Chapter 3 The Relational Database Model

A Logical View of Data Relational model Table Enables programmer to view data logically rather than physically Table Has advantages of structural and data independence Resembles a file from conceptual point of view Easier to understand than its hierarchical and network database predecessors

Tables and Their Characteristics Table: two-dimensional structure composed of rows and columns Contains group of related entities = an entity set Terms entity set and table are often used interchangeably entity set ≒ table ≒ relation

Tables and Their Characteristics Table also called a relation because the relational model’s creator, Codd, used the term relation as a synonym for table Think of a table as a persistent relation: A relation whose contents can be permanently saved for future use

Tables and Their Characteristics

Table 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 or a combination of attributes that uniquely identifies each row

Tables and Their Characteristics

Keys 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) Key’s role is based on determination If you know the value of attribute A, you can look up (determine) the value of attribute B

Keys (continued) HOURS_COMPLETED → CLASSIFICATION Freshman Sophomore Junior Freshman Sophomore Junior Senior Senior HOURS_COMPLETED → CLASSIFICATION

Keys Composite key Key attribute Superkey Candidate key Primary key Composed of more than one attribute Key attribute Any attribute that is part of a key Superkey Any key that uniquely identifies each row Candidate key A superkey without redundancies Minimal superkey Does not contain a subset of attributes that itself a superkey. Primary key Candidate key selected to uniquely identify all other attributes in a given row. Cannot contains null entries.

Secondary key not necessarily yield a unique outcome Super key STU_NUM (STU_NUM, STU_LNAME) Candidate key (STU_LNAME, STU_FNAME, STU_INIT, STU_PHONE) Primary key Secondary key not 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 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 when functions such as (COUNT, AVERAGE, and SUM) Can create logical problems when relational tables are linked

Keys (continued) Controlled redundancy : Makes the relational database work Tables within the database share common attributes that enable the tables to be linked together Multiple occurrences of values in a table are not redundant when they are required to make the relationship work Redundancy exists only when there is unnecessary duplication of attribute values

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

Keys (continued) Foreign key (FK) Referential integrity Secondary key An attribute whose values match primary key values in the related table Referential integrity FK contains a value that refers to an existing valid tuple (row) in another relation Secondary key Key used strictly for data retrieval purposes

Keys (continued)

Integrity Rules

Integrity Rules Entity 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

Integrity Rules 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 table to which it is related. AGENT_CODE Every non-null foreign key value must reference an existing primary key value.

-99 null or matching

Integrity Rules (continued) To avoid nulls, some designer use special codes, known as flags, to indicate the absence of some value. The code -99 could be used as the AGNET_CODE of 4th row of the CUSTOMER table to indicate that customer Paul does not yet have an agent assigned to him.

Relational Database Operators Relational algebra defines the theoretical way of manipulating table contents using relational operators: UNION INTERSECT DIFFERENCE PRODUCT SELECT PROJECT JOIN DIVIDE Use of relational algebra operators on existing tables (relations) produces new relations

Relational Database Operators UNION combines all rows from two tables, excluding duplicate rows. The two tables must be union compatible. (share the same columns and domains)

INTERSECT yields only the rows that appear in both tables INTERSECT yields 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 yields all possible pairs of rows from two tables PRODUCT yields all possible pairs of rows from two tables. (Cartesian product)

SELECT Figure 2.9 yields values for all rows found in a table. Can be used to list either all row values or it can yield only those row values that match a specified criterion It yields a horizontal subset of a table. Figure 2.9

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

JOIN allows information to be combined from two or more tables. Real power behind the relational database, allowing the use of independent tables linked by common attributes.

Relational 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: PRODUCT of the tables is created. (Figure 3.12) SELECT is performed on Step 1 output to yield only the rows for which the common attribute values match. (Figure 3.13) Common column(s) are called join column(s) PROJECT is performed to yield a single copy of each attribute, thereby eliminating the duplicate column. (Figure 3.14)

PRODUCT

SELECT PROJECT

Relational Algebra Operators (continued) Natural Join: Final outcome yields table that Does not include unmatched pairs Provides only copies of matches

Relational Algebra Operators The column on which the join was made occurs only once in the new table (AGENT_CODE) If no match is made between the table rows the new table does not include the unmatched row (AGENT_CODE 421) So, we need another form of join

Relational Algebra Operators EquiJOIN links tables based on an equality condition that compares specified columns of each table. Outcome does not eliminate duplicate columns 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.

