Complete dbms notes

Data Base Management Systems
1
Chapter 01
Overview of Data Base Management Systems:
Data bases and database systems have become an essential
component of everyday life in modern society.
Examples for database Applications:
 Purchases from the supermarket
 Purchases using your credit card
 Booking a holiday at the travel agents
 Using the local library
 Taking out insurance
 Using the Internet
 Studying at university
Need to store data :
Data originates at one time and used later(i.e.) Store
registrations for grading later, Store for future
information needs, Governmental regulations requires access
to past data, Data used later for auditing, evaluation
purpose, Used more than once : save for future use
Limitations of manual methods:
Problems of speed, Problems of accuracy, Problems of
consistency and reliability, Problems of poor response
time, Problems of work-load handling capability, Problems
of meeting ad hoc information needs, Problems of cost,
Problems due to human frailties: (misplaced) loyalty,
inconsistency, irregularity, difficulties in handling big
tasks …
Why computerized data processing?
Advantage of speed, Advantage of accuracy, Advantage of
reliability and consistency, Advantage of storage and
retrieval efficiency, Advantage of on-line-access to meet
ad-hoc needs, Advantage of cost
Data Base : Collection of related data. By data, we mean known
facts that can be recorded and that have implicit meaning.
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Definition of DBMS: A data base management system(DBMS) is a
collection of programs that enables users to create and
maintain a database. The DBMS is hence a general purpose
software system that facilitate the process of defining,
constructing , manipulating and sharing databases among the
various users and applications.
Historical development of database Technologies:
 Early Database Applications: The Hierarchical and
Network Models were introduced in mid 1960’s and
dominated during the seventies. A bulk of the worldwide
database processing still occurs using these models.
 Relational Model based Systems: The model that was
originally introduced in 1970 was heavily researched
and experimented with in IBM and the universities.
Relational DBMS Products emerged in the 1980’s.
 Object-oriented applications: OODBMSs were introduced
in late 1980’s and early 1990’s to cater to the need of
complex data processing in CAD and other applications.
Their use has not taken off much.
 Data on the Web and E-commerce Applications: Web
contains data in HTML (Hypertext markup language) with
links among pages. This has given rise to a new set of
applications and E-commerce is using new standards like
XML (eXtended Markup Language).
Extending Database Capabilities:
New functionality is being added to DBMSs in the following
areas:
Scientific Applications, Image Storage and Management,
Audio and Video data management, Data Mining, Spatial data
management, Time Series and Historical Data Management
Transaction Management:
 A transaction is a collection of operations that performs
a single logical function in a database application
 Transaction-management component ensures that the
database remains in a consistent (correct) state despite
system failures (e.g., power failures and operating
system crashes) and transaction failures.
 Concurrency-control manager controls the interaction
among the concurrent transactions, to ensure the
consistency of the database.
A database transaction is a unit of interaction with a
database management system or similar system that is treated
in a coherent and reliable way independent of other
transactions that must be either entirely completed or
aborted.
In some systems, transactions are also called LUW for Logical
Units of Work.
In database products the ability to handle transactions allows
the user to ensure that integrity of a database is maintained.pdfMachine - is a pdf writer that produces quality PDF files with ease!
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A single transaction might require several queries, each
reading and/or writing information in the database. When this
happens it is usually important to be sure that the database
is not left with only some of the queries carried out. For
example, when doing a money transfer, if the money was debited
from one account, it is important that it also be credited to
the depositing account. Also, transactions should not
interfere with each other.
Storage Management:
 Storage management is a program module that provides the
interface between the low-level data stored in the
database and the application programs and queries
submitted to the system.
 The storage manager is responsible to the following
tasks:
 interaction with the file manager
 efficient storing, retrieving and updating of data
Database Administrator:
A database administrator (DBA) is a person who is responsible
for the environmental aspects of a database. In general, these
include:
 Recoverability - Creating and testing Backups
 Integrity - Verifying or helping to verify data integrity
 Security - Defining and/or implementing access controls
to the data
 Availability - Ensuring maximum uptime
 Performance - Ensuring maximum performance given
budgetary constraints
 Development and testing support - Helping programmers and
engineers to efficiently utilize the database.
The role of a database administrator has changed according to
the technology of database management systems (DBMSs) as well
as the needs of the owners of the databases.
Types of Databases and Database Applications
 Numeric and Textual Databases (Traditional Database)
 Multimedia Databases (Video clips, pictures, sound
message)
 Geographic Information Systems (GIS)(Weather data, map
analysis, satellite images)
 Data Warehouses (Decision making)
 Real-time and Active Databases (Internet based (World
wide web))
A simplified database system environment.
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Advantages of Using the Database Approach
 Controlling redundancy in data storage and in development
and maintenance efforts.
o Duplication is wasteful. It costs time and money to
enter the data more than once.
o It takes up additional storage space, again with
associated costs.
o Perhaps more importantly, duplication can lead to
loss of data integrity.
 Sharing of data among multiple users.
 Restricting unauthorized access to data.
o When multiple users shares a large database, it is
likely that most users will not be authorized to
access all information in the database.
 Providing persistent storage for program Objects
o A complex object in C++ can be stored permanently in
an Object Oriented DBMS. Such an object is said to
be persistence, since it survives the termination of
the program execution and can later be directly
retrieved by another C++ program.
 Providing backup and recovery services.
 Providing multiple interfaces to different classes of
users.
 Representing complex relationships among data.
Database Users:
o Database administrators: responsible for authorizing
access to the database, for co-ordinating and monitoring
its use, acquiring software, and hardware resources,
controlling its use and monitoring efficiency of
operations.
o Database Designers: Responsible to define the content,
the structure, the constraints, and functions or
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transactions against the database. They must communicate
with the end-users and understand their needs.
o End users : End users are the people whose jobs require
access to the database for querying, updating, and
generating reports; the database primarily exists for
their use. There are several categories of end users:
o Casual End User: access database occasionally when
needed. But they may need different information each
time.
o Naïve or Parametric End user : they make up a large
section of the end-user population. They use
previously well-defined functions in the form of
“canned transactions” against the database. Examples
are bank-tellers or reservation clerks who do this
activity for an entire shift of operations.
o Sophisticated End User : These include business
analysts, scientists, engineers, others thoroughly
familiar with the system capabilities. Many use
tools in the form of software packages that work
closely with the stored database.
o Stand-alone End User : Mostly maintain personal
databases using ready-to-use packaged applications.
An example is a tax program user that creates his or
her own internal database.
Describing data : Levels of Abstraction
• Database Schema: The description of a database. Includes
descriptions of the database structure and the
constraints that should hold on the database.
• Schema Diagram: A diagrammatic display of (some aspects
of) a database schema.
The data in a DBMS is described at three levels of
abstraction, as illustrated in the figure.
Defines DBMS schemas at three levels:
o Internal schema at the internal level to describe
physical storage structures and access paths. Typically
uses a physical data model.( how a record (e.g.,
customer) is stored-Physical level)
o Conceptual schema at the conceptual level to describe
the structure and constraints for the whole database
for a community of users. Uses a conceptual or an
implementation data model.( describes data stored in
database, and the relationships among the data –
Logical level)
o External schemas at the external level to describe the
various user views. Usually uses the same data model as
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the conceptual level.(describes data as seen by a
user/application – View Level)
o Schema – describes contents of the database
o e.g., what information about a set of customers and
accounts and the relationship between them)
o Physical schema: how data is stored at physical
level (how)
o Logical schema: data contained at the logical level
(what)
o Database Instance: The actual data stored in a database
at a particular moment in time. Also called database
state (or occurrence).
Data Independence :
When a schema at a lower level is changed, only the mappings
between this schema and higher-level schemas need to be
changed in a DBMS that fully supports data independence. The
higher-level schemas themselves are unchanged. Hence, the
application programs need not be changed since they refer to
the external schemas
 Physical Data Independence – the ability to modify the
physical schema without changing the logical schema
o Applications depend on the logical schema
o In general, the interfaces between the various
levels and components should be well defined so that
changes in some parts do not seriously influence
others.
 Logical Data Independence – the ability to modify
conceptual schema without changing the external Schema or
application programs.
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History of Data Models :
 Relational Model: proposed in 1970 by E.F. Codd (IBM),
first commercial system in 1981-82. Now in several
commercial products (DB2, ORACLE, SQL Server, SYBASE,
INFORMIX).
 Network Model: the first one to be implemented by
Honeywell in 1964-65 (IDS System). Adopted heavily due
to the support by CODASYL (CODASYL - DBTG report of
1971). Later implemented in a large variety of systems -
IDMS (Cullinet - now CA), DMS 1100 (Unisys), IMAGE
(H.P.), VAX -DBMS (Digital Equipment Corp.).
 Hierarchical Data Model: implemented in a joint effort by
IBM and North American Rockwell around 1965. Resulted in
the IMS family of systems. The most popular model. Other
system based on this model: System 2k (SAS inc.)
 Object-oriented Data Model(s): several models have been
proposed for implementing in a database system. One set
comprises models of persistent O-O Programming Languages
such as C++ (e.g., in OBJECTSTORE or VERSANT), and
Smalltalk (e.g., in GEMSTONE). Additionally, systems like
O2, ORION (at MCC - then ITASCA), IRIS (at H.P.- used in
Open OODB).
 Object-Relational Models: Most Recent Trend. Started with
Informix Universal Server. Exemplified in the latest
versions of Oracle-10i, DB2, and SQL Server etc. systems.
