chapter 7 1 chapter 7: introduction to sql modern database management jeffrey a. hoffer, mary b....
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1Chapter 7
Chapter 7:Introduction to SQL
Modern Database Management
Jeffrey A. Hoffer, Mary B. Prescott,
Fred R. McFadden
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3Chapter 7
The Physical Design Stage of SDLC (Figures 2-4, 2-5 revisited)
Purpose –programming, testing, training, installation, documentingDeliverable – operational programs, documentation, training materials, program/data structures
Database activity – physical database design anddatabase implementation
Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design
Physical Design
Implementation
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4Chapter 7
History of SQL• 1970–E. Codd develops relational database
concept• 1974-1979–System R with Sequel (later SQL)
created at IBM Research Lab• 1979–Oracle markets first relational DB with SQL• 1986–ANSI SQL standard released• 1989, 1992, 1999, 2003–Major ANSI standard
updates• Current–SQL is supported by most major database
vendors
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5Chapter 7
Purpose of SQL Standard• Specify syntax/semantics for data definition
and manipulation• Define data structures• Enable portability• Specify minimal (level 1) and complete (level
2) standards• Allow for later growth/enhancement to
standard
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6Chapter 7
Benefits of a Standardized Relational Language
• Reduced training costs
• Productivity
• Application portability
• Application longevity
• Reduced dependence on a single vendor
• Cross-system communication
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7Chapter 7
SQL Environment• Catalog
– A set of schemas that constitute the description of a database
• Schema– The structure that contains descriptions of objects created by a user (base
tables, views, constraints)
• Data Definition Language (DDL)– Commands that define a database, including creating, altering, and
dropping tables and establishing constraints
• Data Manipulation Language (DML)– Commands that maintain and query a database
• Data Control Language (DCL)– Commands that control a database, including administering privileges and
committing data
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8Chapter 7
SQL Environment• Catalog
– A set of schemas that constitute the description of a database
• Schema– The structure that contains descriptions of objects created by a user (base
tables, views, constraints)
• Data Definition Language (DDL)– Commands that define a database, including creating, altering, and
dropping tables and establishing constraints
• Data Manipulation Language (DML)– Commands that maintain and query a database
• Data Control Language (DCL)– Commands that control a database, including administering privileges and
committing data
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9Chapter 7
Figure 7-1A simplified schematic of a typical SQL environment, as described by the SQL-2003 standard
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10Chapter 7
Some SQL Data types
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11Chapter 7
Figure 7-4 DDL, DML, DCL, and the database development process
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12Chapter 7
SQL Database Definition• Data Definition Language (DDL)• Major CREATE statements:
– CREATE SCHEMA – defines a portion of the database owned by a particular user
– CREATE TABLE – defines a table and its columns– CREATE VIEW – defines a logical table from one
or more views
• Other CREATE statements: CHARACTER SET, COLLATION, TRANSLATION, ASSERTION, DOMAIN
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13Chapter 7
Table CreationFigure 7-5: General syntax for CREATE TABLE
Steps in table creation:
1. Identify data types for attributes
2. Identify columns that can and cannot be null
3. Identify columns that must be unique (candidate keys)
4. Identify primary key-foreign key mates
5. Determine default values
6. Identify constraints on columns (domain specifications)
7. Create the table and associated indexes
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14Chapter 7
The following slides create tables for this enterprise data model
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15Chapter 7
Figure 7-6 SQL database definition commands for Pine Valley Furniture
Overall table definitions
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16Chapter 7
Defining attributes and their data types
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17Chapter 7
Non-nullable specification
Identifying primary key
Primary keys can never have NULL values
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18Chapter 7
Non-nullable specifications