equiJOIN criteria(C=E) B C 1 X 10 2 20 3 y D E m 10 n 20 p 40 A B C D E 1 x 10 m 3 y 2 20 n

theta JOIN criteria(C<E) B C 1 X 10 2 20 3 y D E m 10 n 20 p 40 A B C D E 1 x 10 n 20 p 40 2 3 y

Relational Algebra Operators Outer join: Matched pairs are retained and any unmatched values in other table are left null In outer join for tables CUSTOMER and AGENT, two scenarios are possible: Left outer join Yields all rows in CUSTOMER table, including those that do not have a matching value in the AGENT table Right outer join Yields all rows in AGENT table, including those that do not have matching values in the CUSTOMER table

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 System Catalog Contains metadata — data about data Provides detailed accounting of all tables found within the user/designer-created database Contains (at least) all the attribute names and characteristics for each table in the system Sometimes described as “the database designer’s database” because it records the design decisions about tables and their structures

The Data Dictionary and System Catalog Contains metadata Detailed system data dictionary that describes all objects within the database Terms “system catalog” and “data dictionary” are often used interchangeably Can be queried just like any user/designer-created table

Relationships within the Relational 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

Relationships within the Relational Database 1:M relationship Relational modeling ideal Should be the norm in any relational database design 1:1 relationship Should be rare in any relational database design M:N relationships Cannot be implemented as such in the relational model M:N relationships can be changed into two 1:M relationships

The 1:M Relationship Relational database norm Found in any database environment

The 1:M Relationship Between PAINTER and PAINTING

Example 1:M Relationship

The Implemented 1:M Relationship Between PAINTER and PAINTING PAINTER (PAINTER_NUM) PAINTING (PAINTING_NUM) (PAINTER_NUM)

The 1:M Relationship Between COURSE and CLASS

The Implemented 1:M Relationship Between COURSE and CLASS COURSE (CRS_CODE) CLASS (CLASS_CODE) (CRS_CODE)

The 1:1 Relationship One entity can be related to only one other entity, and vice versa Sometimes means that entity components were not defined properly Could indicate that two entities actually belong in the same table As rare as 1:1 relationships should be, certain conditions absolutely require their use

The 1:1 Relationship Between PROFESSOR and DEPARTMENT

The 1:1 Relationship (continued) 1:M 1:1

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

Sample Student Enrollment Data

The M:N Relationship Between STUDENT and CLASS 321452 10014

Composite 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 Chen’s ERD, represented by a diamond within a rectangle

Converting the M:N Relationship into Two 1:M Relationships STUDENT (STU_NUM) ENROLL (CLASS_CODE+STU_NUM) (CLASS_CODE,STU_NUM) CLASS (CLASS_CODE) (CRS_CODE)

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

The Expanded Entity Relationship Model

The Relational Schema for the Ch03_TinyCollege Database

Data 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 to Two 1:M Relationships INVOICE PRODUCT INVOICE LINE PRODUCT

A Small Invoicing System CUSTOMER (CUS_CODE) INVOICE (INV_NUM) (CUS_CODE) A Small Invoicing System LINE (INV_NUM+LINE_NUM) (INV_NUM, PROD_CODE) PRODUCT (PROD_CODE)

The Relational Schema for the Invoicing System

Why Data Redundancies ? Sometimes, data redundancy is necessary Product price (LINE_PRICE in LINE table) : 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.

Indexes 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

Indexes

Codd’s Relational Database Rules In 1985, Codd published a list of 12 rules to define a relational database system The reason was the concern that many vendors were marketing products as “relational” even though those products did not meet minimum relational standards

Codd’s Relational Database Rules

Summary Tables are basic building blocks of a relational database Keys are central to the use of relational tables Keys define functional dependencies Superkey Candidate key Primary key Secondary key Foreign key

Summary (continued) Each table row must have a primary key which uniquely identifies all attributes Tables can be linked by common attributes. Thus, the primary key of one table can appear as the foreign key in another table to which it is linked The relational model supports relational algebra functions: SELECT, PROJECT, JOIN, INTERSECT, UNION, DIFFERENCE, PRODUCT, and DIVIDE. Good design begins by identifying appropriate entities and attributes and the relationships among the entities. Those relationships (1:1, 1:M, and M:N) can be represented using ERDs.

Logical View of Data Relational Database Designer focuses on logical representation rather than physical Use of table advantageous Structural and data independence Related records stored in independent tables Logical simplicity

A 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 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.

A Logical View of Data entity set ≒ table ≒ relation 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

Immaterial 無關緊要的

Keys 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 !)