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Chapter 02
Entity Relation Model:
The Entity Relationship (ER) data model allows us to
describe the data involved in a real world enterprise in terms
of objects and their relationships and is widely used to
develop an initial data base design. Within the larger context
of the overall design process, the ER model is used in a phase
called “Conceptual database design”.
Database design and ER Diagrams:
The database design process can be divided into six steps. The
ER model is most relevant to the first three steps.
1. Requirement Analysis:
The very first step in designing a database application
is to understand what data is to be stored in the database,
what application must be built in top of it, and what
operations are most frequent and subject to performance
requirements. In other words, we must find out what the users
want from the database.
2. Conceptual database Design:
The information gathered in the requirements analysis
step is used to develop a high-level description of the data
to be stored in the database, along with the constraints known
to hold over this data. The ER model is one of several high-
level or semantic, data models used in database design.
3. Logical Database Design:
We must choose a database to convert the conceptual
database design into a database schema in the data model of
the chosen DBMS. Normally we will consider the Relational DBMS
and therefore, the task in the logical design step is to
convert an ER schema into a relational database schema.
Beyond ER Design
4. Schema Refinement:
This step is to analyze the collection of relations in
our relational database schema to identify potential problems,
and refine it.
5. Physical Database Design:
This step may simply involve building indexes on some
table and clustering some tables or it may involve substantial
redesign of parts of database schema obtained from the earlier
steps.
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6. Application and security Design:
Any software project that involves a DBMS must consider
aspects of the application that go beyond the database itself.
We must describe the role of each entity (users, user group,
departments)in every process that is reflected in some
application task, as part of a complete workflow for the task.
A DBMS Provides several mechanisms to assist in this step.
Entity Types, Attributes and Keys:
Entities are specific objects or things in the mini-world
that are represented in the database.
For example, Employee or staff, Department or Branch , Project
are called Entity.
Attributes are properties used to describe an entity.
For example an EMPLOYEE entity may have a Name, SSN, Address,
Sex, BirthDate and Department may have a Dname, Dno,
DLocation.
A specific entity will have a value for each of its
attributes.
For example a specific employee entity may have Name='John
Smith', SSN='123456789', Address ='731, Fondren, Houston, TX',
Sex='M', BirthDate='09-JAN-55‘
Each attribute has a value set (or data type) associated
with it – e.g. integer, string, subrange, enumerated type, …
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Types of Attributes:
 Simple (Atomic) Vs. Composite
 Single Valued Vs. Multi Valued
 Stored Vs. Derived
 Null Values
Simple Vs. Composite Attribute:
 Simple
– Attribute that are not divisible are called simple
or atomic attribute.
For example, Street_name or Door_number. i.e. the
division of composite attribute.
 Composite
– The attribute may be composed of several components.
For example, Address (Apt#, House#, Street, City,
State, ZipCode, Country) or Name (FirstName,
MiddleName, LastName). Composition may form a hierarchy
where some components are themselves composite.
Single Valued Vs. Multi Valued:
 Single Valued
– An attribute having only one value.
For example, Age, Date of birth, Sex, SSN
 Multi-valued
– An entity may have multiple values for that
attribute.
For example, Color of a CAR or Previous Degrees of a
STUDENT. Denoted as {Color} or {PreviousDegrees}.Phone
number of an employee.
Stored Vs. Derived
In some cases two are more attributes values are related
– for example the age and date of birth of a person. For a
particular person entity, the value of age can be determined
from the current (today’s) date and the value of that person’s
Birthdate.
The Age attribute is hence called a derived attribute and
is said to be derivable from the Birthdate attribute , which
is called stored attribute.
Null Values
In some cases a particular entity may not have an
applicable value for an attribute. For example, a college
degree attribute applies only to persons with college degrees.
For such situation, a special value called null is created.
Key attributes of an Entity Type:
An important constraint on the entities of an entity type
is the Key or uniqueness constraint on attributes. An entity
type usually has an attribute whose values are distinct for
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each individual entity in the entity set. Such an attribute is
called a Key attribute, and its values can be used to identify
each entity uniquely.
For example name attribute is a key of the company entity
type, because no two companies are allowed to have the same
name. For the person entity type, a typical key attribute is
socialSecurityNumber (SSN). In ER diagrammatic notation, each
key attribute has its name underlined inside the oval.
Relationships and Relationship sets:
A relationship is an association among two or more
entities. For example we may have the relationship that Antony
works in the Marketing department. A relationship type R among
the n entity types E1,E2,… ,En defines a set of associations
or a relationship set- among entities from these entity types.
Informally each relationship instance ri in R is an
association of entities, where the association includes
exactly one entity from each participating entity type. Each
such relationship instance ri represents the facts that the
entities participating in ri are related in some way in the
corresponding mini world situation. For example consider a
relationship type Works_for between the two entity types
Employee and Department, which associates each employee with
the department for which the employee works. Each relationship
instance in the relationship set Works_for associates one
employee entity and one department entity. Figure illustrates
this example.
Degree of a Relationship:
The degree of a relationship is the number of
participating entity types. Hence the above work_for
relationship is of degree two. A relationship type of degree
two is called Binary, and one of degree three is called
Ternary. An example of Ternary relationship is given below.
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Relationship of degree:
o two is binary
o three is ternary
o four is quaternary
Another Example for Binary Relationship
Example for Ternary Relationship.
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Example for quaternary Relationship
Constraints on relationship Types:
Relationship types usually have certain constraints
that limit the possible combinations of entities that may
participate in the corresponding relationship set. These
constraints are determined from the mini world situation that
the relationship represent. For example in the works_for
relationship, if the company has a rule that each employee
must work for exactly one department, that we would like to
describe this constraint in the schema. There are two type.
1. Cardinality Ratio
Describes maximum number of possible relationship
occurrences for an entity participating in a given
relationship type.
2. Participation:
Determines whether all or only some entity occurrences
participate in a relationship.
 Cardinality ratio (of a binary relationship): 1:1, 1:N,
N:1, or M:N
SHOWN BY PLACING APPROPRIATE NUMBER ON THE LINK.
 Participation constraint (on each participating entity
type): total (called existence dependency) or partial.
SHOWN BY DOUBLE LINING THE LINK
NOTE: These are easy to specify for Binary Relationship Types.
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Example for 1:1 relationship
Example for M:N relationships
Alternative (min, max) notation for relationship structural
constraints:
 Specified on each participation of an entity type E in a
relationship type R
 Specifies that each entity e in E participates in at
least min and at most max relationship instances in R
 Default(no constraint): min=0, max=n
 Must have minmax, min0, max 1
 Derived from the knowledge of mini-world constraints
Examples:
 A department has exactly one manager and an employee can
manage at most one department.
– Specify (0,1) for participation of EMPLOYEE in
MANAGES
– Specify (1,1) for participation of DEPARTMENT in
MANAGES
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 An employee can work for exactly one department but a
department can have any number of employees.
– Specify (1,1) for participation of EMPLOYEE in
WORKS_FOR
– Specify (0,n) for participation of DEPARTMENT in
WORKS_FOR
Types of Entity:
1. Strong Entity
a. Entity which has a key attribute in its attribute
list.
2. Weak Entity
a. Entity which doesn’t have the Key attribute.
Weak Entity Sets:
An entity set that does not possess sufficient attributes to
form a primary key is called a weak entity set. One that does
have a primary key is called a strong entity set.
For example,
 The entity set transaction has attributes transaction-
number, date and amount.
 Different transactions on different accounts could share
the same number.
 These are not sufficient to form a primary key (uniquely
identify a transaction).
 Thus transaction is a weak entity set.
For a weak entity set to be meaningful, it must be part of a
one-to-many relationship set. This relationship set should
have no descriptive attributes.
 Member of a strong entity set is a dominant entity.
 Member of a weak entity set is a subordinate entity.
A weak entity set does not have a primary key, but we need a
means of distinguishing among the entities.
The discriminator of a weak entity set is a set of attributes
that allows this distinction to be made.
The primary key of a weak entity set is formed by taking the
primary key of the strong entity set on which its existence
depends (see Mapping Constraints) plus its discriminator.
To illustrate:
 Transaction is a weak entity. It is existence-dependent
on account.
 The primary key of account is account-number.
 Transaction-number distinguishes transaction entities
within the same account (and is thus the discriminator).
 So the primary key for transaction would be (account-
number, transaction-number).
Note: The primary key of a weak entity is found by taking the
primary key of the strong entity on which it is existence-
dependent, plus the discriminator of the weak entity set.
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ER Diagram
Notations for ER Diagram
Sample ER diagram for a Company Schema with structural
Constraints is shown below.
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Identifying Relationship:
It is a relationship between Strong entity and weak
entity.
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An ER Diagram for a Bank database Schema
Enhanced-ER (EER) Model Concepts
 It Includes all modeling concepts of basic ER
 Additional concepts: subclasses/super classes,
specialization/generalization,
 The resulting model is called the enhanced-ER or Extended
ER (E2R or EER) model
 It is used to model applications more completely and
accurately if needed
 It includes some object-oriented concepts, such as
inheritance
Subclasses and Super classes:
 An entity type may have additional meaningful sub
groupings of its entities
 Example: EMPLOYEE may be further grouped into SECRETARY,
ENGINEER, MANAGER, TECHNICIAN, SALARIED_EMPLOYEE,
HOURLY_EMPLOYEE,…
o Each of these groupings is a subset of EMPLOYEE
entities
o Each is called a subclass of EMPLOYEE
o EMPLOYEE is the super class for each of these
subclasses
 These are called super class/subclass relationships
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Specialization:
Is the process of defining a set of subclasses of a super
class. The set of subclasses is based upon some distinguishing
characteristics of the entities in the super class
For Example,
{SECRETARY, ENGINEER, TECHNICIAN}
is a specialization of EMPLOYEE based upon job type.
