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📄 Contents

  1. Sams Teach Yourself SQL in 24 Hours, Third Edition
  2. Table of Contents
  3. Copyright
  4. About the Authors
  5. Acknowledgments
  6. Tell Us What You Think!
  7. Introduction
  8. Part I: A SQL Concepts Overview
  9. Hour 1. Welcome to the World of SQL
  10. SQL Definition and History
  11. SQL Sessions
  12. Types of SQL Commands
  13. An Introduction to the Database Used in This Book
  14. Summary
  15. Q&A
  16. Workshop
  17. Part II: Building Your Database
  18. Hour 2. Defining Data Structures
  19. What Is Data?
  20. Basic Data Types
  21. Summary
  22. Q&A
  23. Workshop
  24. Hour 3. Managing Database Objects
  25. What Are Database Objects?
  26. What Is a Schema?
  27. A Table: The Primary Storage for Data
  28. Integrity Constraints
  29. Summary
  30. Q&A
  31. Workshop
  32. Hour 4. The Normalization Process
  33. Normalizing a Database
  34. Summary
  35. Q&A
  36. Workshop
  37. Hour 5. Manipulating Data
  38. Overview of Data Manipulation
  39. Populating Tables with New Data
  40. Updating Existing Data
  41. Deleting Data from Tables
  42. Summary
  43. Q&A
  44. Workshop
  45. Hour 6. Managing Database Transactions
  46. What Is a Transaction?
  47. What Is Transactional Control?
  48. Transactional Control and Database Performance
  49. Summary
  50. Q&A
  51. Workshop
  52. Part III: Getting Effective Results from Queries
  53. Hour 7. Introduction to the Database Query
  54. What Is a Query?
  55. Introduction to the <tt>SELECT</tt> Statement
  56. Examples of Simple Queries
  57. Summary
  58. Q&amp;A
  59. Workshop
  60. Hour 8. Using Operators to Categorize Data
  61. What Is an Operator in SQL?
  62. Comparison Operators
  63. Logical Operators
  64. Conjunctive Operators
  65. Negating Conditions with the <tt>NOT</tt> Operator
  66. Arithmetic Operators
  67. Summary
  68. Q&amp;A
  69. Workshop
  70. Hour 9. Summarizing Data Results from a Query
  71. What Are Aggregate Functions?
  72. Summary
  73. Q&amp;A
  74. Workshop
  75. Hour 10. Sorting and Grouping Data
  76. Why Group Data?
  77. The <tt>GROUP BY</tt> Clause
  78. <tt>GROUP BY</tt> Versus <tt>ORDER BY</tt>
  79. The <tt>HAVING</tt> Clause
  80. Summary
  81. Q&amp;A
  82. Workshop
  83. Hour 11. Restructuring the Appearance of Data
  84. The Concepts of ANSI Character Functions
  85. Various Common Character Functions
  86. Miscellaneous Character Functions
  87. Mathematical Functions
  88. Conversion Functions
  89. The Concept of Combining Character Functions
  90. Summary
  91. Q&amp;A
  92. Workshop
  93. Hour 12. Understanding Dates and Times
  94. How Is a Date Stored?
  95. Date Functions
  96. Date Conversions
  97. Summary
  98. Q&amp;A
  99. Workshop
  100. Part IV: Building Sophisticated Database Queries
  101. Hour 13. Joining Tables in Queries
  102. Selecting Data from Multiple Tables
  103. Types of Joins
  104. Join Considerations
  105. Summary
  106. Q&amp;A
  107. Workshop
  108. Hour 14. Using Subqueries to Define Unknown Data
  109. What Is a Subquery?
  110. Embedding a Subquery Within a Subquery
  111. Summary
  112. Q&A
  113. Workshop
  114. Hour 15. Combining Multiple Queries into One
  115. Single Queries Versus Compound Queries
  116. Why Would I Ever Want to Use a Compound Query?
  117. Compound Query Operators
  118. Using an <tt>ORDER BY</tt> with a Compound Query
  119. Using <tt>GROUP BY</tt> with a Compound Query
  120. Retrieving Accurate Data
  121. Summary
  122. Workshop
  123. Q&amp;A
  124. Part V: SQL Performance Tuning
  125. Hour 16. Using Indexes to Improve Performance
  126. What Is an Index?
  127. How Do Indexes Work?
  128. The <tt>CREATE INDEX</tt> Command
  129. Types of Indexes
  130. When Should Indexes Be Considered?
  131. When Should Indexes Be Avoided?
  132. Summary
  133. Q&amp;A
  134. Workshop
  135. Hour 17. Improving Database Performance
  136. What Is SQL Statement Tuning?
  137. Database Tuning Versus SQL Tuning