Primary key
Some primary keys are composite– composed of multiple attributes
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19Chapter 7
Default value
Domain constraint
Controlling the values in attributes
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20Chapter 7
Primary key of parent table
Identifying foreign keys and establishing relationships
Foreign key of dependent table
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21Chapter 7
Unique Parameters for MySQL
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] tbl_name [(] LIKE old_tbl_name [)]; create_definition: column_definition | [CONSTRAINT [symbol]] PRIMARY KEY [index_type] (index_col_name,...) | KEY [index_name] [index_type] (index_col_name,...) | INDEX [index_name] [index_type] (index_col_name,...) | [CONSTRAINT [symbol]] UNIQUE [INDEX] [index_name] [index_type] (index_col_name,...) | [FULLTEXT|SPATIAL] [INDEX] [index_name] (index_col_name,...) | [CONSTRAINT [symbol]] FOREIGN KEY [index_name] (index_col_name,...) [reference_definition] | CHECK (expr) column_definition: col_name type [NOT NULL | NULL] [DEFAULT default_value] [AUTO_INCREMENT] [[PRIMARY] KEY] [COMMENT 'string'] [reference_definition]
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22Chapter 7
For example…
Client table: create table client ( clientID int not null auto_increment primary key, Name varchar(40), Address varchar(100), contactPerson varchar(80), contactNumer char(12) ) type=InnoDB
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23Chapter 7
Data Integrity Controls
• Referential integrity – constraint that ensures that foreign key values of a table must match primary key values of a related table in 1:M relationships
• Restricting:– Deletes of primary records– Updates of primary records– Inserts of dependent records
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24Chapter 7
Relational integrity is enforced via the primary-key to foreign-key match
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25Chapter 7
Changing and Removing Tables
• ALTER TABLE statement allows you to change column specifications:– ALTER TABLE CUSTOMER_T ADD (TYPE
VARCHAR(2))
• DROP TABLE statement allows you to remove tables from your schema:– DROP TABLE CUSTOMER_T
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26Chapter 7
Schema Definition• Control processing/storage efficiency:
– Choice of indexes– File organizations for base tables– File organizations for indexes– Data clustering– Statistics maintenance
• Creating indexes– Speed up random/sequential access to base table data– Example
• CREATE INDEX NAME_IDX ON CUSTOMER_T(CUSTOMER_NAME)
• This makes an index for the CUSTOMER_NAME field of the CUSTOMER_T table
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27Chapter 7
Insert Statement• Adds data to a table• Inserting into a table
– INSERT INTO CUSTOMER_T VALUES (001, ‘Contemporary Casuals’, ‘1355 S. Himes Blvd.’, ‘Gainesville’, ‘FL’, 32601);
• Inserting a record that has some null attributes requires identifying the fields that actually get data– INSERT INTO PRODUCT_T (PRODUCT_ID,
PRODUCT_DESCRIPTION,PRODUCT_FINISH, STANDARD_PRICE, PRODUCT_ON_HAND) VALUES (1, ‘End Table’, ‘Cherry’, 175, 8);
• Inserting from another table– INSERT INTO CA_CUSTOMER_T SELECT * FROM CUSTOMER_T
WHERE STATE = ‘CA’;
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28Chapter 7
Creating Tables with Identity Columns
Inserting into a table does not require explicit customer ID Inserting into a table does not require explicit customer ID entry or field listentry or field list
INSERT INTO CUSTOMER_T VALUES ( ‘Contemporary INSERT INTO CUSTOMER_T VALUES ( ‘Contemporary Casuals’, ‘1355 S. Himes Blvd.’, ‘Gainesville’, ‘FL’, 32601);Casuals’, ‘1355 S. Himes Blvd.’, ‘Gainesville’, ‘FL’, 32601);
New with SQL:2003
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29Chapter 7
Delete Statement
• Removes rows from a table
• Delete certain rows– DELETE FROM CUSTOMER_T WHERE
STATE = ‘HI’;
• Delete all rows– DELETE FROM CUSTOMER_T;
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30Chapter 7
Update Statement
• Modifies data in existing rows
• UPDATE PRODUCT_T SET UNIT_PRICE = 775 WHERE PRODUCT_ID = 7;
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31Chapter 7
Merge Statement
Makes it easier to update a table…allows combination of Insert and Update in one statement
Useful for updating master tables with new data
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32Chapter 7
SELECT Statement• Used for queries on single or multiple tables• Clauses of the SELECT statement:
– SELECT• List the columns (and expressions) that should be returned from the query
– FROM• Indicate the table(s) or view(s) from which data will be obtained
– WHERE• Indicate the conditions under which a row will be included in the result
