Chapter 6 © 2013 Pearson Education, Inc. Publishing as Prentice Hall 1 Modern Database Management...

Chapter 6 © 2013 Pearson Education, Inc. Publishing as Prentice Hall1

CHAPTER 6:INTRODUCTION TO SQL

Modern Database Management11th Edition

Jeffrey A. Hoffer, V. Ramesh, Heikki Topi

First class:Slides # 6, 8,

9, 33~53


OBJECTIVES

Define terms Define a database using SQL data

definition language Write single table queries using SQL Establish referential integrity using

SQL


SQL OVERVIEW

Structured Query Language

The standard for relational database management systems (RDBMS)

RDBMS: A database management system that manages data as a collection of tables in which all relationships are represented by common values in related tables


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


PURPOSE OF SQL STANDARD

Specify syntax/semantics for data definition and manipulation

Define data structures and basic operations

Enable portability of database definition and application modules

Specify minimal (level 1) and complete (level 2) standards

Allow for later growth/enhancement to standard


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) – our focus Commands that maintain and query a database

Data Control Language (DCL) Commands that control a database, including

administering privileges and committing data

Chapter 6 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Figure 6-1A simplified schematic of a typical SQL environment, as described by the SQL: 200n standard

7


FIG 6-2 QUERY SYNTAX

123456

SELECT Major, AVERAGE(GPA) , ExpGraduateYr WHERE ExpGraduateYr = “2015” GROUP BY Major HAVING AVERAGE(GPA) >=3.0 ORDER BY Major

8

Just a quick example to show

sequence of clauses

Display order can be specified here

Cond for

records

Cond for

groups

Sequence!


SQL DATA TYPES


Figure 6-4 DDL, DML, DCL, and the database development process

10


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 new table and its columns

CREATE VIEW–defines a logical table from one or more tables or views

Other CREATE statements: CHARACTER SET, COLLATION, TRANSLATION, ASSERTION, DOMAIN


TABLE CREATIONFigure 6-5 General syntax for CREATE TABLE statement used in data definition language

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


THE FOLLOWING SLIDES CREATE TABLES FOR THIS ENTERPRISE DATA MODEL

(from Chapter 1, Figure 1-3)

13


FIGURE 6-3: SAMPLE PVFC DATA

14

14


Figure 6-6 SQL database definition commands for Pine Valley Furniture Company (Oracle 11g)

Overall table definitions


Defining attributes and their data types

Constraint name Cnstr Type Field

16

Primary keys can never have NULL values


Non-nullable specifications

composite key

Some primary keys are composite– composed of multiple attributes

17


Default value

Domain constraint

Controlling the values in attributes18

validation rule


Primary key of parent table

Identifying foreign keys and establishing relationships

Foreign key of dependent table

Constraint name Cnstrnt Type Field REFERENCES Tbl(Key)


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


Relational integrity is enforced via the primary-key to foreign-key match

Figure 6-7 Ensuring data integrity through updates


CHANGING TABLES ALTER TABLE statement allows you to

change column specifications:

Table Actions:

Example (adding a new column with a default

value):

ADD COLUMN Name Type Default


REMOVING TABLES

DROP TABLE statement allows you to remove tables from your schema:

DROP TABLE CUSTOMER_T


INSERT STATEMENT

Adds one or more rows to a table Inserting into a table

VALUE – “s” Sequence!!! Inserting a record that has some null

attributes requires identifying the fields that actually get data

(field-list) VALUES (value-list)

INSERT INTO Table VALUES (value-list)


INSERT STATEMENT (CONT)

Inserting from another table A SUBSET from another table

Interpretation?


DELETE STATEMENT

Removes rows from a table Delete certain rows

DELETE FROM CUSTOMER_T WHERE CUSTOMERSTATE = ‘HI’;

Delete all rows DELETE FROM CUSTOMER_T; Careful!!!


UPDATE STATEMENT

Modifies data in existing rows

Note: the WHERE clause may be a subquery (Chap 7)

UPDATE Table-nameSET Attribute = ValueWHERE Criteria-to-apply-the-update


COMPARISON OF ALTER, INSERT, AND UPDATE ALTER: changing the columns of the

table ALTER TABLE CUSTOMER_T ADD field…

INSERT: adding records based on the existing table INSERT INTO CUSTOME_T VALUES (… )

UPDATE: changing the values of some fields in existing records UPDATE CUSTOMER_T SET field = value …

WHERE…28


RECAP OF “THREE MODIFICATIONS”

29

KEY WORD /Change

what

SYNTAX / Example

ALTER:

INSERT:

UPDATE:

29


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

Inte

rpre

tati

on

?

30


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(CUSTOMERNAME)

This makes an index for the CUSTOMERNAME field of the CUSTOMER_T table


QUERY: SELECT STATEMENT

This is the CENTER section of the chapter, and the CENTER of this course

Many slides contain multiple points of concepts/skills – do NOT miss the text, and play attention to color code 32


SELECT EXAMPLE Find products with standard price less

than $275

Table 6-3: Comparison Operators in SQL

Every SESLECT statement returns a result table - Can be used as part of another querySELECT DISTINCT: no duplicate rowsSELECT * : all columns selected


SELECT EXAMPLE USING ALIAS

Alias is an alternative column or table name

SELECT CUST.CUSTOMER_NAME AS NAME, CUST.CUSTOMER_ADDRESS

FROM CUSTOMER_V [AS] CUSTWHERE NAME = ‘Home

Furnishings’;

Note1: Specifying source table, P. 262Note2: Use of alias, P. 263

The two alias have very different usage …


USING EXPRESSIONS (PP. 263-264)

P. 264 ProductStandardPrice*1.1 AS

Plus10Percent What does it look like which contents

we learned in Access (in IS 312)? Observe the result on P. 264

35

35


SELECT EXAMPLE USING A FUNCTION Using the COUNT aggregate function

to find totals

SELECT COUNT(*) FROM ORDERLINE_TWHERE ORDERID = 1004;

Note 1: SELECT PRODUCT_ID, COUNT(*)

FROM ORDER_LINE_V

WHERE ORDER_ID = 1004;Note 2: COUNT (*) and COUNT - different

More functions:P. 264~5

P. 265

More exmpl:P. 266


SELECT EXAMPLE USING FUNCTIONS What is average standard price for

each [examine!!] product in inventory?