  138. Formatting Your SQL Statement
  139. Full Table Scans
  140. Other Performance Considerations
  141. Performance Tools
  142. Summary
  143. Q&amp;A
  144. Workshop
  145. Part VI: Using SQL to Manage Users and Security
  146. Hour 18. Managing Database Users
  147. Users Are the Reason
  148. The Management Process
  149. Tools Utilized by Database Users
  150. Summary
  151. Q&amp;A
  152. Workshop
  153. Hour 19. Managing Database Security
  154. What Is Database Security?
  155. How Does Security Differ from User Management?
  156. What Are Privileges?
  157. Controlling User Access
  158. Controlling Privileges Through Roles
  159. Summary
  160. Q&amp;A
  161. Workshop
  162. Part VII: Summarized Data Structures
  163. Hour 20. Creating and Using Views and Synonyms
  164. What Is a View?
  165. Creating Views
  166. Dropping a View
  167. What Is a Synonym?
  168. Summary
  169. Q&amp;A
  170. Workshop
  171. Hour 21. Working with the System Catalog
  172. What Is the System Catalog?
  173. How Is the System Catalog Created?
  174. What Is Contained in the System Catalog?
  175. Examples of System Catalog Tables by Implementation
  176. Querying the System Catalog
  177. Updating System Catalog Objects
  178. Summary
  179. Q&amp;A
  180. Workshop
  181. Part VIII: Applying SQL Fundamentals in Today's World
  182. Hour 22. Advanced SQL Topics
  183. Advanced Topics
  184. Cursors
  185. Stored Procedures and Functions
  186. Triggers
  187. Dynamic SQL
  188. Call-Level Interface
  189. Using SQL to Generate SQL
  190. Direct Versus Embedded SQL
  191. Summary
  192. Q&amp;A
  193. Workshop
  194. Hour 23. Extending SQL to the Enterprise, the Internet, and the Intranet
  195. SQL and the Enterprise
  196. Accessing a Remote Database
  197. Accessing a Remote Database Through a Web Interface
  198. SQL and the Internet
  199. SQL and the Intranet
  200. Summary
  201. Q&amp;A
  202. Workshop
  203. Hour 24. Extensions to Standard SQL
  204. Various Implementations
  205. Examples of Extensions from Some Implementations
  206. Interactive SQL Statements
  207. Summary
  208. Q&amp;A
  209. Workshop
  210. Part IX: Appendixes
  211. Appendix A. Common SQL Commands
  212. SQL Statements
  213. SQL Clauses
  214. Appendix B. Using MySQL for Exercises
  215. Windows Installation Instructions
  216. Linux Installation Instructions
  217. Appendix C. Answers to Quizzes and Exercises
  218. Hour 1, "Welcome to the World of SQL"
  219. Hour 2, "Defining Data Structures"
  220. Hour 3, "Managing Database Objects"
  221. Hour 4, "The Normalization Process"
  222. Hour 5, "Manipulating Data"
  223. Hour 6, "Managing Database Transactions"
  224. Hour 7, "Introduction to the Database Query"
  225. Hour 8, "Using Operators to Categorize Data"
  226. Hour 9, "Summarizing Data Results from a Query"
  227. Hour 10, "Sorting and Grouping Data"
  228. Hour 11, "Restructuring the Appearance of Data"
  229. Hour 12, "Understanding Dates and Time"
  230. Hour 13, "Joining Tables in Queries"
  231. Hour 14, "Using Subqueries to Define Unknown Data"
  232. Hour 15, "Combining Multiple Queries into One"
  233. Hour 16, "Using Indexes to Improve Performance"
  234. Hour 17, "Improving Database Performance"
  235. Hour 18, "Managing Database Users"
  236. Hour 19, "Managing Database Security"
  237. Hour 20, "Creating and Using Views and Synonyms"
  238. Hour 21, "Working with the System Catalog"
  239. Hour 22, "Advanced SQL Topics"
  240. Hour 23, "Extending SQL to the Enterprise, the Internet, and the Intranet"
  241. Hour 24, "Extensions to Standard SQL"
  242. Appendix D. <tt>CREATE TABLE</tt> Statements for Book Examples
  243. <tt>EMPLOYEE_TBL</tt>
  244. <tt>EMPLOYEE_PAY_TBL</tt>
  245. <tt>CUSTOMER_TBL</tt>
  246. <tt>ORDERS_TBL</tt>
  247. <tt>PRODUCTS_TBL</tt>
  248. Appendix E. <tt>INSERT</tt> Statements for Data in Book Examples
  249. <tt>INSERT</tt> Statements
  250. Appendix F. Glossary
  251. Appendix G. Bonus Exercises
Recommended Book

