1
Week 4
September 19
• Relational Data Model
• Views
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Relational Model
2
• Objectives
– A degree of data independence
– Address data semantic, consistency and redundancy
problems
– Set-oriented data manipulation language
• Structured Query Language (SQL)
Database Criteria
Data Set
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Presentation
method
Information
Data Set
Presentation
method
3
Information
Criteria
Data Set
Presentation
method
Information
Database
Data Set
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Presentation
method
Information
Domain = all values an attribute can assume
4
Entity
Tuples (rows)
• Cardinalitiy =
number of
tuples
Relation
Attributes (columns)
• Degree of a relation = number of attributes
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Domain of an Attribute
5
• Set of allowable values for one or more attributes
Attribute 1
Domain
Attribute 2
Union
or
Intersection
Information
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Domain
Properties of Relations
• Distinct (i.e., unique) relation name
• Each cell contains exactly one atomic (single) value
– No repeating groups
• Distinct attribute name
• The values of an attribute come from the same domain
• Order of attributes has no significance
• Each tuple is distinct (i.e., unique)
– No duplicate tuples
• Order of tuples has no significance
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
6
Unique Identification of a Relation
Relation
key
Superkey
Candidate key
Primary key
Foreign key
?
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
7
Identifying a Tuple
• Superkey
An attribute or a set of attributes that uniquely identifies a
tuple within a relation
• Candidate key
A super key such that no proper subset is a superkey within
the relation
– Uniquely identifies the tuple (uniqueness)
– Contains no unique subset (irreducibility)
• Primary key
The candidate key that is selected to identify tuples
uniquely within a relation
– Should remain constant over the life of the tuple
– Most efficient way of identifying a tuple
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
8
Finding the Primary Key
Super Key
Candidate Key
Primary key
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
9
Keys
10
CDs Relation
129341 Help!
Beatles
Columbia
1-29150-8384-0
129342 Hard Day’s Night
Beatles
Columbia
1-29150-7115-0
129343 Sergeant Pepper’s
Beatles
Columbia
1-29150-2484-0
129344 Magical Mystery Tour
Beatles
Columbia
1-29150-7515-0
129345 Abbey Road
Beatles
Apple
1-15700-9510-0
Attributes
• Catalog number
• Record title
• Artist name
• Record label
• UPC
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Superkey?
Candidate key?
Primary key?
Selecting a Key
• Criteria
– An efficient way of identifying an entity
– The attribute (value) remains constant over the life of
the entity
• Never changes
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
11
Identifying a Tuple
12
• Foreign key
An attribute or set of attributes within one relation that
matches the candidate key of some (possibly the same)
relation
Relation
key
foreign key
Relation
key
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Foreign Key
13
CDs Relation
129341 Help!
Beatles
COL
1-29150-8384-0
129342 Hard Day’s Night
Beatles
COL
1-29150-7115-0
129343 Sergeant Pepper’s
Beatles
COL
1-29150-2484-0
129344 Magical Mystery Tour
Beatles
COL
129345 Abbey Road
Beatles
APP
Recording Label
Relation
(home relation)
Must match!
1-29150-7515-0
1-15700-9510-0
COL
Columbia Records
APP
Apple Records
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Relational Integrity
Constraints placed on the set of values allowed for the
attributes of a relation.
• Entity integrity
– No attribute of a primary key can be null (every tuple
must be uniquely identified)
• Referential integrity
– If a foreign key exists in a relation, either the foreign
key value must match a candidate key value of some
tuple in its home relation, or the foreign key value
must be wholly null (i.e., no key exists in the home
relation)
• General constraints (reflect business practices)
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
14
Null Value
• Absence of any value (i.e., unknown or nonapplicable to a
tuple)
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
15
Views
• A view is a virtual relation or one that does not actually
exist, but dynamically derived
– Can be constructed by performing operations (i.e.,
select, project, join, etc.) on values of existing base
relations
• Base relation - a named relation, corresponding to
an entity in the conceptual schema, whose tuples are
physically stored in the database
• View - a dynamic result of one or more relational
operations operating on the base relations to produce
another
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
16
Schema and Subschemas
17
Internal Level
Physical Database
DBMS
Some end-user applications
can be supported by views
External
Subschema
Level
User
User
DBMS Software
Schema Conceptual Level
Subschema
User
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
User
Subschema
User
User
Views
18
Base Relation R
Key
Base Relation S
Foreign
Key
Key
Criterion
View
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Purpose of Views
• Provides a powerful and flexible security mechanism by
hiding parts of the database from certain users
• Permits user access in a way that is customized to their
needs
• Simplify complex operations on the base relations
• Designed to support the external model
• Provides logical independence
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
19
Updating Views
• Allowed on views
– Derived from a single base relation, and
– Containing the primary key or a candidate key
• NOT allowed on views
– Derived from multiple base relations
– Involving aggregations (i.e., summations) or groups
operations
• Vendors may have other constraints on updating views
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
20
Relational Languages
Relational Calculus vs. Relational Algebra
• Relational calculus
Nonprocedural formal relational data manipulation
language in which the user specifies what data should be
retrieved, but not how to retrieve it.
