Chapter 3The Relational Database Model

Logical View of DataLogical View of Data

Relational Database – Designer focuses on logical representation

rather than physical– Use of table advantageous

Structural and data independenceRelated records stored in independent tablesLogical simplicity

A Logical View of DataA Logical View of DataA Logical View of DataA Logical View of Data Relational database model’s structural and data

independence

– allow us to ignore the structure’s physical complexity.

– enables us to view data logically rather than physically.

The logical view allows a simpler file concept of data storage.

The use of logically independent tables is easier to understand.

Logical simplicity yields simpler and more effective database design methodologies.

A Logical View of DataA Logical View of Data Entities and Attributes

– An entity is a person, place, event, or thing for which we intend to collect data.

University -- Students, Faculty Members, Courses

– Each entity has certain characteristics known as attributes.

Student -- Student Number, Name, GPA, Date of Enrollment, Data of Birth, Home Address, Phone Number, Major

– A grouping of related entities becomes an entity set.

The STUDENT entity set contains all student entities.

The FACULTY entity set contains all faculty entities.

Table CharacteristicsTable Characteristics• Two-dimensional structure with rows and columns• Row (tuple) represent a single entity occurrence within the

entity set.• Columns represent attributes• Row/column intersection represents single value• Column values all have same data format• Each column has range of values called attribute domain

• Order of the rows and columns is immaterial to the DBMS

• Tables must have an attribute to uniquely identify each row

A Logical View of DataA Logical View of DataA Logical View of DataA Logical View of Data

Tables and Their Characteristics

– A table contains a group of related entities -- i.e. an entity set.

– The terms entity set and table are often used interchangeably.

– A table is also called a relation.

entity set ≒ table ≒ relation

KeysKeys

– A key Consists of one or more attributes that determine other attributes

– Primary key (PK) is an attribute (or a combination

of attributes) that uniquely identifies any given entity (row)

KeysKeys– The key’s role is based on a concept known as

determination, which is used in the definition of functional dependence.

The attribute B is functionally dependent on A if A determines B. ( A → B )

An attribute that is part of a key is known as a key attribute.

A multi-attribute key is known as a composite key.

If the attribute (B) is functionally dependent on a composite key (A) but not on any subset of that composite key, the attribute (B) is fully functionally dependent on (A). (A is minimal !)

STU_HRS → STU_CLASS

Freshman

Sophomore

Junior

Senior

Keys Keys

Superkey– Uniquely identifies each entity

Candidate key– Minimal superkey

Primary key– Candidate key selected to uniquely identify all other

attributes in a given row

Secondary key– Used only for data retrieval

Foreign key– Values must match primary key in another table

Super key– STU_NUM– (STU_NUM, STU_LNAME)

Candidate key– STU_NUM– (STU_LNAME, STU_FNAME, STU_INIT, STU_PHONE)

Primary key– STU_NUM

Secondary keynot necessarily yield a unique outcome– CUSTOMER table:

Primary key : Customer number Secondary key : Customer last name, Customer phone number

– A secondary key’s effectiveness in narrowing down a search depends on how restrictive it is.

Null ValuesNull Values

No data entry Not permitted in primary key Should be avoided in other attributes Can represent

– An unknown attribute value

– A known, but missing, attribute value

– A “not applicable” condition

Can create problems in logic and using formulas (COUNT, AVERAGE, and SUM)

KeysKeysKeysKeys

Controlled redundancy (shared common attributes) makes the relational database work.

The primary key of one table appears again as the link (foreign key) in another table.

– VEND_CODE in PRODUCT table (Figure 3.2)

referential integrity :if the foreign key contains a value, that value refers to an existing valid tuple in another relation.

Integrity RulesIntegrity RulesEntity integrity

– (unique and no null) All Primary key entries are unique , and no part of a Primary key may be null .

CUS_CODE AGENT_CODE

Referential integrity– (null or matching)

A Foreign key may have either a null entry or an entry that matches the primary key value in a tableto which it is related.

