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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

Logical Operators

newterm_icon.gif

Logical operators are those operators that use SQL keywords to make compar-isons instead of symbols. The logical operators covered in the following subsections are

  • IS NULL
  • BETWEEN
  • IN
  • LIKE
  • EXISTS
  • UNIQUE
  • ALL and ANY

IS NULL

The NULL operator is used to compare a value with a NULL value. For example, you might look for employees who do not have a pager by searching for NULL values in the PAGER column of the EMPLOYEE_TBL table.

The following example shows comparing a value to a NULL value:

Example

Meaning

WHERE SALARY IS NULL

Salary has no value

The following example does not find a NULL value:

Example

Meaning

WHERE SALARY = NULL

Salary has a value containing the letters N-U-L-L

   input_icon.gif

   SELECT EMP_ID, LAST_NAME, FIRST_NAME, PAGER

   FROM EMPLOYEE_TBL

   WHERE PAGER IS NULL;

   output_icon.gif
EMP_ID    LAST_NAM FIRST_NA PAGER
--------- -------- -------- -----
311549902 STEPHENS TINA
442346889 PLEW     LINDA
220984332 WALLACE  MARIAH
443679012 SPURGEON TIFFANY

4 rows selected.

Understand that the literal word "null" is different than a NULL value. Examine the following example:

   input_icon.gif

   SELECT EMP_ID, LAST_NAME, FIRST_NAME, PAGER

   FROM EMPLOYEE_TBL

   WHERE PAGER = NULL;

   output_icon.gif
no rows selected.

BETWEEN

The BETWEEN operator is used to search for values that are within a set of values, given the minimum value and the maximum value. The minimum and maximum values are included as part of the conditional set.

Example

Meaning

WHERE SALARY BETWEEN '20000' AND '30000'

The salary must fall between 20000 and 30000, including the values 20000 and 30000

   input_icon.gif

   SELECT *

   FROM PRODUCTS_TBL

   WHERE COST BETWEEN 5.95 AND 14.5;

   output_icon.gif
PROD_ID    PROD_DESC                       COST
---------- ------------------------------ ------
222        PLASTIC PUMPKIN 18 INCH         7.75
90         LIGHTED LANTERNS               14.5
15         ASSORTED COSTUMES              10
1234       KEY CHAIN                       5.95

4 rows selected.

Notice that the values 5.95 and 14.5 are included in the output.

IN

The IN operator is used to compare a value to a list of literal values that have been specified. For TRUE to be returned, the compared value must match at least one of the values in the list.

Examples

Meaning

WHERE SALARY IN('20000', '30000', '40000')

The salary must match one of the values 20000, 30000, or 40000

   input_icon.gif

   SELECT *

   FROM PRODUCTS_TBL

   WHERE PROD_ID IN ('13','9','87','119');

   output_icon.gif
PROD_ID    PROD_DESC                       COST
---------- ------------------------------ ------
119        ASSORTED MASKS                  4.95
87         PLASTIC SPIDERS                 1.05
9          CANDY CORN                      1.35
13         FALSE PARAFFIN TEETH            1.1

4 rows selected.

Using the IN operator can achieve the same results as using the OR operator and can return the results more quickly.

LIKE

The LIKE operator is used to compare a value to similar values using wildcard operators. There are two wildcards used in conjunction with the LIKE operator:

  • The percent sign (%)
  • The underscore (_)

The percent sign represents zero, one, or multiple characters. The underscore represents a single number or character. The symbols can be used in combinations.

Examples are

WHERE SALARY LIKE '200%'

Finds any values that start with 200

WHERE SALARY LIKE '%200%'

Finds any values that have 200 in any position

WHERE SALARY LIKE '_00%'

Finds any values that have 00 in the second and third positions

WHERE SALARY LIKE '2_%_%'

Finds any values that start with 2 and are at least three characters in length

WHERE SALARY LIKE '%2'

Finds any values that end with 2

WHERE SALARY LIKE '_2%3'

Finds any values that have a 2 in the second position and end with a 3

WHERE SALARY LIKE '2___3'

Finds any values in a five-digit number that start with 2 and end with 3

The following example shows all product descriptions that end with the letter S in uppercase:

   input_icon.gif

   SELECT PROD_DESC

   FROM PRODUCTS_TBL

   WHERE PROD_DESC LIKE '%S';

   output_icon.gif
PROD_DESC
------------------
LIGHTED LANTERNS
ASSORTED COSTUMES
PLASTIC SPIDERS
ASSORTED MASKS

4 rows selected.