• Relational algebra
A theoretical language with operators that work on one or
two relations to produce another relation.
Ricardo, 1990
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
21
Relational Algebra
• Fundamental operations:
– Selection ()
Unary operations (applied to a
– Projection ()
single relation)
– Union ()
– Set difference
– Intersection ()
– Cartesian product
– Join ( )
– Division
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
22
Aggregating and Grouping Operations
•
•
•
•
•
Count
Sum
Average (value)
Minimum (value)
Maximum (value)
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
23
Selection
24
• Selection operator works on a single relation and defines a
relation that contains only those tuples that satisfy the
specified condition (predicate)
predicate(R)
Predicate
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Projection
25
• Defines a relation that contains a vertical subset, extracting
the values of specified attributes and eliminating
duplicates.
attribute-1, ..., attribute-n(R)
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Projection on a Selection
26
• The selection extracts a subset of tuples from the original
relation R based on the predicate. The project further
reduces the number of attributes.
attribute-1, ..., attribute-n(predicate(R))
Original relation

R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento

Project on a Selection
27
license, make(color=‘Silver’(Cars))
Cars
3ABC123
4MIS345
4MIS837
4LLP394
4NOP934
4TLC394
4MSE291
5AMD042
4GAS294
Acura TL
Honda Accord DX
Honda Accord LX
Lexus GS400
Acura CL
Mercedes CLK320
BMW 525i
BMW M Roadster
Lexus LS430
color=‘Silver’
Silver
White
Black
Silver
MetBlue
Red
Teal
BlueMet
GoldMet
Step 1 3ABC123 Acura TL
4LLP394
license,
make
Lexus GS400
Silver
Silver
Step 2 3ABC123 Acura TL
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
4LLP394
Lexus GS400
Union
28
• The union of two relations R and S with I and J tuples,
receptively, is obtained by concatenating them into one
relation with a maximum of (I + J) tuples, duplicate tuples
being eliminated.
– R and S must be union compatible (i.e., same attributes)
attribute-1, ..., attribute-n(R) attribute-1, ..., attribute-n(S)
RS
R  S
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Union
RS
Set 1
3ABC123
4MIS345
4MIS837
4LLP394
4NOP934

Acura TL
Silver
Honda Accord DX
White
Honda Accord LX 3ABC123
Black Acura TL
4MIS345
Lexus GS400
Silver Honda Accord DX
4MIS837
Acura CL
MetBlue Honda Accord LX
4LLP394 Lexus GS400
Common
4NOP934 Acura CL
4TLC394 Mercedes CLK320
Set 2
BMW 525i
4TLC394 Mercedes CLK320 4MSE291
Red
5AMD042
BMW M Roadster
4MSE291 BMW 525i
Teal
5AMD042 BMW M Roadster 4GAS294
BlueMet Lexus LS430
4LLP394 Lexus GS400
Silver
4GAS294 Lexus LS430
GoldMet
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
29
Silver
White
Black
Silver
MetBlue
Red
Teal
BlueMet
GoldMet
Set Difference
30
• Defines a relation consisting of the tuples that are in
relation R, but not in S.