SELECT AVG (STANDARD_PRICE) AS AVERAGE FROM PROCUCT_V;

AVG, COUNT, MAX, MIN, SUM; LN, EXP, POWER, SQRT More functions – P. 264 **Issues about set value and aggregates:

265-6** Wildcards: 267, top Null values, 268, top


ISSUES ABOUT SET VALUE AND AGGREGATES

Last line on P. 265: SQL cannot return both a row value (such as ID) and a set value (such as COUNT/AVG/SUM of a group); users must run two separate queries, one

that returns row info and one that returns set info

1. SELECT S_ID FROM STUDENT √2. SELECT AVG(GPA) FROM STUDENT √3. SELECT S_ID, AVG(GPA) FROM

STUDENT Χ38

One of the above is not legitimate

38


SELECT EXAMPLE–BOOLEAN OPERATORS AND, OR, and NOT Operators for customizing

conditions in WHERE clause

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.


Figure 6-9 Boolean query without use of parentheses

40


SELECT EXAMPLE–BOOLEAN OPERATORS With parentheses…these override the normal

precedence of Boolean operators

Note: by default, the AND operator takes precedence over the OR operator. With parentheses, you can make the OR take place before the AND.


Figure 6-9 Boolean query with use of parentheses

42


SELECT EXAMPLE– USING DISTINCT VALUES Compare:

SELECT ORDER_IDFROM ORDER_LINE_V;

and –

SELECT DISTINCT ORDER_IDFROM ORDER_LINE_V;

But:SELECT DISTINCT ORDER_ID, ORDER_QUANTITY

FROM ORDER_LINE_V;Compare three figures on 272 & 273


SELECT EXAMPLE – SORTING RESULTS WITH THE ORDER BY CLAUSE

Sort the results first by STATE, and then within a state by the CUSTOMER NAME

Note: the IN operator in this example allows you to include rows whose CustomerState value is either FL, TX, CA, or HI. It is more efficient than separate OR conditions.The opposite is NOT IN

Can order by multiple fields: primary sort, 2ndry sort, …


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 – those in PP. 264~266 (aggregate of whole table)

Vector aggregate: multiple values returned from SQL query with aggregate function (via GROUP BY)

P. 275 near bottom:Each column referenced in the SELECT statement

must be referenced in the GROUP BY clause; if not, it must be used as an argument for an aggregate

Fn

One value for a group “vector”

Result: P. 275


SINGLE-VALUE FIELDS VS AGGREGATE FUNCTIONS

1. SELECT S_ID FROM STUDENT2. SELECT MAJOR, AVG(GPA) FROM

STUDENT GROUP BY MAJOR3. SELECT S_ID, AVG(GPA) FROM

STUDENT GROUP BY MAJOR4. SELECT COUNT(S_ID), AVG(GPA) FROM

STUDENT GROUP BY MAJOR

46

One of the above is not legitimate. Compare Slide

#39

46


SELECT EXAMPLE– QUALIFYING RESULTS BY CATEGORIES USING THE HAVING CLAUSE

For use with GROUP BY

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.

See 275-276


SELECT STATEMENT WITH GROUP BY

Used for queries on single or multiple tables Clauses of the SELECT statement:

(Sequence!!) 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 criteriaRead closely PP. 275-276

Can have multiple sorts –

primary, 2ndry, …


Figure 6-10 SQL statement processing order (adapted from van der Lans, 2006 p.100)


The HAVING clause acts like a WHERE clause, but it identifies groups that meet a criterion, rather than rows. Therefore, you will usually see a HAVING

clause … WHERE qualifies a set of rows,

while HAVING qualifies a set of …

HAVING clause

Interpretation: example on P. 276


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


SAMPLE CREATE VIEW

View has a nameView 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


IS NULL: the field has a null value – not 0, not space (_), but no value

Useful in scenarios as follows: Sold_date in a real estate property table Paid_date in a student registration table Transaction_amount in a auction table …

Syntax: WHERE field_name IS NULL The opposite is: WHERE field_name IS NOT

NULL

IS NULL


Syntax of CREATE VIEW: CREATE VIEW view-name AS

SELECT (that provides the rows and columns of the view)

Example: CREATE VIEW ORDER_TOTALS_V AS

SELECT PRODUCT_ID PRODUCT,SUM(STANDARD_PRICE*QUANTITY)

TOTALFROM INVOICE_V

GROUP BY PRODUCT_ID;

Views


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


DISADVANTAGES OF VIEWS

Use processing time each time view is referenced

May or may not be directly updateable

Chapter 6 © 2013 Pearson Education, Inc. Publishing as Prentice Hall 1 Modern Database Management...

Documents

Transcript of Chapter 6 © 2013 Pearson Education, Inc. Publishing as Prentice Hall 1 Modern Database Management...