Another specialization of EMPLOYEE based on the method of pay
is,
{SALARIED_EMPLOYEE, HOURLY_EMPLOYEE}.
Super class/subclass relationships and specialization can be
diagrammatically represented in EER diagrams. Attributes of a
subclass are called specific attributes.
For example, Typing Speed of SECRETARY
The subclass can participate in specific relationship types.
For example, BELONGS_TO of HOURLY_EMPLOYEE
Figure shows the Specialization of an Employee based on Job
Type.
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We may have several specializations of the same super class
Generalization:
 The reverse of the specialization process
 Several classes with common features are generalized into
a super class; original classes become its subclasses
 Example: CAR, TRUCK generalized into VEHICLE; both CAR,
TRUCK become subclasses of the super class VEHICLE.
o We can view {CAR, TRUCK} as a specialization of
VEHICLE
o Alternatively, we can view VEHICLE as a
generalization of CAR and TRUCK
FIGURE Generalization.
(a) Two entity types, CAR and TRUCK.
(b) Generalizing CAR and TRUCK into the superclass VEHICLE.
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Constraints on Specialization and generalization
1. Predicate Defined
 If we can determine exactly those entities that will
become members of each subclass by a condition, the
subclasses are called predicate-defined (or condition-
defined) subclasses
o Condition is a constraint that determines subclass
members
o Display a predicate-defined subclass by writing the
predicate condition next to the line attaching the
subclass to its super class
2.Attribute Defined:
 If all subclasses in a specialization have membership
condition on same attribute of the super class,
specialization is called an attribute defined-
specialization
 Attribute is called the defining attribute of the
specialization
 Example: Job Type is the defining attribute of the
specialization {SECRETARY, TECHNICIAN, ENGINEER} of
EMPLOYEE
3. User Defined
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 subclass is called user-defined
o Membership in a subclass is determined by the
database users by applying an operation to add an
entity to the subclass
o Membership in the subclass is specified individually
for each entity in the superclass by the user
The figure shows the constraints on Specialization &
Generalization
4. Disjointness Constraint:
 Specifies that the subclasses of the specialization must
be disjointed (an entity can be a member of at most one
of the subclasses of the specialization)
 Specified by ‘d’ in EER diagram
 If not disjointed, overlap; that is the same entity may
be a member of more than one subclass of the
specialization
 Specified by ‘o’ in EER diagram
5. Completeness Constraint:
 Total specifies that every entity in the superclass must
be a member of some subclass in the specialization/
generalization
 Shown in EER diagrams by a double line
 Partial allows an entity not to belong to any of the
subclasses
 Shown in EER diagrams by a single line
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Example of disjoint partial Specialization
 Hence, we have four types of specialization /
generalization:
o Disjoint, total
o Disjoint, partial
o Overlapping, total
o Overlapping, partial
 Note: Generalization usually is total because the super
class is derived from the subclasses.
Specialization / Generalization Hierarchies, Lattices and
Shared Subclasses:
 A subclass may itself have further subclasses specified
on it
 Forms a hierarchy or a lattice
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 Hierarchy has a constraint that every subclass has only
one superclass (called single inheritance)
 In a lattice, a subclass can be subclass of more than
one superclass (called multiple inheritance)
 In a lattice or hierarchy, a subclass inherits
attributes not only of its direct superclass, but also
of all its predecessor superclasses
A specialization lattice with shared subclass
ENGINEERING_MANAGER
An EER Diagram Example
Specialization / Generalization Lattice Example (UNIVERSITY)
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Chapter 03
Relational Model
Relational Model Terminology :
 A relation is a table with columns and rows.
– Only applies to logical structure of the database,
not the physical structure.
 Attribute is a named column of a relation.
 Domain is the set of allowable values for one or more
attributes.
 Tuple is a row of a relation.
 Degree is the number of attributes in a relation.
 Cardinality is the number of tuples in a relation.
 Relational Database is a collection of normalized
relations with distinct relation names.
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Examples of Attribute Domains
Alternative Terminology for Relational Model
Mathematical Definition of Relation
 Consider two sets, D1 & D2, where D1 = {2, 4} and D2 =
{1, 3, 5}.
 Cartesian product, D1 ´ D2, is set of all ordered pairs,
where first element is member of D1 and second element is
member of D2.
D1 X D2 = {(2, 1), (2, 3), (2, 5), (4, 1), (4, 3), (4, 5)}
 Alternative way is to find all combinations of elements
with first from D1 and second from D2.
 Any subset of Cartesian product is a relation; e.g.
R = {(2, 1), (4, 1)}
 May specify which pairs are in relation using some
condition for selection; e.g.
– second element is 1:
R = {(x, y) | x ÎD1, y ÎD2, and y = 1}
– first element is always twice the second:
S = {(x, y) | x ÎD1, y ÎD2, and x = 2y}
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 Consider three sets D1, D2, D3 with Cartesian Product D1
X D2 X D3; e.g.
D1 = {1, 3} D2 = {2, 4} D3 = {5, 6}
D1 X D2 X D3 = {(1,2,5), (1,2,6), (1,4,5), (1,4,6),
(3,2,5), (3,2,6), (3,4,5), (3,4,6)}
 Any subset of these ordered triples is a relation.
 The Cartesian product of n sets (D1, D2, . . ., Dn) is:
D1 X D2 X . . . X Dn = {(d1, d2, . . . , dn) | d1 ÎD1, d2 ÎD2,
. . . , dnÎDn}
usually written as:
n
XDi
i = 1
 Any set of n-tuples from this Cartesian product is a
relation on the n sets.
Database Relations
 Relation schema
– Named relation defined by a set of attribute and
domain name pairs.
 Relational database schema
– Set of relation schemas, each with a distinct name.
Properties of Relations
 Relation name is distinct from all other relation names
in relational schema.
 Each cell of relation contains exactly one atomic
(single) value.
 Each attribute has a distinct name.
 Values of an attribute are all from the same domain.
 Each tuple is distinct; there are no duplicate tuples.
 Order of attributes has no significance.
 Order of tuples has no significance, theoretically.
Relational Keys
 Superkey
– An attribute, or a set of attributes, that uniquely
identifies a tuple within a relation.
 Candidate Key
– Superkey (K) such that no proper subset is a
superkey within the relation.
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– In each tuple of R, values of K uniquely identify
that tuple (uniqueness).
– No proper subset of K has the uniqueness property
(irreducibility).
 Primary Key
– Candidate key selected to identify tuples uniquely
within relation.
 Alternate Keys
– Candidate keys that are not selected to be primary
key.
 Foreign Key
– Attribute, or set of attributes, within one relation
that matches candidate key of some (possibly same)
relation.
Relational Integrity
 Null
– Represents value for an attribute that is currently
unknown or not applicable for tuple.
– Deals with incomplete or exceptional data.
– Represents the absence of a value and is not the
same as zero or spaces, which are values.
 Entity Integrity
– In a base relation, no attribute of a primary key
can be null.
 Referential Integrity
– If foreign key exists in a relation, either foreign
key value must match a candidate key value of some
tuple in its home relation or foreign key value must
be wholly null.
 Enterprise Constraints
– Additional rules specified by users or database
administrators.
Views
 Base Relation
– Named relation corresponding to an entity in
conceptual schema, whose tuples are physically
stored in database.
 View
– Dynamic result of one or more relational operations
operating on base relations to produce another
relation.
 A virtual relation that does not necessarily actually
exist in the database but is produced upon request, at
time of request.
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 Contents of a view are defined as a query on one or more
base relations.
 Views are dynamic, meaning that changes made to base
relations that affect view attributes are immediately
reflected in the view.
Purpose of Views
 Provides powerful and flexible security mechanism by
hiding parts of database from certain users.
 Permits users to access data in a customized way, so that
same data can be seen by different users in different
ways, at same time.
 Can simplify complex operations on base relations.
Updating Views
 All updates to a base relation should be immediately
reflected in all views that reference that base relation.
 If view is updated, underlying base relation should
reflect change.
 There are restrictions on types of modifications that can
be made through views:
 Updates are allowed if query involves a single base
relation and contains a candidate key of base
relation.
 Updates are not allowed involving multiple base
relations.
 Updates are not allowed involving aggregation or
grouping operations.
 Classes of views are defined as:
– theoretically not updateable;
– theoretically updateable;
– partially updateable.
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Chapter 05
Query Languages: Relational Algebra :
Introduction
 Relational algebra and relational calculus are formal
languages associated with the relational model.
 Informally, relational algebra is a (high-level)
procedural language and relational calculus a non-
procedural language.
 However, formally both are equivalent to one another.
 A language that produces a relation that can be derived
using relational calculus is relationally complete.
Relational Algebra
 Relational algebra operations work on one or more
relations to define another relation without changing the
original relations.
 Both operands and results are relations, so output from
one operation can become input to another operation.
 Allows expressions to be nested, just as in arithmetic.
This property is called closure.
 Five basic operations in relational algebra: Selection,
Projection, Cartesian product, Union, and Set
Difference.
 These perform most of the data retrieval operations
needed.
 Also have Join, Intersection, and Division operations,
which can be expressed in terms of 5 basic operations.
Relational Algebra Operations
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Selection (or Restriction)
  predicate (R)
– Works on a single relation R and defines a relation
that contains only those tuples (rows) of R that
satisfy the specified condition (predicate).