– R and S must be union-compatible (i.e., same attributes)
attribute-1, ..., attribute-n(R) attribute-1, ..., attribute-n(S)
RS
R
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
S
Set Difference
license, model, color(Set 1) license, model, color(Set 2)
31
Set 1
3ABC123
4MIS345
4MIS837
4LLP394
4NOP934
Acura TL
Honda Accord DX
Honda Accord LX
Lexus GS400
Acura CL
Silver
White
Black
Silver
MetBlue
3ABC123 Acura TL
4MIS345 Honda Accord DX
Not unique to Set 1
4MIS837 Honda Accord LX
Set 2
4NOP934 Acura CL
4TLC394 Mercedes CLK320
Red
4MSE291 BMW 525i
Teal
5AMD042 BMW M Roadster
BlueMet
RS
4LLP394 Lexus GS400
Silver
Set 1
Set 2
4GAS294 Lexus LS430
GoldMet
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Silver
White
Black
MetBlue
Set Difference
32
Reversed
attribute-1, ..., attribute-n(S) attribute-1, ..., attribute-n(R)
RS
R
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
S
Set Difference
license, model, color(Set 2) license, model, color(Set 1)
33
Set 1
3ABC123
4MIS345
4MIS837
4LLP394
4NOP934
Acura TL
Honda Accord DX
Honda Accord LX
Lexus GS400
Acura CL
Silver
White
Black
Silver
MetBlue
4TLC394 Mercedes CLK320
4MSE291 BMW 525i
5AMD042 BMW M Roadster
Set 2
4GAS294 Lexus LS430
4TLC394 Mercedes CLK320
Red
4MSE291 BMW 525i
Teal
5AMD042 BMW M Roadster
BlueMet
RS
4LLP394 Lexus GS400
Silver
Set 1
Set 2
4GAS294 Lexus LS430
GoldMet
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Red
Teal
BlueMet
GoldMet
Intersection
34
• An intersection consists of the set of all tuples that are in
both R and S.
– R and S must be union-compatible (i.e., same attributes)
attribute-1, ..., attribute-n(R) attribute-1, ..., attribute-n(S)
RS

R
S
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Intersection
35
Set 1
3ABC123
4MIS345
4MIS837
4LLP394
4NOP934
Acura TL
Honda Accord DX
Honda Accord LX
Lexus GS400
Acura CL
Silver
White
Black
Silver
MetBlue
4LLP394
Lexus GS400
Silver
Set 2
4TLC394
4MSE291
5AMD042
4LLP394
4GAS294
Mercedes CLK320
BMW 525i
BMW M Roadster
Lexus GS400
Lexus LS430
Red
Teal
BlueMet
Silver
GoldMet
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
RS
Set 1  Set 2
Cartesian Product
• The Cartesian product defines a relation that is the
concatenation of every tuple of relation R with every tuple
of relation S
attribute-1, ..., attribute-n(R) attribute-1, ..., attribute-n(S)
– I  J tuples
– N + M attributes
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
36
Cartesian Product
37
Cartesian
Product
Relation R
Relation S
a, b
c, d
e, f
} u, v
} w, x
} y, z
RS
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
a, b, u, v
a, b, w, x
a, b, y, z
c, d, u ,v
c, d, w, x
c, d, y, z
e, f, u, v
e, f, w, x
e, f, y, z
Joins
•
•
•
•
•
Theta join
Equi-join
Natural join
Outer join
Semi-join
38
Selection performed on two
relations, R and S, that share at
least a single common attribute
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Theta-Join vs. Equi-Join
R
FS
• Theta-join
A resulting relation that contains tuples satisfying the
predicate F from the Cartesian product of R and S
– The predicate F is of the form
R.aj  S.bj
where  may be one of the comparison operators <, <=,
>, >=, <>
• Equi-join
– Where  is ‘=‘
R
R.aj  S.bj S
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
39
Theta Join
40
( model (Cars))
Cars
LS340
GS300
GS430
C320
E320
S430
325i
523i
530i
cars.make < ‘MB’
( URL(Manufacturers))
Manufacturers
Lexus
Lexus
Lexus
MB
MB
MB
BMW
BMW
BMW
Lexus www.lexus.com
MB
www.mercedesbenz.com
BMW www.bmw.com
URL
model
model
URL
LS340 www.lexus.com
GS300 www.lexus.com
GS430 www.lexus.com
325i
www.bmw.com
523i
www.bmw.com
530i
www.bmw.com
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Equi-Join
41
( model(Cars))
Cars
LS340
GS300
GS430
C320
E320
S430
325i
523i
530i
S8
cars.make=manufacturers .make( URL(Manufacturers))
Manufacturers
Lexus
Lexus
Lexus
MB
MB
MB
BMW
BMW
BMW
Audi
Lexus
MB
BMW
Porsche
www.lexus.com
www.mercedesbenz.com
www.bmw.com
www.porsche.com
URL
No match
model
model
LS340
GS300
GS430
C320
E320
S430
325i
523i
530i
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
No match
URL
www.lexus.com
www.lexus.com
www.lexus.com
www.mercedesbenz.com
www.mercedesbenz.com
www.mercedesbenz.com
www.bmw.com
www.bmw.com
www.bmw.com
Natural Join
42
• Equi-join of two relations R and S over all common
attributes x
– One occurrence of each common attribute is eliminated
from the result
R
S
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Natural Join
43
( model, make (Cars))
cars.make=manufacturers .make
( make, URL(Manufacturers))
Cars
LS340
GS300
GS430
C320
E320
S430
325i
523i
530i
Manufacturers
Lexus
Lexus
Lexus
MB
MB
MB
BMW
BMW
BMW
Lexus www.lexus.com
MB
www.mercedesbenz.com
BMW www.bmw.com
LS340
GS300
GS430
C320
E320
S430
325i
523i
530i
Lexus
Lexus
Lexus
MB
MB
MB
BMW
BMW
BMW
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
www.lexus.com
www.lexus.com
www.lexus.com
www.mercedesbenz.com
www.mercedesbenz.com
www.mercedesbenz.com
www.bmw.com
www.bmw.com
www.bmw.com
“Make” not repeated
Outer Join
44
• A join in which tuples from R that do not have matching
values in the common attributes of S are also included in
the result relation.
