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Modern Database Management Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden SQL

SQL Is: Structured Query Language The standard for relational database management systems (RDBMS) SQL-92 Standard -- Purpose: 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

Benefits of a Standardized Relational Language Reduced training costs Productivity Application portability Application longevity Reduced dependence on a single vendor Cross-system communication

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

Figure 7-1: A simplified schematic of a typical SQL environment, as described by the SQL-92 standard

SQL Data types (from Oracle8) String types CHAR(n) – fixed-length character data, n characters long Maximum length = 2000 bytes VARCHAR2(n) – variable length character data, maximum 4000 bytes LONG – variable-length character data, up to 4GB. Maximum 1 per table Numeric types NUMBER(p,q) – general purpose numeric data type INTEGER(p) – signed integer, p digits wide FLOAT(p) – floating point in scientific notation with p binary digits precision Date/time type DATE – fixed-length date/time in dd-mm-yy form

Figure 7-4: DDL, DML, DCL, and the database development process

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

Table Creation Figure 7-5: General syntax for CREATE TABLE Steps in table creation: Identify data types for attributes Identify columns that can and cannot be null Identify columns that must be unique (candidate keys) Identify primary key-foreign key mates Determine default values Identify constraints on columns (domain specifications) Create the table and associated indexes

Figure 7-3: Sample Pine Valley Furniture data customers orders order lines products

Figure 7-6: SQL database definition commands for Pine Valley Furniture

Figure 7-6: SQL database definition commands for Pine Valley Furniture Defining attributes and their data types

Figure 7-6: SQL database definition commands for Pine Valley Furniture Non-nullable specifications Note: primary keys should not be null

Figure 7-6: SQL database definition commands for Pine Valley Furniture Identifying primary keys This is a composite primary key

Figure 7-6: SQL database definition commands for Pine Valley Furniture Identifying foreign keys and establishing relationships

Figure 7-6: SQL database definition commands for Pine Valley Furniture Default values and domain constraints

Figure 7-6: SQL database definition commands for Pine Valley Furniture Overall table definitions

Using and Defining Views Views provide users controlled access to tables Advantages of views: Simplify query commands Provide data security Enhance programming productivity CREATE VIEW command

View Terminology Base Table A 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

Sample CREATE VIEW CREATE 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 name View is based on a SELECT statement CHECK_OPTION works only for updateable views and prevents updates that would create rows not included in the view

Table 7-2: Pros and Cons of Using Dynamic Views

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

Figure 7-7: Ensuring data integrity through updates

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

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

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’;

Delete Statement Removes rows from a table Delete certain rows DELETE FROM CUSTOMER_T WHERE STATE = ‘HI’; Delete all rows DELETE FROM CUSTOMER_T;

Update Statement Modifies data in existing rows UPDATE PRODUCT_T SET UNIT_PRICE = 775 WHERE PRODUCT_ID = 7;

The 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

Figure 7-8: SQL statement processing order (adapted from van der Lans, p.100)

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

SELECT Example with 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’;

SELECT Example Using a Function Using the COUNT aggregate function to find totals SELECT COUNT(*) FROM ORDER_LINE_V WHERE ORDER_ID = 1004; Note: with aggregate functions you can’t have single-valued columns included in the SELECT clause

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

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

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

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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