–
Example - Selection (or Restriction)
 List all staff with a salary greater than £10,000.
 salary > 10000 (Staff)
Projection
  col1, . . . , coln(R)
– Works on a single relation R and defines a relation
that contains a vertical subset of R, extracting the
values of specified attributes and eliminating
duplicates.
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Example - Projection
 Produce a list of salaries for all staff, showing only
staffNo, fName, lName, and salary details.
staffNo, fName, lName, salary(Staff)
Union
 R  S
– Union of two relations R and S defines a relation
that contains all the tuples of R, or S, or both R
and S, duplicate tuples being eliminated.
– R and S must be union-compatible.
 If R and S have I and J tuples, respectively, union is
obtained by concatenating them into one relation with a
maximum of (I + J) tuples.
Example - Union
 List all cities where there is either a branch office or a
property for rent.
 city(Branch)   city(PropertyForRent)
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Set Difference
 R – S
– Defines a relation consisting of the tuples that are
in relation R, but not in S.
Example - Set Difference
 List all cities where there is a branch office but no
properties for rent.
 city(Branch) –  city(PropertyForRent)
Intersection
 R  S
– Defines a relation consisting of the set of all
tuples that are in both R and S.
 Expressed using basic operations:
R  S = R – (R – S)
Example – Intersection
 List all cities where there is both a branch office and
at least one property for rent.
 city(Branch)   city(PropertyForRent)
Cartesian product
 R X S
– Defines a relation that is the concatenation of
every tuple of relation R with every tuple of
relation S.
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Example - Cartesian product
 List the names and comments of all clients who have
viewed a property for rent.
(clientNo, fName, lName(Client)) X ( clientNo, propertyNo, comment (Viewing))
Example - Cartesian product and Selection
 Use selection operation to extract those tuples where
Client.clientNo = Viewing.clientNo.
 Client.clientNo = Viewing.clientNo(( clientNo, fName, lName(Client))  (
clientNo, propertyNo, comment(Viewing)))
 Cartesian product and Selection can be reduced to a
single operation called a Join.
Join Operations
 Join is a derivative of Cartesian product.
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 Equivalent to performing a Selection, using join
predicate as selection formula, over Cartesian product of
the two operand relations.
 One of the most difficult operations to implement
efficiently in an RDBMS and one reason why RDBMSs have
intrinsic performance problems.
 Various forms of join operation
 Theta join
 Equijoin (a particular type of Theta join)
 Natural join
 Outer join
 Semijoin
Theta join (-join)
 R FS
– Defines a relation that contains tuples satisfying
the predicate F from the Cartesian product of R and
S.
– The predicate F is of the form R.ai  S.bi where 
may be one of the comparison operators (<, , >, ,
=, ).
 Can rewrite Theta join using basic Selection and
Cartesian product operations.
R FS = F(R  S)
 Degree of a Theta join is sum of degrees of the operand
relations R and S. If predicate F contains only equality
(=), the term Equijoin is used.
Example - Equijoin
(clientNo, fName, lName(Client)) Client.clientNo = Viewing.clientNo
( clientNo, propertyNo, comment(Viewing))
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Natural join
 R S
– An Equijoin of the two relations R and S over all
common attributes x. One occurrence of each common
attribute is eliminated from the result.
Example - Natural join
( clientNo, fName, lName(Client))
( clientNo, propertyNo, comment(Viewing))
Outer join
 To display rows in the result that do not have matching
values in the join column, use Outer join.
 R S
– (Left) outer join is join in which tuples from R
that do not have matching values in common columns
of S are also included in result relation.
Example - Left Outer join
 Produce a status report on property viewings.
 propertyNo, street, city(PropertyForRent)
Viewing
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 R F S
– Defines a relation that contains the tuples of R
that participate in the join of R with S.
– Can rewrite Semijoin using Projection and Join:
– R F S = A(R F S)
Example - Semijoin
 List complete details of all staff who work at the branch
in Glasgow.
Staff Staff.branchNo = Branch.branchNo and Branch.city = ‘Glasgow’ Branch
Division
 R  S
– Defines a relation over the attributes C that consists
of set of tuples from R that match combination of every
tuple in S.
 Expressed using basic operations:
T1  C(R)
T2  C((S X T1) – R)
T  T1 – T2
Example - Division
 Identify all clients who have viewed all properties with
three rooms.
( clientNo, propertyNo(Viewing))  ( propertyNo( rooms = 3
(PropertyForRent)))
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Relational Calculus
 Relational calculus query specifies what is to be
retrieved rather than how to retrieve it.
– No description of how to evaluate a query.
 In first-order logic (or predicate calculus), predicate
is a truth-valued function with arguments.
 When we substitute values for the arguments, function
yields an expression, called a proposition, which can be
either true or false.
 If predicate contains a variable (e.g. ‘x is a member of
staff’), there must be a range for x.
 When we substitute some values of this range for x,
proposition may be true; for other values, it may be
false.
 When applied to databases, relational calculus has forms:
tuple and domain.
Tuple Relational Calculus
 Interested in finding tuples for which a predicate is
true. Based on use of tuple variables.
 Tuple variable is a variable that ‘ranges over’ a named
relation: i.e., variable whose only permitted values are
tuples of the relation.
 Specify range of a tuple variable S as the Staff relation
as:
Staff(S)
 To find set of all tuples S such that P(S) is true:
{S | P(S)}
Tuple Relational Calculus - Example
 To find details of all staff earning more than £10,000:
{S | Staff(S)  S.salary > 10000}
 To find a particular attribute, such as salary, write:
{S.salary | Staff(S)  S.salary > 10000}
Query-by-Example (QBE)
 Visual approach for accessing information in a database
through use of query templates.
 Example values are entered into template to represent
what access to database is to achieve, such as the answer
to a query.
 Originally developed by IBM in 1970s and has proved so
popular that QBE (or similar) is now provided by most
DBMSs.
 When user constructs a QBE - in background, DBMS creates
an equivalent SQL statement.
 Allows user to:
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- Ask questions about data in one or more tables.
- Specify the fields we want in the answer.
- Select records according to some criteria.
- Perform calculations on the data in tables.
- Insert and delete records.
- Modify values of fields.
- Create new fields and tables
Introduction to Microsoft Access
Summary of Microsoft Access Query Types
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Introduction to Microsoft Access Queries
Building Select Queries using QBE - Specifying Criteria
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Chapter : 06
SQL
Objectives of SQL
 Ideally, database language should allow user to:
– create the database and relation structures;
– perform insertion, modification, deletion of data
from relations;
– perform simple and complex queries.
 Must perform these tasks with minimal user effort and
command structure/syntax must be easy to learn.
 It must be portable.
 SQL is a transform-oriented language with 2 major
components:
– A DDL for defining database structure.
– A DML for retrieving and updating data.
 Until SQL3, SQL did not contain flow of control commands.
These had to be implemented using a programming or job-
control language, or interactively by the decisions of
user.
 SQL is relatively easy to learn:
– it is non-procedural - you specify what information
you require, rather than how to get it;
– it is essentially free-format.
 Consists of standard English words:
1) CREATE TABLE Staff(staffNo VARCHAR(5),
lName VARCHAR(15),
salary DECIMAL(7,2));
2) INSERT INTO Staff VALUES (‘SG16’, ‘Brown’, 8300);
3) SELECT staffNo, lName, salary
FROM Staff
WHERE salary > 10000;
 Can be used by range of users including DBAs, management,
application developers, and other types of end users.
 An ISO standard now exists for SQL, making it both the
formal and de facto standard language for relational
databases.
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History of SQL
 In 1974, D. Chamberlin (IBM San Jose Laboratory) defined
language called ‘Structured English Query Language’
(SEQUEL).
 A revised version, SEQUEL/2, was defined in 1976 but name
was subsequently changed to SQL for legal reasons.
 Still pronounced ‘see-quel’, though official
pronunciation is ‘S-Q-L’.
 IBM subsequently produced a prototype DBMS called System
R, based on SEQUEL/2.
 Roots of SQL, however, are in SQUARE (Specifying Queries
as Relational Expressions), which predates System R
project.
 In late 70s, ORACLE appeared and was probably first
commercial RDBMS based on SQL.
 In 1987, ANSI and ISO published an initial standard for
SQL.
 In 1989, ISO published an addendum that defined an
‘Integrity Enhancement Feature’.
 In 1992, first major revision to ISO standard occurred,
referred to as SQL2 or SQL/92.
 In 1999, SQL3 was released with support for object-
oriented data management.
Importance of SQL
 SQL has become part of application architectures such as
IBM’s Systems Application Architecture.
 It is strategic choice of many large and influential
organizations (e.g. X/OPEN).
 SQL is Federal Information Processing Standard (FIPS) to
which conformance is required for all sales of databases
to American Government.
 SQL is used in other standards and even influences
development of other standards as a definitional tool.
Examples include:
– ISO’s Information Resource Directory System (IRDS)
Standard
– Remote Data Access (RDA) Standard.
Writing SQL Commands
 SQL statement consists of reserved words and user-defined
words.
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– Reserved words are a fixed part of SQL and must be spelt
exactly as required and cannot be split across lines.
– User-defined words are made up by user and represent
names of various database objects such as relations,
columns, views.
 Most components of an SQL statement are case insensitive,
except for literal character data.
 More readable with indentation and lineation:
– Each clause should begin on a new line.
– Start of a clause should line up with start of other
clauses.
– If clause has several parts, should each appear on a
separate line and be indented under start of clause.
 Use extended form of BNF notation:
- Upper-case letters represent reserved words.
- Lower-case letters represent user-defined words.
- | indicates a choice among alternatives.
- Curly braces indicate a required element.
- Square brackets indicate an optional element.