– Missing values in the second relation are set to null
R
S
– Right outer join (all tuples on right kept)
vs. left outer join (all tuples on the left kept)
vs. full outer join (all tuples on both sides kept)
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Left Outer Join
 model (Cars)
Cars
LS340
GS300
GS430
C320
E320
S430
325i
523i
530i
S8
cars.make=manufacturers.make

45
URL(Manufacturers)
Manufacturers
Lexus
Lexus
Lexus
MB
MB
MB
BMW
BMW
BMW
Audi
Lexus
MB
BMW
Porsche
www.lexus.com
www.mercedesbenz.com
www.bmw.com
www.porsche.com
Match all the
models in Cars to
the URLs in
Manufacturers
LS340
GS300
GS430
C320
E320
S430
No match 325i
found
523i
530i
S8
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
www.lexus.com
www.lexus.com
www.lexus.com
www.mercedesbenz.com
www.mercedesbenz.com
www.mercedesbenz.com
www.bmw.com
www.bmw.com
www.bmw.com
null
Right Outer Join
 model (Cars)
Cars
LS340
GS300
GS430
C320
E320
S430
325i
523i
530i
S8
cars.make=manufacturers.make

46
URL(Manufacturers)
Manufacturers
Lexus
Lexus
Lexus
MB
MB
MB
BMW
BMW
BMW
Audi
Lexus
MB
BMW
Porsche
www.lexus.com
www.mercedesbenz.com
www.bmw.com
www.porsche.com
Match all the
URLs in
Manufacturers to
the models in Cars
No match
found
LS340
GS300
GS430
C320
E320
S430
325i
523i
530i
null
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
www.lexus.com
www.lexus.com
www.lexus.com
www.mercedesbenz.com
www.mercedesbenz.com
www.mercedesbenz.com
www.bmw.com
www.bmw.com
www.bmw.com
www.porsche.com
Division
• Consists of the set of tuples from R defined over the
attributes C that match the combination of every tuple in S
attribute-x(R) attribute-x( predicate(S))
Select
Project R T1
Project of Select T2
Occurrences in project T1 that match the pattern in T2
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
47
rno,pno(Viewing) pno( rooms=3(Property_for_Rent))
(Identify all renters who have viewed all properties with 3 rooms)
Viewing
Rno
CR56
CR76
CR56
CR62
CR56
Pno
PA14
PG4
PG4
PA14
PG36
Pno
Street
PA14 
PL94
PG4
PG36
PG21
PG16
Date
5/24/95
4/20/95
5/26/95
5/14/95
4/28/95
Comment
Too small
Too remote
Rno = Renter Number
No dining room
Area City
Property_for_Rent
Pcode Type Rooms Rent
6
4
3
3
5
4
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Ono Sno Bno
48
rno,pno(Viewing) pno( rooms=3(Property_for_Rent))
(Select Property_for_Rent with rooms = 3)
49
Property_for_Rent
Pno
Street
PA14 
PL94
PG4
PG36
PG21
PG16
Area City
Pcode Type Rooms Rent
6
4
3
3
5
4
Ono Sno Bno
( rooms=3(Property_for_Rent)
Pno
Street

PG4
PG36
Area City
Pcode Type Rooms Rent
3
3
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Ono Sno Bno
rno,pno(Viewing) pno( rooms=3(Property_for_Rent))
(Project proper number)
50
Select(Property_for_Rent)
Pno
Street

PG4
PG36
Area City
Pcode Type Rooms Rent
3
3
Ono Sno Bno
pno( rooms=3(Property_for_Rent)
Pno
PG4
PG36
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
All properties for
rent with 3 rooms.