What Are Aggregate Functions?

newterm_icon.gif

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:

   input_icon.gif

   SELECT *

   FROM PRODUCTS_TBL;

   output_icon.gif
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:

   input_icon.gif

   SELECT EMP_ID, LAST_NAME, FIRST_NAME, PAGER

   FROM EMPLOYEE_TBL;

   output_icon.gif
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:

   syntax_icon.gif
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.

   input_icon.gif

   SELECT COUNT(*)

   FROM EMPLOYEE_TBL;

   output_icon.gif
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.

   input_icon.gif

   SELECT COUNT(EMP_ID)

   FROM EMPLOYEE_TBL;

   output_icon.gif
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.

   input_icon.gif

   SELECT COUNT(PAGER)

   FROM EMPLOYEE_TBL;

   output_icon.gif
COUNT(PAGER)
------------
           2

The ORDERS_TBL table, shown next, is used in the following COUNT example:

   input_icon.gif

   SELECT *

   FROM ORDERS_TBL;

   output_icon.gif
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.

   input_icon.gif

   SELECT COUNT(DISTINCT(PROD_ID))

   FROM ORDERS_TBL;

   output_icon.gif
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:

   syntax_icon.gif
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:

   input_icon.gif

   SELECT SUM(COST)

   FROM PRODUCTS_TBL;

   output_icon.gif
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:

   syntax_icon.gif
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:

   input_icon.gif

   SELECT AVG(COST)

   FROM PRODUCTS_TBL;

   output_icon.gif
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.

   input_icon.gif

   SELECT AVG(PAY_RATE), AVG(SALARY)

   FROM EMPLOYEE_PAY_TBL;

   output_icon.gif
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.

   syntax_icon.gif
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:

   input_icon.gif

   SELECT MAX(COST)

   FROM PRODUCTS_TBL;

   output_icon.gif
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.

   syntax_icon.gif
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:

   input_icon.gif

   SELECT MIN(COST)

   FROM PRODUCTS_TBL;

   output_icon.gif
MIN(COST)
----------
     1.05

The final example combines aggregate functions with the use of arithmetic operators:

   input_icon.gif

   SELECT COUNT(ORD_NUM), SUM(QTY),
       
   SUM(QTY) / COUNT(ORD_NUM) AVG_QTY

   FROM ORDERS_TBL;

   output_icon.gif
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.

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