– GROUP BY• Indicate categorization of results
– HAVING• Indicate the conditions under which a category (group) will be included
– ORDER BY• Sorts the result according to specified criteria
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33Chapter 7
Figure 7-10 SQL statement processing order (adapted from van der Lans, p.100)
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34Chapter 7
SELECT Example
• Find products with standard price less than $275
– SELECT PRODUCT_NAME, STANDARD_PRICE – FROM PRODUCT_V – WHERE STANDARD_PRICE < 275;
Table 7-3: Comparison Operators in SQL
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35Chapter 7
SELECT Example using Alias
• Alias is an alternative column or table name
SELECT CUST.CUSTOMER AS NAME, CUST.CUSTOMER_ADDRESS
FROM CUSTOMER_V CUST
WHERE NAME = ‘Home Furnishings’;
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36Chapter 7
SELECT Example Using a Function
• Using the COUNT aggregate function to find totals
SELECT COUNT(*) FROM ORDER_LINE_VWHERE ORDER_ID = 1004;
Note: with aggregate functions you can’t have single-valued columns included in the SELECT clause
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37Chapter 7
SELECT Example – Boolean Operators• AND, OR, and NOT Operators for customizing
conditions in WHERE clause
SELECT PRODUCT_DESCRIPTION, PRODUCT_FINISH, STANDARD_PRICE
FROM PRODUCT_V
WHERE (PRODUCT_DESCRIPTION LIKE ‘%Desk’
OR PRODUCT_DESCRIPTION LIKE ‘%Table’)
AND UNIT_PRICE > 300;
Note: the LIKE operator allows you to compare strings using wildcards. For example, the % wildcard in ‘%Desk’ indicates that all strings that have any number of characters preceding the word “Desk” will be allowed
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38Chapter 7
Venn Diagram from Previous Query
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39Chapter 7
SELECT Example – Sorting Results with the ORDER BY Clause
• Sort the results first by STATE, and within a state by CUSTOMER_NAME
SELECT CUSTOMER_NAME, CITY, STATE
FROM CUSTOMER_V
WHERE STATE IN (‘FL’, ‘TX’, ‘CA’, ‘HI’)
ORDER BY STATE, CUSTOMER_NAME;
Note: the IN operator in this example allows you to include rows whose STATE value is either FL, TX, CA, or HI. It is more efficient than separate OR conditions
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40Chapter 7
SELECT Example – Categorizing Results Using the GROUP BY Clause
• For use with aggregate functions– Scalar aggregate: single value returned from SQL query with
aggregate function– Vector aggregate: multiple values returned from SQL query with
aggregate function (via GROUP BY)
SELECT STATE, COUNT(STATE)
FROM CUSTOMER_V
GROUP BY STATE;
Note: you can use single-value fields with aggregate functions if they are included in the GROUP BY clause
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41Chapter 7
SELECT Example – Qualifying Results by Categories
Using the HAVING Clause
• For use with GROUP BY
SELECT STATE, COUNT(STATE)
FROM CUSTOMER_V
GROUP BY STATE
HAVING COUNT(STATE) > 1;
Like a WHERE clause, but it operates on groups (categories), not on individual rows. Here, only those groups with total numbers greater than 1 will be included in final result
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42Chapter 7
Figure 7-8: SQL statement processing order (adapted from van der Lans, p.100)
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43Chapter 7
Using and Defining Views• Views provide users controlled access to tables• Base Table – table containing the raw data• Dynamic View
– A “virtual table” created dynamically upon request by a user – No data actually stored; instead data from base table made available
to user– Based on SQL SELECT statement on base tables or other views
• Materialized View– Copy or replication of data– Data actually stored– Must be refreshed periodically to match the corresponding base tables
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44Chapter 7
Sample CREATE VIEWCREATE VIEW EXPENSIVE_STUFF_V AS
SELECT PRODUCT_ID, PRODUCT_NAME, UNIT_PRICE
FROM PRODUCT_T
WHERE UNIT_PRICE >300
WITH CHECK_OPTION;
View has a nameView is based on a SELECT statementCHECK_OPTION works only for updateable views and prevents updates that would create rows not included in the view
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45Chapter 7
Advantages of Views• Simplify query commands• Assist with data security (but don't rely on views
for security, there are more important security measures)
• Enhance programming productivity• Contain most current base table data• Use little storage space• Provide customized view for user• Establish physical data independence
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46Chapter 7
Disadvantages of Views
• Use processing time each time view is referenced
• May or may not be directly updateable