rno,pno(Viewing) pno( rooms=3(Property_for_Rent))
(Project renter number and property number)
51
Viewing
Rno
CR56
CR76
CR56
CR62
CR56
Pno
PA14
PG4
PG4
PA14
PG36
Date
5/24/95
4/20/95
5/26/95
5/14/95
4/28/95
Comment
Too small
Too remote
No dining room
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
rno,pno
Rno
CR56
CR76
CR56
CR62
CR56
Pno
PA14
PG4
PG4
PA14
PG36
rno,pno(Viewing) pno( rooms=3(Property_for_Rent))
Renters who have
viewed properties Rno
CR56
CR76
CR56
CR62
CR56
Pno
PA14
PG4
PG4
PA14
PG36
Pno
PG4
PG36
All rental
properties with
3 rooms
Who has viewed all
rental properties with
3 rooms?
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
52
rno,pno(Viewing) pno( rooms=3(Property_for_Rent))
Renters who have
viewed properties Rno
CR56
CR76
CR56
CR62
CR56
Pno
PA14
PG4
PG4
PA14
PG36
Pno
PG4
PG36
All rental
properties with
3 rooms
Who has viewed all
rental properties with
3 rooms?
Renter CR76 has viewed PG4, but not PG36
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
53
rno,pno(Viewing) pno( rooms=3(Property_for_Rent))
(Identify all renters who have viewed all properties with three
rooms)
Renters who have
viewed properties Rno
CR56
CR76
CR56
CR62
CR56
Pno
PA14
PG4
PG4
PA14
PG36
{
Pno
PG4
PG36
All rental
properties with
3 rooms
Who has viewed all
rental properties with
3 rooms?
Rno
CR56
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
54
Renter number CR56 has
viewed all properties
with 3 rooms
Relational Calculus
55
• Specifies what is to be retrieved rather than how to retrieve
it
– Predicate (truth-value function arguments)
– Proposition (arguments replaced with values including
the predicate)
{x | P(x)}
Where P is the predicate
For all x, if P is true (e.g., for all x if predicate (x > 100)
is true)
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Tuple Oriented vs. Domain Oriented
Relational Calculus
• Tuple oriented (entities or rows)
Finding tuples for which a predicate is true
– Tuple variables (ranges over a named relation)
– Quantifiers:
Number of
• Existential (): there exists (one instance)
tuples to be
• Universal (): for all
applied to
• Domain oriented (attributes or columns)
Test for membership condition
– Variables that take their values from domains
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
56
57
SQL (pronounced ‘sequel’)
• Projection
• Selection
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Structured Query Language (SQL)
• Transform-oriented language:
– DDL (data definition language)
– DML (data manipulation language)
• Non-procedural (no logic structure required)
– Specify what information is required, rather than how
that information should be retrieved
– Format-free (not bound to columns)
– English-like (although abbreviated)
• First standard database language to gain wide acceptance
– ISO 1992 standards (today ISO 2003)
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
58
SQL Terminology
• ISO terminology
– Relations (entity type)  Tables
– Attributes (properties)  Columns
– Tuples (entities)
 Rows
column
Row {
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
59
SELECT: Basic Syntax
SELECT
[DISTINCT | ALL | {* | [column-list [AS alias]] [, ...]}
FROM table-name [alias] [, ...]
[WHERE condition]
[GROUP BY column-list] [HAVING condition]
[ORDER BY column-list]
[ ] = optional
| = select one option
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
60
Examples Using Products Table
Products (product_code (key), product_description,
product_cost, product_MSRP, product_retail_price,
retail_unit, manufacturer_code (foreign key), active_date,
number_on_hand, number_on_order, number_committed)
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
61
Projection
Performed on a Single Table
 product_code, product_description(Products)
Column-list
SELECT product_code, product_description
FROM products
Result: All rows with only the product_code and
product_description columns are retrieved
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
62
SQL> select product_code, product_description from products;
PRODUCT_CO PRODUCT_DESCRIPTION
---------- -----------------------------301-III
Direct/Reflecting Speakers
3800
Three-way Speaker System
4312
Studio Monitors
901Classic Direct/Reflecting Spkr System
AM3
Acoustimass Speaker System
AM5
Acoustimass 5 Speaker System
AM7
Acoustimass 7 Speaker System
AT-10
Loudspeakers
AT-15
Three-way Speaker
CCS-350
Compact System w/CD Player
CCS-450
Compact System w/CD Player
CCS-550
75-watt System w/CD Changer
CD-1000C
Compact Disc Changer
CDP-297
Compact Disc Player
CDP-397
Compact Disc Player
CDP-C225
Disc Jockey CD Changer
.
