values of a column that do not contain a NULL value. When used with a query, the COUNT function returns a numeric value. When the COUNT function is used with the DISTINCT command, only the distinct rows are counted. ALL (opposite of DISTINCT) is the default; it is not necessary to include ALL in the syntax. Duplicate rows are counted if DISTINCT is not specified. One other option with the COUNT function is to use COUNT with an asterisk. COUNT, when used with an asterisk (COUNT(*)), counts all the rows of a table including duplicates, whether a NULL value is contained in a column or not.
The syntax for the COUNT function is as follows:
COUNT [ (*) | (DISTINCT | ALL) ] (COLUMN NAME)
Example |
Meaning |
SELECT COUNT(EMPLOYEE_ID) FROM EMPLOYEE_PAY_ID |
Counts all employee IDs |
SELECT COUNT(DISTINCT SALARY)FROM EMPLOYEE_PAY_TBL |
Counts only the distinct rows |
SELECT COUNT(ALL SALARY)FROM EMPLOYEE_PAY_TBL |
Counts all rows for SALARY |
SELECT COUNT(*) FROM EMPLOYEE_TBL |
Counts all rows of the EMPLOYEE table |
COUNT(*) is used in the following example to get a count of all records in the EMPLOYEE_TBL table. There are six employees.
SELECT COUNT(*) FROM EMPLOYEE_TBL; COUNT(*) ---------- 6
COUNT(EMP_ID) is used in the next example to get a count of all the employee identifications that exist in the table. The returned count is the same as the last query because all employees have an identification number.
SELECT COUNT(EMP_ID) FROM EMPLOYEE_TBL; COUNT(EMP_ID) ------------- 6
COUNT(PAGER) is used in the following example to get a count of all of the employee records that have a pager number. Only two employees had pager numbers.
SELECT COUNT(PAGER) FROM EMPLOYEE_TBL; COUNT(PAGER) ------------ 2
The ORDERS_TBL table, shown next, is used in the following COUNT example:
SELECT * FROM ORDERS_TBL; ORD_NUM CUST_ID PROD_ID QTY ORD_DATE_ ---------- ---------- ----------------- ------------- 56A901 232 11235 1 22-OCT-99 56A917 12 907 100 30-SEP-99 32A132 43 222 25 10-OCT-99 16C17 090 222 2 17-OCT-99 18D778 287 90 10 17-OCT-99 23E934 432 13 20 15-OCT-99 90C461 560 1234 2 7 rows selected.
This last example obtains a count of all distinct product identifications in the ORDERS_TBL table.
SELECT COUNT(DISTINCT(PROD_ID)) FROM ORDERS_TBL; COUNT(DISTINCT(PROD_ID)) ------------------------ 6
The PROD_ID 222 has two entries in the table, thus reducing the distinct values from 7 to 6.
The SUM Function
The SUM function is used to return a total on the values of a column for a group of rows. The SUM function can also be used in conjunction with DISTINCT. When SUM is used with DISTINCT, only the distinct rows are totaled, which may not have much purpose. Your total is not accurate in that case because rows of data are omitted.
The syntax for the SUM function is as follows:
SUM ([ DISTINCT ] COLUMN NAME)
Example |
Meaning |
SELECT SUM(SALARY) FROM EMPLOYEE_PAY_TBL |
Totals the salaries |
SELECT SUM(DISTINCT SALARY) FROM EMPLOYEE_PAY_TBL |
Totals the distinct salaries |
In the following query, the sum, or total amount, of all cost values is being retrieved from the PRODUCTS_TBL table:
SELECT SUM(COST) FROM PRODUCTS_TBL; SUM(COST) ---------- 163.07
The AVG Function
The AVG function is used to find averages for a group of rows. When used with the DISTINCT command, the AVG function returns the average of the distinct rows. The syntax for the AVG function is as follows:
AVG ([ DISTINCT ] COLUMN NAME)
Example |
Meaning |
SELECT AVG(SALARY) FROM EMPLOYEE_PAY_TBL |
Returns the average salary |
SELECT AVG(DISTINCT SALARY) EMPLOYEE_PAY_TBL |
Returns the distinct FROM average salary |
The average value for all values in the PRODUCTS_TBL table's COST column is being retrieved in the following example:
SELECT AVG(COST) FROM PRODUCTS_TBL; AVG(COST) ---------- 13.5891667
The next example uses two aggregate functions in the same query. Because some employees are paid hourly and others paid a salary, you want to retrieve the average value for both PAY_RATE and SALARY.
SELECT AVG(PAY_RATE), AVG(SALARY) FROM EMPLOYEE_PAY_TBL; AVG(PAY_RATE) AVG(SALARY) ------------- ----------- 13.5833333 30000
The MAX Function
The MAX function is used to return the maximum value for the values of a column in a group of rows. NULL values are ignored when using the MAX function. The DISTINCT command is an option. However, because the maximum value for all the rows is the same as the distinct maximum value, DISTINCT is useless.
MAX([ DISTINCT ] COLUMN NAME)
Example |
Meaning |
SELECT MAX(SALARY) FROM EMPLOYEE_PAY_TBL |
Returns the highest salary |
SELECT MAX(DISTINCT SALARY) FROM EMPLOYEE_PAY_TBL |
Returns the highest distinct salary |
The following example returns the maximum value for the COST column in the PRODUCTS_TBL table:
SELECT MAX(COST) FROM PRODUCTS_TBL; MAX(COST) ---------- 59.99
The MIN Function
The MIN function returns the minimum value of a column for a group of rows. NULL values are ignored when using the MIN function. The DISTINCT command is an option. However, because the minimum value for all rows is the same as the minimum value for distinct rows, DISTINCT is useless.
MIN([ DISTINCT ] COLUMN NAME)
Example |
Meaning |
SELECT MIN(SALARY) FROM EMPLOYEE_PAY_TBL |
Returns the lowest salary |
SELECT MIN(DISTINCT SALARY) FROM EMPLOYEE_PAY_TBL |
Returns the lowest distinct salary |
The following example returns the minimum value for the COST column in the PRODUCTS_TBL table:
SELECT MIN(COST) FROM PRODUCTS_TBL; MIN(COST) ---------- 1.05
The final example combines aggregate functions with the use of arithmetic operators:
SELECT COUNT(ORD_NUM), SUM(QTY), SUM(QTY) / COUNT(ORD_NUM) AVG_QTY FROM ORDERS_TBL; COUNT(ORD_NUM) SUM(QTY) AVG_QTY -------------- ---------- ---------- 7 160 22.857143
You have performed a count on all order numbers, figured the sum of all quantities ordered, and, by dividing the two figures, have derived the average quantity of an item per order. You also created a column alias for the computation—AVG_QTY.