- … indicates optional repetition (0 or more).
Literals
 Literals are constants used in SQL statements.
 All non-numeric literals must be enclosed in single
quotes (e.g. ‘London’).
 All numeric literals must not be enclosed in quotes (e.g.
650.00).
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SELECT Statement
SELECT [DISTINCT | ALL]
{* | [columnExpression [AS newName]] [,...] }
FROM TableName [alias] [, ...]
[WHERE condition]
[GROUP BY columnList] [HAVING condition]
[ORDER BY columnList]
FROM Specifies table(s) to be used.
WHERE Filters rows.
GROUP BY Forms groups of rows with same column value.
HAVING Filters groups subject to some condition.
SELECT Specifies which columns are to appear in output.
ORDER BY Specifies the order of the output.
 Order of the clauses cannot be changed.
 Only SELECT and FROM are mandatory.
Example 5.1 All Columns, All Rows
List full details of all staff.
SELECT staffNo, fName, lName, address,
position, sex, DOB, salary, branchNo
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FROM Staff;
 Can use * as an abbreviation for ‘all columns’:
SELECT *
FROM Staff;
Specific Columns, All Rows
Produce a list of salaries for all staff, showing only staff
number, first and last names, and salary.
SELECT staffNo, fName, lName, salary
FROM Staff;
Use of DISTINCT
List the property numbers of all properties that have been
viewed.
SELECT propertyNo
FROM Viewing;
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 Use DISTINCT to eliminate duplicates:
SELECT DISTINCT propertyNo
FROM Viewing;
Calculated Fields
Produce a list of monthly salaries for all staff, showing
staff number, first and last names, and salary details.
SELECT staffNo, fName, lName, salary/12
FROM Staff;
 To name column, use AS clause:
SELECT staffNo, fName, lName, salary/12
AS monthlySalary
FROM Staff;
Comparison Search Condition
List all staff with a salary greater than 10,000.
SELECT staffNo, fName, lName, position, salary
FROM Staff
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Compound Comparison Search Condition
List addresses of all branch offices in London or Glasgow.
SELECT *
FROM Branch
WHERE city = ‘London’ OR city = ‘Glasgow’;
Range Search Condition
List all staff with a salary between 20,000 and 30,000.
FROM Staff
WHERE salary BETWEEN 20000 AND 30000;
 BETWEEN test includes the endpoints of range.
 Also a negated version NOT BETWEEN.
 BETWEEN does not add much to SQL’s expressive power.
Could also write:
FROM Staff
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WHERE salary>=20000 AND salary <= 30000;
 Useful, though, for a range of values.
Set Membership
List all managers and supervisors.
SELECT staffNo, fName, lName, position
FROM Staff
WHERE position IN (‘Manager’, ‘Supervisor’);
 There is a negated version (NOT IN).
 IN does not add much to SQL’s expressive power.
 Could have expressed this as:
FROM Staff
WHERE position=‘Manager’ OR
position=‘Supervisor’;
 IN is more efficient when set contains many values
Pattern Matching
Find all owners with the string ‘Glasgow’ in their address.
SELECT clientNo, fName, lName, address, telNo
FROM PrivateOwner
WHERE address LIKE ‘%Glasgow%’;
 SQL has two special pattern matching symbols:
– %: sequence of zero or more characters;
– _ (underscore): any single character.
 LIKE ‘%Glasgow%’ means a sequence of characters of any
length containing ‘Glasgow’.
NULL Search Condition
List details of all viewings on property PG4 where a comment
has not been supplied.
 There are 2 viewings for property PG4, one with and one
without a comment.
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 Have to test for null explicitly using special keyword IS
NULL:
SELECT clientNo, viewDate
FROM Viewing
WHERE propertyNo = ‘PG4’ AND
comment IS NULL;
 Negated version (IS NOT NULL) can test for non-null
values.
Single Column Ordering
List salaries for all staff, arranged in descending order of
salary.
SELECT staffNo, fName, lName, salary
FROM Staff
ORDER BY salary DESC;
Multiple Column Ordering
Produce abbreviated list of properties in order of property
type.
SELECT propertyNo, type, rooms, rent
FROM PropertyForRent
ORDER BY type;
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 Four flats in this list - as no minor sort key specified,
system arranges these rows in any order it chooses.
 To arrange in order of rent, specify minor order:
SELECT propertyNo, type, rooms, rent
ORDER BY type, rent DESC;
SELECT Statement – Aggregates
 ISO standard defines five aggregate functions:
COUNT returns number of values in specified column.
SUM returns sum of values in specified column.
AVG returns average of values in specified column.
MIN returns smallest value in specified column.
MAX returns largest value in specified column.
 Each operates on a single column of a table and returns a
single value.
 COUNT, MIN, and MAX apply to numeric and non-numeric
fields, but SUM and AVG may be used on numeric fields
only.
 Apart from COUNT(*), each function eliminates nulls first
and operates only on remaining non-null values.
 COUNT(*) counts all rows of a table, regardless of
whether nulls or duplicate values occur.
 Can use DISTINCT before column name to eliminate
duplicates.
 DISTINCT has no effect with MIN/MAX, but may have with
SUM/AVG.
 Aggregate functions can be used only in SELECT list and
in HAVING clause.
 If SELECT list includes an aggregate function and there
is no GROUP BY clause, SELECT list cannot reference a
column out with an aggregate function. For example, the
following is illegal:
SELECT staffNo, COUNT(salary)
FROM Staff;
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Use of COUNT(DISTINCT)
How many different properties viewed in May ‘01?
SELECT COUNT(DISTINCT propertyNo) AS count
FROM Viewing
WHERE viewDate BETWEEN ‘1-May-01’
AND ‘31-May-01’;
Use of COUNT and SUM
Find number of Managers and sum of their salaries.
SELECT COUNT(staffNo) AS count,
SUM(salary) AS sum
FROM Staff
WHERE position = ‘Manager’;
Use of MIN, MAX, AVG
Find minimum, maximum, and average staff salary.
SELECT MIN(salary) AS min,
MAX(salary) AS max,
AVG(salary) AS avg
FROM Staff;
SELECT Statement - Grouping
 Use GROUP BY clause to get sub-totals.
 SELECT and GROUP BY closely integrated: each item in
SELECT list must be single-valued per group, and SELECT
clause may only contain:pdfMachine - is a pdf writer that produces quality PDF files with ease!
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– column names
– aggregate functions
– constants
– expression involving combinations of the above.
 All column names in SELECT list must appear in GROUP BY
clause unless name is used only in an aggregate function.
 If WHERE is used with GROUP BY, WHERE is applied first,
then groups are formed from remaining rows satisfying
predicate.
 ISO considers two nulls to be equal for purposes of GROUP
BY.
Find number of staff in each branch and their total salaries.
SELECT branchNo,
COUNT(staffNo) AS count,
SUM(salary) AS sum
FROM Staff
GROUP BY branchNo
ORDER BY branchNo;
Restricted Groupings – HAVING clause
 HAVING clause is designed for use with GROUP BY to
restrict groups that appear in final result table.
 Similar to WHERE, but WHERE filters individual rows
whereas HAVING filters groups.
 Column names in HAVING clause must also appear in the
GROUP BY list or be contained within an aggregate
function.
For each branch with more than 1 member of staff, find number
of staff in each branch and sum of their salaries.
SELECT branchNo,
COUNT(staffNo) AS count,
SUM(salary) AS sum
FROM Staff
GROUP BY branchNo
HAVING COUNT(staffNo) > 1
ORDER BY branchNo;
Subqueries
 Some SQL statements can have a SELECT embedded within
them.
 A subselect can be used in WHERE and HAVING clauses of an
outer SELECT, where it is called a subquery or nested
query.
 Subselects may also appear in INSERT, UPDATE, and DELETE
statements.
List staff who work in branch at ‘163 Main St’.pdfMachine - is a pdf writer that produces quality PDF files with ease!
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FROM Staff
WHERE branchNo =
(SELECT branchNo
FROM Branch
WHERE street = ‘163 Main St’);
Subquery with Equality
 Inner SELECT finds branch number for branch at ‘163 Main
St’ (‘B003’).
 Outer SELECT then retrieves details of all staff who work
at this branch.
 Outer SELECT then becomes:
FROM Staff
WHERE branchNo = ‘B003’;
Subquery with Aggregate
List all staff whose salary is greater than the average
salary, and show by how much.
SELECT staffNo, fName, lName, position,
salary – (SELECT AVG(salary) FROM Staff) As SalDiff
FROM Staff
WHERE salary >
(SELECT AVG(salary)
FROM Staff);
 Cannot write ‘WHERE salary > AVG(salary)’
 Instead, use subquery to find average salary (17000), and
then use outer SELECT to find those staff with salary
greater than this:
SELECT staffNo, fName, lName, position,
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salary – 17000 As salDiff
FROM Staff
Subquery Rules
 ORDER BY clause may not be used in a subquery (although
it may be used in outermost SELECT).
 Subquery SELECT list must consist of a single column name
or expression, except for subqueries that use EXISTS.
 By default, column names refer to table name in FROM
clause of subquery. Can refer to a table in FROM using an
alias.
 When subquery is an operand in a comparison, subquery
must appear on right-hand side.
 A subquery may not be used as an operand in an
expression.
Nested subquery: use of IN
List properties handled by staff at ‘163 Main St’.
SELECT propertyNo, street, city, postcode, type, rooms, rent
WHERE staffNo IN
(SELECT staffNo
FROM Staff
WHERE branchNo =
(SELECT branchNo
FROM Branch
WHERE street = ‘163 Main St’));
Multi-Table Queries
 Can use subqueries provided result columns come from same
table.