.
.
100 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
63
Selection
Performed on a Single Table
64
 manufacturer_code = ‘SON’ (Products)
SELECT *  All columns
FROM products
WHERE manufacturer_code = ‘SON’
Proposition
(predicate)
Result: Only those rows with SON for their
manufacturer_code are retrieved along with all columns.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Four Ways to Build a Selection
65

• Relational operator (=, <, <=, >, >=, <>)
– Logical AND and OR
WHERE column-name = x AND column-name = y
• Specific range using BETWEEN
WHERE column-name BETWEEN x AND y
• Specific values using IN
WHERE column-name IN (list-of-values)
• Character match using LIKE and wildcards (%, _)
– UPPER and LOWER functions
WHERE LOWER(column-name) LIKE ‘%character-string%’
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Four ways to build a SELECTION:
1. Relational and Logical Operators
• Relational operators:
=
equals
>, < greater than, less than
<=
less than or equal to
>=
greater than or equal to
<>
not equal to
• Logical operators
AND, OR AND evaluated before OR
NOT
NOT evaluated before AND and OR
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
66
WHERE (Condition)
SELECT *
All columns
FROM products
WHERE manufacturer_code = ‘SON’
OR manufacturer_code = ‘PAN’
SELECT *
FROM products
WHERE product_msrp >= 100 AND product_msrp <= 500
AND product_code = ‘SON’ OR product_code = ‘PAN’
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
67
select * from products where manufacturer_code='SON';
PRODUCT_CO PRODUCT_DESCRIPTION
PRODUCT_COST PRODUCT_MSRP PRODUCT_RETAIL_PRICE RE MAN
---------- ------------------------------ ------------ ------------ -------------------- -- --ACTIVE_DA NUMBER_ON_HAND NUMBER_ON_ORDER NUMBER_COMMITTED
--------- -------------- --------------- ---------------CDP-297
Compact Disc Player
84.47
129.95
116.96 EA SON
25-AUG-96
4
0
0
68
CDP-397
25-AUG-96
Compact Disc Player
11
97.47
CDP-C225
25-AUG-96
Disc Jockey CD Changer
10
CDP-C325
25-AUG-96
Disc Jockey CD Changer
1
0
CDP-C425
25-AUG-96
Disc Jockey CD Changer
5
0
CDP-C525
25-AUG-96
Disc Jockey CD Changer
6
0
SL-S600
25-AUG-96
Super-Beta Video Recorder
13
TC-W490
25-AUG-96
Double Cassette Deck
6
0
149.95
134.96 EA SON
199.95
179.96 EA SON
229.95
206.96 EA SON
249.95
224.96 EA SON
299.95
269.96 EA SON
329.95
296.96 EA SON
169.95
152.96 EA SON
0
129.97
0
0
149.47
0
162.47
0
194.97
0
214.47
0
0
110.47
0
12 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
0
Projection on a Selection
Performed on a Single Table
69
 product_code, product_description ( manufacturer_code = ‘SON’ or ‘PAN’ (Products))
Column list
Predicate (condition)
SELECT product_code, product_description
FROM products
WHERE manufacturer_code = ‘SON’
OR manufacturer_code = ‘PAN’
Only one condition has to be true
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
SQL> select product_code, product_description from products where
2 manufacturer_code='SON' or manufacturer_code='PAN';
PRODUCT_CO
---------CDP-297
CDP-397
CDP-C225
CDP-C325
CDP-C425
CDP-C525
PV-2201
PV-4210
PV-4250
SC-T095
SC-TC430
SL-S600
TC-W490
TC-WR590
TC-WR690
TC-WR790
TC-WR875
PRODUCT_DESCRIPTION
-----------------------------Compact Disc Player
Compact Disc Player
Disc Jockey CD Changer
Disc Jockey CD Changer
Disc Jockey CD Changer