The following example shows all product descriptions whose second character is the letter S in uppercase:

   input_icon.gif

   SELECT PROD_DESC

   FROM PRODUCTS_TBL

   WHERE PROD_DESC LIKE '_S%';

   output_icon.gif
PROD_DESC
------------------
ASSORTED COSTUMES
ASSORTED MASKS

2 rows selected.

EXISTS

The EXISTS operator is used to search for the presence of a row in a specified table that meets certain criteria.

Example

Meaning

WHERE EXISTS (SELECT EMP_ID FROM EMPLOYEE_TBL WHERE EMPLOYEE_ID = '333333333')

Searching to see whether the EMP_ID 3333333333 is in the EMPLOYEE_TBL

The following example is a form of a subquery, which is further discussed during Hour 14, "Using Subqueries to Define Unknown Data."

   mysql_icon.gif
   input_icon.gif

   SELECT COST

   FROM PRODUCTS_TBL

   WHERE EXISTS ( SELECT COST
               
   FROM PRODUCTS_TBL
               
   WHERE COST > 100 );

   output_icon.gif
No rows selected.

----------

There were no rows selected because no records existed where the cost was greater than 100.

Consider the following example:

   mysql_icon.gif
   input_icon.gif

   SELECT COST

   FROM PRODUCTS_TBL

   WHERE EXISTS ( SELECT COST
               
   FROM PRODUCTS_TBL
               
   WHERE COST < 100 );

   output_icon.gif
COST
----------
     29.99
      7.75
       1.1
      14.5
        10
      1.35
      1.45
      1.05
      4.95
      5.95
     59.99

11 rows selected.

The cost was displayed for records in the table because records existed where the product cost was less than 100.

UNIQUE

The UNIQUE operator searches every row of a specified table for uniqueness (no duplicates).

Example

Meaning

WHERE UNIQUE (SELECT SALARY FROM EMPLOYEE_TBL WHERE EMPLOYEE_ID = '333333333')

Testing SALARY to see whether there are duplicates

ALL and ANY Operators

The ALL operator is used to compare a value to all values in another value set.

Example

Meaning

WHERE SALARY > ALL SALARY (SELECT FROM EMPLOYEE_TBL WHERE CITY = 'INDIANAPOLIS')

Testing SALARY to see whether it is greater than all salaries of the employees living in Indianapolis

   mysql_icon.gif
   input_icon.gif

   SELECT *

   FROM PRODUCTS_TBL

   WHERE COST > ALL ( SELECT COST
                   
   FROM PRODUCTS_TBL
                   
   WHERE COST < 10 );

   output_icon.gif
PROD_ID    PROD_DESC                       COST
---------- ------------------------------ ------
11235      WITCHES COSTUME                29.99
90         LIGHTED LANTERNS               14.5
15         ASSORTED COSTUMES              10
2345       OAK BOOKSHELF                  59.99

4 rows selected.

In this output, there were five records that had a cost greater than the cost of all records having a cost less than 10.

The ANY operator is used to compare a value to any applicable value in the list according to the condition.

Example

Meaning

WHERE SALARY > ANY (SELECT SALARY FROM EMPLOYEE_TBL WHERE CITY = 'INDIANAPOLIS')

Testing SALARY to see whether it is greater than any of the salaries of employees living in Indianapolis

   mysql_icon.gif
   input_icon.gif

   SELECT *

   FROM PRODUCTS_TBL

   WHERE COST > ANY ( SELECT COST
                   
   FROM PRODUCTS_TBL
                   
   WHERE COST < 10 );

   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
119        ASSORTED MASKS                  4.95
1234       KEY CHAIN                       5.95
2345       OAK BOOKSHELF                  59.99

10 rows selected.

In this output, more records were returned than when using ALL, because the cost only had to be greater than any of the costs that were less than 10. The one record that was not displayed had a cost of 1.05, which was not greater than any of the values less than 10 (which was, in fact, 1.05) .

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