AGENT_CODE

null or matching

Relational Database OperatorsRelational Database OperatorsRelational Database OperatorsRelational Database Operators Relational algebra defines the theoretical way of

manipulating table contents using the eight relational functions: – SELECT– PROJECT– JOIN– INTERSECT– UNION– DIFFERENCE– PRODUCT– DIVIDE

Use of relational algebra operators on existing tables (relations) produces new relations

Relational Database OperatorsRelational Database OperatorsRelational Database OperatorsRelational Database Operators UNION combines all rows from two tables. The two

tables must be union compatible.(share the same columns and domains)

INTERSECT produces a listing that contains only the rows that appear in both tables. The two tables must be union compatible.

DIFFERENCE yields all rows in one table that are not found in the other table; i.e., it subtracts one table from the other. The tables must be union compatible.

PRODUCT produces a list of all possible pairs of rows from two tables. (Cartesian product)

SELECT yields values for all rows found in a table. It yields a horizontal subset of a table.

Figure 2.9

PROJECT produces a list of all values for selected attributes. It yields a vertical subset of a table.

JOIN allows us to combine information from two or more tables. JOIN is the real power behind the relational database, allowing the use of independent tables linked by common attributes.

Relational Database OperatorsRelational Database Operators• Natural JOIN links tables by selecting only the

rows with common values in their common attribute(s). It is the result of a 3-stage process:

1. A PRODUCT of the tables is created. (Figure 3.12)

2. A SELECT is performed on the output of the first step to yield only the rows for which the common attribute values match. (Figure 3.13)Common column(s) are called join column(s)

3. A PROJECT is performed to yield a single copy of each attribute, thereby eliminating the duplicate column. (Figure 3.14)

Relational Database OperatorsRelational Database OperatorsRelational Database OperatorsRelational Database Operators– Data warehouse

links tables based on an equality condition that compares specified columns of each table. The outcome of the EquiJOIN does not eliminate duplicate columns and the condition or criteria to join the tables must be explicitly defined.

– Theta JOIN is an equiJOIN that compares specified columns of each table using a comparison operator other than the equality(=) comparison operator.

– In an Outer JOIN, the unmatched pairs would be retained and the values for the unmatched other tables would be left null.

equiJOIN criteria(C=E)

A B C

1 X 10

2 X 2020

3 y 10

D E

m 10

n 2020

p 40

A B C D E

1 x 10 m 10

3 y 10 m 10

2 x 2020 n 2020

theta JOIN criteria(C<E)

A B C

1 X 10

2 X 2020

3 y 10

D E

m 10

n 2020

p 40

A B C D E

1 x 10 n 2020

1 x 10 p 40

2 x 2020 p 40

3 y 10 n 2020

3 y 10 p 40

CUSTOMER Full Outer JOINCUSTOMER.AGENT_CODE=AGENT.AGENT_CODE AGENT

CUSTOMER Left Outer JOINCUSTOMER.AGENT_CODE=AGENT.AGENT_CODE AGENT

CUSTOMER Right Outer JOINCUSTOMER.AGENT_CODE=AGENT.AGENT_CODE AGENT

DIVIDE requires the use of one single-column table and one two-column table.

This is not an uncommon situation, but the solution is not straightforward since the SQL SELECT statement does not directly support relational division.

There are numerous ways to achieve the same results as a relational division, however. The easiest method is to rephrase the request. Instead of "list the suppliers who provide all product categories,“ which is difficult to process, try "list all suppliers where the count of their product categories is equal to the count of all product categories."

The Data Dictionary and The Data Dictionary and the System Catalogthe System Catalog

Data dictionary contains metadata to provide detailed accounting of all tables within the database.

System catalog is a very detailed system data dictionary that describes all objects within the database.

– Authorized users, access privileges …

– System catalog is a system-created database whose tables store the database characteristics and contents.

Terms “system catalog” and “data dictionary” are often used interchangeably

Relationships within the Relationships within the Relational DatabaseRelational Database

E-R Diagram (ERD)

– Rectangles are used to represent entities.

– Entity names are nouns and capitalized.

– Diamonds are used to represent the relationship(s) between the entities.