Disc Jockey CD Changer
HQ VHS Video Cassette Recorde
4-Head VHS Video Cass Recorde
HiFi VHS Video Cass Recorder
Compact Stereo System
Compact System w/CD Changer
Super-Beta Video Recorder
Double Cassette Deck
Double Cassette Deck
Double Cassette Deck
Double Cassette Deck
Double Cassette Deck
17 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
70
SQL>
2
3
4
select product_code, product_description, product_msrp
from products
where manufacturer_code='SON' or manufacturer_code='PAN'
and product_msrp >= 100 and product_msrp <=500;
PRODUCT_CO
---------CDP-297
CDP-397
CDP-C225
CDP-C325
CDP-C425
CDP-C525
PV-2201
PV-4210
PV-4250
SC-T095
SC-TC430
SL-S600
TC-W490
TC-WR590
TC-WR690
TC-WR790
TC-WR875
PRODUCT_DESCRIPTION
PRODUCT_MSRP
------------------------------ -----------Compact Disc Player
129.95
Compact Disc Player
149.95
Disc Jockey CD Changer
199.95
Disc Jockey CD Changer
229.95
Disc Jockey CD Changer
249.95
Disc Jockey CD Changer
299.95
HQ VHS Video Cassette Recorde
229.95
4-Head VHS Video Cass Recorde
299.95
HiFi VHS Video Cass Recorder
349.95
Compact Stereo System
139.95
Compact System w/CD Changer
429.95
Super-Beta Video Recorder
329.95
Double Cassette Deck
169.95
Double Cassette Deck
199.95
Double Cassette Deck
249.95
Double Cassette Deck
329.95
Double Cassette Deck
429.95
17 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
71
Both conditions
must be true
Four ways to build a SELECTION:
2. Range Search with BETWEEN
SELECT product_code, product_description, product_msrp
FROM products
WHERE product_msrp BETWEEN 100 and 500  Inclusive
AND manufacturer_code = ‘SON’ OR manufacturer_code =
‘PAN’
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
72
SQL>
2
3
4
5
select product_code, product_description, product_msrp,
manufacturer_code
from products
where manufacturer_code = 'SON' or manufacturer_code = 'PAN'
73
and product_msrp between 100 and 500;
PRODUCT_CO
---------CDP-297
CDP-397
CDP-C225
CDP-C325
CDP-C425
CDP-C525
PV-2201
PV-4210
PV-4250
SC-T095
SC-TC430
SL-S600
TC-W490
TC-WR590
TC-WR690
TC-WR790
TC-WR875
PRODUCT_DESCRIPTION
PRODUCT_MSRP MAN
------------------------------ ------------ --Compact Disc Player
129.95 SON
Compact Disc Player
149.95 SON
Disc Jockey CD Changer
199.95 SON
Disc Jockey CD Changer
229.95 SON
Disc Jockey CD Changer
249.95 SON
Disc Jockey CD Changer
299.95 SON
HQ VHS Video Cassette Recorde
229.95 PAN
4-Head VHS Video Cass Recorde
299.95 PAN
HiFi VHS Video Cass Recorder
349.95 PAN
Compact Stereo System
139.95 PAN
Compact System w/CD Changer
429.95 PAN
Super-Beta Video Recorder
329.95 SON
Double Cassette Deck
169.95 SON
Double Cassette Deck
199.95 SON
Double Cassette Deck
249.95 SON
Double Cassette Deck
329.95 SON
Double Cassette Deck
429.95 SON
17 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Four ways to build a SELECTION:
3. Search for Specific Values Using IN
SELECT product_code, product_description
FROM products
WHERE manufacturer_code IN ('SON', 'PAN', 'BOS');
List of values
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
74
SQL>
2
3
select product_code, product_description
from products
where manufacturer_code in ('SON', 'PAN', 'BOS');
PRODUCT_CO
---------DVD-A110
DVP-S7000
DVP-S3000
DVP-S500D
KV-20S40
KV-20V80
KV-27V22
KV-27V26
KV-27V36
KV-32V36
KV-35V36
KV-32XBR48
KV-35XBR48
KV-35XBR88
.
.
.