 If result columns come from more than one table must use
a join.
 To perform join, include more than one table in FROM
clause.
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 Use comma as separator and typically include WHERE clause
to specify join column(s).
 Also possible to use an alias for a table named in FROM
clause.
 Alias is separated from table name with a space.
 Alias can be used to qualify column names when there is
ambiguity.
Simple Join
List names of all clients who have viewed a property along
with any comment supplied.
SELECT c.clientNo, fName, lName,
propertyNo, comment
FROM Client c, Viewing v
WHERE c.clientNo = v.clientNo;
 Only those rows from both tables that have identical
values in the clientNo columns (c.clientNo = v.clientNo)
are included in result.
 Equivalent to equi-join in relational algebra.
Alternative JOIN Constructs
 SQL provides alternative ways to specify joins:
FROM Client c JOIN Viewing v ON c.clientNo = v.clientNo
FROM Client JOIN Viewing USING clientNo
FROM Client NATURAL JOIN Viewing
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 In each case, FROM replaces original FROM and WHERE.
However, first produces table with two identical clientNo
columns.
Sorting a join
For each branch, list numbers and names of staff who manage
properties, and properties they manage.
SELECT s.branchNo, s.staffNo, fName, lName,
propertyNo
FROM Staff s, PropertyForRent p
WHERE s.staffNo = p.staffNo
ORDER BY s.branchNo, s.staffNo, propertyNo;
Three Table Join
For each branch, list staff who manage properties, including
city in which branch is located and properties they manage.
SELECT b.branchNo, b.city, s.staffNo, fName, lName,
propertyNo
FROM Branch b, Staff s, PropertyForRent p
WHERE b.branchNo = s.branchNo AND
s.staffNo = p.staffNo
ORDER BY b.branchNo, s.staffNo, propertyNo
 Alternative formulation for FROM and WHERE:
FROM (Branch b JOIN Staff s USING branchNo) AS
bs JOIN PropertyForRent p USING staffNo
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Computing a Join
Procedure for generating results of a join are:
1. Form Cartesian product of the tables named in FROM clause.
2. If there is a WHERE clause, apply the search condition to
each row of the product table, retaining those rows that
satisfy the condition.
3. For each remaining row, determine value of each item in
SELECT list to produce a single row in result table.
4. If DISTINCT has been specified, eliminate any duplicate
rows from the result table.
5. If there is an ORDER BY clause, sort result table as
required.
 SQL provides special format of SELECT for Cartesian
product:
SELECT [DISTINCT | ALL] {* | columnList}
FROM Table1 CROSS JOIN Table2
Outer Joins
 If one row of a joined table is unmatched, row is omitted
from result table.
 Outer join operations retain rows that do not satisfy the
join condition.
 Consider following tables:
 Result table has two rows where cities are same.
 There are no rows corresponding to branches in Bristol
and Aberdeen.
 To include unmatched rows in result table, use an Outer
join.
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Left Outer Join
List branches and properties that are in same city along with
any unmatched branches.
SELECT b.*, p.*
FROM Branch1 b LEFT JOIN
PropertyForRent1 p ON b.bCity = p.pCity;
 Includes those rows of first (left) table unmatched with
rows from second (right) table.
 Columns from second table are filled with NULLs.
Right Outer Join
List branches and properties in same city and any unmatched
properties.
SELECT b.*, p.*
FROM Branch1 b RIGHT JOIN
 Right Outer join includes those rows of second (right)
table that are unmatched with rows from first (left)
table.
 Columns from first table are filled with NULLs.
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Full Outer Join
List branches and properties in same city and any unmatched
branches or properties.
SELECT b.*, p.*
FROM Branch1 b FULL JOIN
 Includes rows that are unmatched in both tables.
 Unmatched columns are filled with NULLs.
Integrity Enhancement Feature
 Consider five types of integrity constraints:
– Required data.
– Domain constraints.
– Entity integrity.
– Referential integrity.
– Enterprise constraints.
Integrity Enhancement Feature
Required Data
position VARCHAR(10) NOT NULL
Domain Constraints
(a) CHECK
sex CHAR NOT NULL
CHECK (sex IN (‘M’, ‘F’))
CREATE DOMAIN
CREATE DOMAIN DomainName [AS] dataType
[DEFAULT defaultOption]
[CHECK (searchCondition)]
For example:
CREATE DOMAIN SexType AS CHAR
CHECK (VALUE IN (‘M’, ‘F’));
sex SexType NOT NULL
 searchCondition can involve a table lookup:
CREATE DOMAIN BranchNo AS CHAR(4)
CHECK (VALUE IN (SELECT branchNo
FROM Branch));
 Domains can be removed using DROP DOMAIN:pdfMachine - is a pdf writer that produces quality PDF files with ease!
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DROP DOMAIN DomainName
[RESTRICT | CASCADE]
IEF - Entity Integrity
 Primary key of a table must contain a unique, non-null
value for each row.
 ISO standard supports FOREIGN KEY clause in CREATE and
ALTER TABLE statements:
PRIMARY KEY(staffNo)
PRIMARY KEY(clientNo, propertyNo)
 Can only have one PRIMARY KEY clause per table. Can still
ensure uniqueness for alternate keys using UNIQUE:
UNIQUE(telNo)
IEF - Referential Integrity
 FK is column or set of columns that links each row in
child table containing foreign FK to row of parent table
containing matching PK.
 Referential integrity means that, if FK contains a value,
that value must refer to existing row in parent table.
 ISO standard supports definition of FKs with FOREIGN KEY
clause in CREATE and ALTER TABLE:
FOREIGN KEY(branchNo) REFERENCES Branch
 Any INSERT/UPDATE that attempts to create FK value in
child table without matching candidate key value in
parent is rejected.
 Action taken that attempts to update/delete a candidate
key value in parent table with matching rows in child is
dependent on referential action specified using ON UPDATE
and ON DELETE subclauses:
– CASCADE - SET NULL
– SET DEFAULT - NO ACTION
CASCADE: Delete row from parent and delete matching rows in
child, and so on in cascading manner.
SET NULL: Delete row from parent and set FK column(s) in child
to NULL. Only valid if FK columns are NOT NULL.
SET DEFAULT: Delete row from parent and set each component of
FK in child to specified default. Only valid if DEFAULT
specified for FK columns
NO ACTION: Reject delete from parent. Default.
FOREIGN KEY (staffNo) REFERENCES Staff ON DELETE SET NULLpdfMachine - is a pdf writer that produces quality PDF files with ease!
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FOREIGN KEY (ownerNo) REFERENCES Owner ON UPDATE CASCADE
IEF - Enterprise Constraints
 Could use CHECK/UNIQUE in CREATE and ALTER TABLE.
 Also have:
CREATE ASSERTION AssertionName
CHECK (searchCondition)
 which is very similar to the CHECK clause.
CREATE ASSERTION StaffNotHandlingTooMuch
CHECK (NOT EXISTS (SELECT staffNo
GROUP BY staffNo
HAVING COUNT(*) > 100))
Data Definition
 SQL DDL allows database objects such as schemas, domains,
tables, views, and indexes to be created and destroyed.
 Main SQL DDL statements are:
CREATE SCHEMA DROP SCHEMA
CREATE/ALTER DOMAIN DROP DOMAIN
CREATE/ALTER TABLE DROP TABLE
CREATE VIEW DROP VIEW
 Many DBMSs also provide:
CREATE INDEX DROP INDEX
 Relations and other database objects exist in an
environment.
 Each environment contains one or more catalogs, and each
catalog consists of set of schemas.
 Schema is named collection of related database objects.
 Objects in a schema can be tables, views, domains,
assertions, collations, translations, and character sets.
All have same owner.
CREATE TABLE
CREATE TABLE TableName
{(colName dataType [NOT NULL] [UNIQUE]
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[CHECK searchCondition] [,...]}
[PRIMARY KEY (listOfColumns),]
{[UNIQUE (listOfColumns),] […,]}
{[FOREIGN KEY (listOfFKColumns)
REFERENCES ParentTableName [(listOfCKColumns)],
[ON UPDATE referentialAction]
[ON DELETE referentialAction ]] [,…]}
{[CHECK (searchCondition)] [,…] })
 Creates a table with one or more columns of the specified
dataType.
 With NOT NULL, system rejects any attempt to insert a
null in the column.
 Can specify a DEFAULT value for the column.
 Primary keys should always be specified as NOT NULL.
 FOREIGN KEY clause specifies FK along with the
referential action
ALTER TABLE
 Add a new column to a table.
 Drop a column from a table.
 Add a new table constraint.
 Drop a table constraint.
 Set a default for a column.
 Drop a default for a column.
Change Staff table by removing default of ‘Assistant’ for
position column and setting default for sex column to female
(‘F’).
ALTER TABLE Staff
ALTER position DROP DEFAULT;
ALTER TABLE Staff
ALTER sex SET DEFAULT ‘F’;
Remove constraint from PropertyForRent that staff not allowed
to handle more than 100 properties at time. Add new column to
Client table.
ALTER TABLE PropertyForRent
DROP CONSTRAINT StaffNotHandlingTooMuch;
ALTER TABLE Client
ADD prefNoRooms PRooms;
DROP TABLE
DROP TABLE TableName [RESTRICT | CASCADE]
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 Removes named table and all rows within it.
 With RESTRICT, if any other objects depend for their
existence on continued existence of this table, SQL does
not allow request.
 With CASCADE, SQL drops all dependent objects (and
objects dependent on these objects).