– “1” side of relationship Number 1 in Chen Model

Bar crossing line in Crow’s Feet Model

– “Many” relationships Letter “M” and “N” in Chen Model

Three pronged “Crow’s foot” in Crow’s Feet Model

The The 1:11:1 Relationship Relationship One entity can be related to only one other

entity, and vice versa

Often means that entity components were not defined properly

Could indicate that two entities actually belong in the same table

Sometimes 1:1 relationships are appropriate

The 1:1 Relationship Between The 1:1 Relationship Between PROFESSOR and DEPARTMENTPROFESSOR and DEPARTMENT

The Implemented 1:1 Relationship Between The Implemented 1:1 Relationship Between PROFESSOR and DEPARTMENTPROFESSOR and DEPARTMENT

The 1:M Relationship The 1:M Relationship Between PAINTER and PAINTINGBetween PAINTER and PAINTING

Example 1:M RelationshipExample 1:M Relationship

The Implemented 1:M Relationship The Implemented 1:M Relationship Between PAINTER and PAINTINGBetween PAINTER and PAINTING

PAINTER (PAINTER_NUM)

PAINTING (PAINTING_NUM) (PAINTER_NUM)

The 1:M Relationship The 1:M Relationship Between COURSE and CLASSBetween COURSE and CLASS

The Implemented 1:M RelationshipThe Implemented 1:M RelationshipBetween COURSE and CLASSBetween COURSE and CLASS

COURSE (CRS_CODE)

CLASS (CLASS_CODE) (CRS_CODE)

The The M:N M:N RelationshipRelationship

Can be implemented by breaking it up to produce a set of 1:M relationships

Can avoid problems inherent to M:N relationship by creating a composite entity or bridge entity

The ERD’s M:N Relationship The ERD’s M:N Relationship Between STUDENT and CLASSBetween STUDENT and CLASS

Sample Student Enrollment DataSample Student Enrollment Data

The M:N Relationship The M:N Relationship Between STUDENT and CLASSBetween STUDENT and CLASS

321452

10014

Composite EntityComposite Entity The tables (Figure 3.25) creates many

redundancies.

Composite Entity ( Bridge Entity)– Links the tables originally were related in a M:N relationship– Includes at least the primary keys of the tables that are to

be linked.

– In ERD,represented by a diamond within a rectangle

Converting the M:N Relationship Converting the M:N Relationship into Two 1:M Relationshipsinto Two 1:M Relationships

ENROLL (CLASS_CODE+STU_NUM) (CLASS_CODE,STU_NUM)

STUDENT (STU_NUM)

CLASS (CLASS_CODE) (CRS_CODE)

Changing the M:N Relationship Changing the M:N Relationship to Two 1:M Relationshipsto Two 1:M Relationships

The Expanded Entity Relationship ModelThe Expanded Entity Relationship Model

The Relational Schema for the The Relational Schema for the Ch03_TinyCollege DatabaseCh03_TinyCollege Database

Data Redundancy RevisitedData Redundancy Revisited

Data redundancy leads to data anomalies

– Such anomalies can destroy database effectiveness

Foreign keys

– Control data redundancies by using common attributes shared by tables

– Foreign keys can reduce redundancy

Sometimes, data redundancy is necessary

Changing the M:N Relationship Changing the M:N Relationship to Two 1:M Relationshipsto Two 1:M Relationships

INVOICE

INVOICE

PRODUCT

PRODUCTLINE

A Small Invoicing System

CUSTOMER (CUS_CODE)

INVOICE (INV_NUM) (CUS_CODE)

LINE (INV_NUM+LINE_NUM) (INV_NUM, PROD_CODE)

PRODUCT (PROD_CODE)

The Relational SchemaThe Relational Schemafor the Invoicing Systemfor the Invoicing System

Why Data Redundancies ?Why Data Redundancies ?

Sometimes, data redundancy is necessary Product price (LINE_PRICE in LINE table) :

Data redundancies !Data redundancies ! The redundancy is crucial to the system’s success.

Copying the product price from PRODUCT table to the LINE table means that it is possible to maintain the historical accuracy of the transactions. Product price may change.

IndexesIndexesIndexesIndexes An index is an orderly arrangement used to logically

access rows in a table of keys and pointers.

An index is composed of – an index key (the index’s reference point) (PAINTER_NUM)

– a set of pointers.

Each pointer points to the location of the data identified by the key.

Makes retrieval of data faster

Each index is associated with only one table

IndexesIndexesIndexesIndexes