TC-KE500S
PRODUCT_DESCRIPTION
----------------------------------DVD/CD Player
DVD/CD Player
DVD/CD Player
DVD/CD Player
20" Trinitron TV
20" Digital Comb Filter TV
27" Trinitron TV
27" Trinitron TV
27" Picture-in-Picture TV
32" 1-Tuner PIP TV
35" 1-Tuner PIP TV
32" Trinitron XBR TV
35" Trinitron XBR TV
35" Trinitron XBR TV
Cassette Deck
95 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
75
Four ways to build a SELECTION:
4. Pattern Match with LIKE
• Wildcard characters
– % = any sequence of zero or more characters
– _ (underscore) = any single character
SELECT product_code, product_description
FROM products
WHERE product_code LIKE ‘C%’
Result: All rows with product codes beginning with C and
their corresponding product_description will be retrieved.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
76
SQL>
2
select product_code, product_description from products
where product_code like 'C%';
PRODUCT_CO
---------CCS-350
CCS-450
CCS-550
CD-1000C
CDP-297
CDP-397
CDP-C225
CDP-C325
CDP-C425
CDP-C525
CS-13RX
CS-13SX1
CS-20SX1
CT-WN70R
PRODUCT_DESCRIPTION
-----------------------------Compact System w/CD Player
Compact System w/CD Player
75-watt System w/CD Changer
Compact Disc Changer
Compact Disc Player
Compact Disc Player
Disc Jockey CD Changer
Disc Jockey CD Changer
Disc Jockey CD Changer
Disc Jockey CD Changer
13" Color Television
13" Stereo Monitor/Television
20" Stereo Monitor/Television
6+1 Cassette Changer
14 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
77
Pattern Match with LIKE and UPPER Function
78
• SELECT product_code, product_description
FROM products
WHERE UPPER(product_description) LIKE UPPER('%casset%');
Changes to upper case
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
SQL> select product_code, product_description
2 from products
3 where upper(product_description) like upper('%casset%')
79
PRODUCT_CO
---------TC-KE400S
TC-KE500S
CT-W606DR
CT-W616DR
TD-W254
TD-W354
TD-W718
RS-TR373
RS-TR575
K-903
TC-WE405
TC-WE605S
•••
K-90
PRODUCT_DESCRIPTION
----------------------------------Cassette Deck
Cassette Deck
Double Cassette Deck
Double Cassette Deck
Double Auto-reverse Cassette Deck
Double Auto-reverse Cassette Deck
Dual Auto-reverse Rec Cassette Deck
Double Auto-reverse Cassette Deck
Double Auto-reverse Cassette Deck
Dual Electronic Cassette Deck
Dual Cassette Deck
Dual Auto-reverse Cassette Deck
Double Cassette Deck
42 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
Like Without Matching the Case
SQL> select product_code, product_description
2 from products
3 where product_description like '%casset%';
no rows selected
Cassette  cassette
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
80
ORDER BY: Sorting the Results
SELECT manufacturer_code, product_code,
product_description
FROM products
ORDER BY manufacturer_code, product_code
Major (sort) key Minor (sort) key
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
81
select manufacturer_code, product_code, product_description
2 from products order by manufacturer_code, product_code;
MAN
--AIW
AIW
BOS
BOS
BOS
BOS
BOS
BOS
CRV
CRV
CRV
DA
DA
DA
GMI
GMI
GMI
GMI
PRODUCT_CO
---------NSX-D2
XK-S9000
301-III
901Classic
AM3
AM5
AM7
VS-100
AT-10
AT-15
SW-12B
PS-6a
PS-8c
PS-9
PVX-31
XL-1800QII
XL-BD10
XL-DD20
...
PRODUCT_DESCRIPTION
-----------------------------Mini Component System
Cassette Deck
Direct/Reflecting Speakers
Direct/Reflecting Spkr System
Acoustimass Speaker System
Acoustimass 5 Speaker System
Acoustimass 7 Speaker System
Center Channel Mini Speaker
Loudspeakers
Three-way Speaker
Subwoofer System
Point Source Speaker System
Point Source Speaker Sytem
Point Source Speaker System
Single Ch 31/3rd Octave Bands
Prof Manual DJ Turntable
Semi-Auto Belt-Dr Turntable
Semi-Automatic Turntable
100 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
82
Using DISTINCT
SELECT DISTINCT(manfuacturer_code)
FROM products
Result: The non-duplicated manufacturer_codes will be
retrieved.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
83
select distinct(manufacturer_code) from products;
MAN
--AIW
BOS
CRV
DA
GMI
HVC
JBL
JVC
MIT
PAN
PIN
PIO
SAM
SHE
SON
TEA
TEC
THN
YAM
19 rows selected.
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento
84
85
R. Ching, Ph.D. • MIS Dept. • California State University, Sacramento