View
Dynamic result of one or more relational operations
operating on base relations to produce another relation.
 Virtual relation that does not necessarily actually exist
in the database but is produced upon request, at time of
request.
 Contents of a view are defined as a query on one or more
base relations.
 With view resolution, any operations on view are
automatically translated into operations on relations
from which it is derived.
 With view materialization, the view is stored as a
temporary table, which is maintained as the underlying
base tables are updated.
SQL - CREATE VIEW
CREATE VIEW ViewName [ (newColumnName [,...]) ]
AS subselect
[WITH [CASCADED | LOCAL] CHECK OPTION]
 Can assign a name to each column in view.
 If list of column names is specified, it must have same
number of items as number of columns produced by
subselect.
 If omitted, each column takes name of corresponding
column in subselect.
 List must be specified if there is any ambiguity in a
column name.
 The subselect is known as the defining query.
 WITH CHECK OPTION ensures that if a row fails to satisfy
WHERE clause of defining query, it is not added to
underlying base table.
 Need SELECT privilege on all tables referenced in
subselect and USAGE privilege on any domains used in
referenced columns.
SQL - DROP VIEW
DROP VIEW ViewName [RESTRICT | CASCADE]
 Causes definition of view to be deleted from database.
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 For example:
DROP VIEW Manager3Staff;
 With CASCADE, all related dependent objects are deleted;
i.e. any views defined on view being dropped.
 With RESTRICT (default), if any other objects depend for
their existence on continued existence of view being
dropped, command is rejected
Restrictions on Views
SQL imposes several restrictions on creation and use of views.
(a) If column in view is based on an aggregate function:
– Column may appear only in SELECT and ORDER BY
clauses of queries that access view.
– Column may not be used in WHERE nor be an argument
to an aggregate function in any query based on view.
 For example, following query would fail:
SELECT COUNT(cnt)
FROM StaffPropCnt;
 Similarly, following query would also fail:
SELECT *
FROM StaffPropCnt
WHERE cnt > 2;
(b) Grouped view may never be joined with a base table or a
view.
 For example, StaffPropCnt view is a grouped view, so any
attempt to join this view with another table or view
fails.
Advantages of Views
 Data independence
 Currency
 Improved security
 Reduced complexity
 Convenience
 Customization
 Data integrity
Disadvantages of Views
 Update restriction
 Structure restriction
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 Performance
Transactions
 SQL defines transaction model based on COMMIT and
ROLLBACK.
 Transaction is logical unit of work with one or more SQL
statements guaranteed to be atomic with respect to
recovery.
 An SQL transaction automatically begins with a
transaction-initiating SQL statement (e.g., SELECT,
INSERT).
 Changes made by transaction are not visible to other
concurrently executing transactions until transaction
completes.
 Transaction can complete in one of four ways:
- COMMIT ends transaction successfully, making changes
permanent.
- ROLLBACK aborts transaction, backing out any changes
made by transaction.
- For programmatic SQL, successful program termination
ends final transaction successfully, even if COMMIT has not
been executed.
- For programmatic SQL, abnormal program end aborts
transaction.
Access Control - Authorization Identifiers and Ownership
 Authorization identifier is normal SQL identifier used to
establish identity of a user. Usually has an associated
password.
 Used to determine which objects user may reference and
what operations may be performed on those objects.
 Each object created in SQL has an owner, as defined in
AUTHORIZATION clause of schema to which object belongs.
 Owner is only person who may know about it.
Privileges
 Actions user permitted to carry out on given base table
or view:
SELECT Retrieve data from a table.
INSERT Insert new rows into a table.
UPDATE Modify rows of data in a table.
DELETE Delete rows of data from a table.
REFERENCES Reference columns of named table in integrity
constraints.
USAGE Use domains, collations, character sets, and
translations.
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 Can restrict INSERT/UPDATE/REFERENCES to named columns.
 Owner of table must grant other users the necessary
privileges using GRANT statement.
 To create view, user must have SELECT privilege on all
tables that make up view and REFERENCES privilege on the
named columns
GRANT
GRANT {PrivilegeList | ALL PRIVILEGES}
ON ObjectName
TO {AuthorizationIdList | PUBLIC}
[WITH GRANT OPTION]
 PrivilegeList consists of one or more of above privileges
separated by commas.
 ALL PRIVILEGES grants all privileges to a user.
 PUBLIC allows access to be granted to all present and
future authorized users.
 ObjectName can be a base table, view, domain, character
set, collation or translation.
 WITH GRANT OPTION allows privileges to be passed on.
Example
Give Manager full privileges to Staff table.
GRANT ALL PRIVILEGES
ON Staff
TO Manager WITH GRANT OPTION;
Give users Personnel and Director SELECT and UPDATE on
column salary of Staff.
GRANT SELECT, UPDATE (salary)
ON Staff
TO Personnel, Director;
GRANT Specific Privileges to PUBLIC
Give all users SELECT on Branch table.
GRANT SELECT
ON Branch
TO PUBLIC;
REVOKE
 REVOKE takes away privileges granted with GRANT.
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REVOKE [GRANT OPTION FOR]
{PrivilegeList | ALL PRIVILEGES}
ON ObjectName
FROM {AuthorizationIdList | PUBLIC}
[RESTRICT | CASCADE]
 ALL PRIVILEGES refers to all privileges granted to a user
by user revoking privileges.
 GRANT OPTION FOR allows privileges passed on via WITH
GRANT OPTION of GRANT to be revoked separately from the
privileges themselves.
 REVOKE fails if it results in an abandoned object, such
as a view, unless the CASCADE keyword has been specified.
 Privileges granted to this user by other users are not
affected.
REVOKE Specific Privileges
Revoke privilege SELECT on Branch table from all users.
REVOKE SELECT
ON Branch
FROM PUBLIC;
Revoke all privileges given to Director on Staff table.
REVOKE ALL PRIVILEGES
ON Staff
FROM Director;
.
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Chapter 07
Overview of Storage and Indexing:
Databases are stored physically as files of records, which are
typically stored on magnetic disks.
Introduction:
The collection of data that makes up a computerized
database must be stored physically on some computer storage
medium. The DBMS software that can then retrieve, update, and
process this data as needed. Computer storage media from a
storage hierarchy that includes two main categories,
 Primary Storage
o The category includes storage media that can be
operated on directly by the computer Central
Processing Unit(CPU), such as the computer main
memory and smaller but faster cache memories.
Primary storage usually provides fast access to data
but is of limited storage capacity.
 Secondary Storage
o This category includes magnetic disks, optical
disks, and tapes. These devices usually have a
larger capacity, cost less, and provide slower
access to data than do primary storage devices. Data
in secondary storage can not be processed directly
by the CPU: it must first be copied into primary
storage.
The storage media are classified by the speed with which
data can be accessed, by the cost per unit of data to buy the
medium, and by the medium’s reliability. Let’s look into the
media that are typically available.
 Cache: The cache is the fastest and most costly form of
storage. Cache memory is small. The computer hardware
manages its use.
 Main Memory: The general purpose machine instructions
operate on main memory. If a power failure or system
crash occurs, the contents of main memory are usually
lost. (i.e.) volatile memory type.
 Flash Memory: Also known as Electrically Erasable
programmable read only memory (EEPROM). Data Survive
power failure in flash memory(Non-volatile Type). Reading
data from flash memory takes less than 100 nanoseconds,
which is as fast as reading data from main memory.
However writing data to flash memory is more complicated.
Data can be written once, but can not be overwritten
directly. To overwrite memory that has been written
already, we have to erase an entire bank of memory once.
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It is used for storing small volumes of data (5-10 MB) in
hand-held computers, digital cameras.
 Magnetic Disk Storage: The primary medium for the long-
term on-line storage of data is the magnetic disk. The
system must move the data from disk to main memory so
that they can be accessed. Disk storage survives power
failures and system crashes. Disk-storage devices
themselves may sometimes fail and thus destroy data, but
such failures do not happen frequently.
 Optical Storage: The popular form of optical storage are
the compact disk(CD) which can hold 640MB of data, and
the digital video disk(DVD), which can hold 4.7 or 8.5 GB
of data per side of the disk. Data are stored optically
on the disk, and are read by the laser. The optical disks
used in read-only compact disks(CD-ROM) or read only
digital video disk(DVD-ROM) can not be written, but are
supplied with the prerecorded data. There are record one
versions of compact disk called (CD-R) and digital
video(DVD-R),which can be written once; such records are
called write once read many(WORM).There are also multiple
write versions of compact disk (CD-RW) and digital video
disk(DVD-RW and DVD-RAM), which can be written multiple
times.
 Tape Storage: It is mainly for back up and archival
data. Although magnetic tape is much cheaper than disks,
access to data is much slower. Because the tape must be
accessed sequentially from the beginning. That’s why the
tape storage is referred to as sequential-access storage.
Storage device Hierarchy
Cache
Main Memory
Flash memory
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Magnetic Disk
Optical Disk
Magnetic Tapes
Magnetic Disks :
The read write head is positioned very close to the platter
surface. We can say almost touching it. It reads or writes
magnetically encoded information. The surface of platter is
divided into circular tracks. There are over 16000 tracks
per platter on typical hard disks. Each track is further
divided into sectors. A sector is defined as the smallest
unit of data that can be read or written. The size of a
sector is typically 512 bytes. There are 200 (on inner
disks) to 400 (on outer disks) typical sectors per track. To
read or write a sector, the disk arm swings to position head
on right track. As the platter spins continually, the data
is read or written as sector passes under head. Let us
understand the head-disk assemblies as follows: Multiple
disk platters are found (typically 2 to 4) on single
spindle. There is one head per platter, which is mounted on
a common arm. Earlier generation disks were vulnerable to
head crashes. Their surface had metal-oxide coatings, which
would disintegrate on head crash and damage all data on
disk. The current generation disks are less susceptible to
such disastrous failures, although individual sectors may
get corrupted.
A disk controller interface between the computer system
and the disk drive hardware. It accepts high-level commands
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to read or write sector, and initiates actions, such as
moving the disk arm to the right track and actually reading
or writing the data. If the data is corrupted, with very
high probability stored checksum won’t match recomputed
checksum. It ensures successful writing by reading back
sector after writing it. It performs remapping of bad
sectors.
Placing File Records on Disk:
Records:
Data is usually stored in the form of records. Each
record consists of a collection of related data values or
items, where each value is formed of one or more bytes and
corresponds to a particular field of the record.
Files, Fixed- Length records and variable length records:
A file is a sequence of records. In many cases all
records in a file are of the same record type. If every
record in the file has exactly the same size(in bytes), the
file is said to be made up of Fixed file records. If
different records in the file have different sizes, the file
is said to be made up of Variable length records.
Fig. Three record storage formats.
(a) A fixed-length record with six fields and size of 71
bytes.
(b) A record with two variable-length fields and three
fixed-length fields.
c) A variable-field record with three types of separator
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Spanned Vs. Unspanned records:
A pointer at the end of the first block points to the
block containing the remainder of the record in case it is
not the next consecutive block on disk. This organization is
called Spanned, because the record can span more than one
block
If records are not allowed to cross block boundaries, the
organization is called unspanned.
FIGURE
Types of record organization. (a) Unspanned. (b) Spanned.
Files of Unordered Records (Heap Files):
In the simplest and most basic type of organization,
records are placed in the file in the order in which they
are inserted, so new records are inserted at the end of the
file. Such an organization is called a heap or pile
file. Inserting a new record is very efficient: the last
disk block of the file is copied into a buffer, the new
record is added; and the block is then rewritten back to the
disk. The address of last file block is kept in the file
header.
Files or Ordered Records (Sorted Files)
We can physically order the records of a file on disk
based on the value of one of their fields- called the
ordering field. This leads to an ordered or sequential file.
If the ordering field is also a key field of the file – a
field guaranteed to have a unique value in each record- then
the field is called the ordering key for the file. Figure
shows an ordered file with name as the ordering key field
assuming that employees having distinct names.)
Figure :
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Some blocks of an ordered (sequential) file of EMPLOYEE
records with NAME as the ordering key field
Hashing Technique:
A type of primary file organization is based on hashing,
which provides very fast access to records on certain search
conditions. This organization is usually called hash file.
The search condition is must be an equality condition on a
single field, called the hash field of the file. In most
cases, the hash field is also a key field of the file, in
which case it is called hash Key. The idea behind hashing is
to provide a function h, called a hash function or
randomizing function that is applied to the hash field value
of a record and yields the address of the disk block in
which the record is stored. A search for the record within
the block can be carried out in a main memory buffer.
Internal Hashing:
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For internal files, hashing is typically implemented as a
hash table through the use of an array of records. Suppose
that the array index range is from 0 to M-1 then we have M
slots whose address corresponds to the array indexes. We
choose a hash function that transforms the hash field value
into an integer between 0 and M-1. One common hash function
is the h(K)=K mod M function, which returns the remainder of
an integer hash field value K after division by M; this
value is then used for the record address.
Non integer hash field values can be transformed into
integers before the mod function applied. For characters
string, the numeric (ASCII) codes associated with characters
can be used in the transformation.
A collision occurs when the hash field value of a record
that is being inserted hashes to an address that already
contains a different record. In this situation, we must
insert the new record in some other position, since its hash
address is occupied. The process of finding another position
is called Collision resolution.
There are numerous methods for collision resolution,
including the following,
1. Open addressing
2. Chaining
3. Multiple hashing
FIGURE: Internal hashing data structures.
(a) Array of M positions for use in internal hashing.
(b) Collision resolution by chaining records.
External Hashing for Disk files:
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Hashing for disk files is called External hashing. To
suit the characteristics of disk storage the target address
space is made of buckets, each of which hold multiple
records. The hashing function maps a key into a relative
bucket number, rather than assign an absolute block address
to the bucket. A table maintained in the file header
converts the bucket number into the corresponding disk block
address, as illustrated in figure.
The collision problem is less severe with buckets, because
as many records as will fit in bucket can hash to the same
bucket without causing problems. However, we must make
provisions for the case where a bucket is filled to capacity
and a new record being inserted hashes to that bucket. We
can use a variation of chaining in which a pointer is
maintained in each bucket to a linked list of overflow
records for the bucket, as shown in the figure.
The pointers in the linked list should be record pointers,
which include both a block address and a relative record
position within the block.
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Indexing structures for files
Primary Indexes(Ordered Indexes)
A primary index is an ordered file whose records are of
fixed length with two fields. The first field is of the same
data type as the ordering key field- called the primary key-
of the data file, and the second field is a pointer to a disk
block( a block address). There is one index entry (or index
record) in the index file for each block in the data file.
Each index entry has the value of the primary key field for
the first record in a block and a pointer to that block as its
two field values. We will refer to the two field values of
index entry i as <K(I,P(i)>
Indexes can also be characterized as dense or sparse. A dense
index has an index entry for the every search key value( and
hence every record) in the data file. A sparse (or non dense)
index on the other hand, has index entries only for some of
the search values. A primary index is hence a non
dense(sparse) index, since it includes an entry for each disk
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block of the data file and the keys of its anchor record
rather than for every search values( or every record).
Clustering Indexes:
If records of a file are physically ordered on a non key
field- which does not have a distinct value for each record-
that field is called the clustering field. We can create a
different type of index, called clustering index, to speed up
the retrieval of records that have the same value for the
clustering field. This differs from a primary index, which
requires that the ordering field of the data file have a
distinct value for each record. A clustering index is also an
ordered file with two fields.
Figure: A clustering index on the DEPTNUMBER ordering non key
field of an EMPLOYEE file.
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Figure : Clustering index with a separate block cluster for
each group of records that share the same value for the
clustering field
Secondary Indexes (Unordered or dense Index)
A secondary index provides a secondary means of accessing
a file for which some primary access already exists. The index
is an ordered file with two fields. The first field is of the
same data type as some non ordering field of the data file
that is an indexing field. The second field is either the
block pointer or a record pointer. There can be many secondary
indexes (and hence indexing field) for the same file.
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Figure: A dense secondary index (with block pointers) on a
nonordering key field of a file.
Multilevel Indexes:
The idea behind the multilevel index is to reduce the
part of the index that we continue to search the blocking
factor is larger than two. Hence the search space is reduced
mush faster.
The following figure explains this concept
A secondary index (with recorded pointers) on a non key field
implemented using one level of indirection so that index
entries are of fixed length and have unique field values.
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Chapter 08
Query Evaluation overview
Introduction
 In network and hierarchical DBMSs, low-level procedural
query language is generally embedded in high-level
programming language.
 Programmer’s responsibility to select most appropriate
execution strategy.
 With declarative languages such as SQL, user specifies what
data is required rather than how it is to be retrieved.
 Relieves user of knowing what constitutes good execution
strategy.
 Also gives DBMS more control over system performance.
 Two main techniques for query optimization:
 heuristic rules that order operations in a query;
 comparing different strategies based on relative costs, and
selecting one that minimizes resource usage.
 Disk access tends to be dominant cost in query processing
for centralized DBMS.
Query Processing
Activities involved in retrieving data from the database.
 Aims of QP:
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– Transform query written in high-level language (e.g.
SQL), into correct and efficient execution strategy
expressed in low-level language (implementing RA);
– execute strategy to retrieve required data.
Query Optimization
Activity of choosing an efficient execution strategy for
processing query.
 As there are many equivalent transformations of same
high-level query, aim of QO is to choose one that
minimizes resource usage.
 Generally, reduce total execution time of query.
 May also reduce response time of query.
 Problem computationally intractable with large number of
relations, so strategy adopted is reduced to finding near
optimum solution.
Example - Different Strategies
Find all Managers who work at a London branch.
SELECT *
FROM Staff s, Branch b
WHERE s.branchNo = b.branchNo AND
(s.position = ‘Manager’ AND b.city = ‘London’);
 Three equivalent RA queries are:
(1)  (position='Manager')  (city='London') 
(Staff.branchNo=Branch.branchNo) (Staff X Branch)
(2)  (position='Manager')  (city='London')(Staff
Staff.branchNo=Branch.branchNo Branch)
(3) ( position='Manager'(Staff) Staff.branchNo=Branch.branchNo
( city='London' (Branch))
 Assume:
– 1000 tuples in Staff; 50 tuples in Branch;
– 50 Managers; 5 London branches;
– no indexes or sort keys;
– results of any intermediate operations stored on
disk;
– cost of the final write is ignored;
– tuples are accessed one at a time.pdfMachine - is a pdf writer that produces quality PDF files with ease!
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Cost Comparison
 Cost (in disk accesses) are:
(1) (1000 + 50) + 2*(1000 * 50) = 101 050
(2) 2*1000 + (1000 + 50) = 3 050
(3) 1000 + 2*50 + 5 + (50 + 5) = 1 160
 Cartesian product and join operations much more expensive
than selection, and third option significantly reduces
size of relations being joined together.
Phases of Query Processing
 QP has four main phases:
– decomposition (consisting of parsing and
validation);
– optimization;
– code generation;
– execution.
Dynamic versus Static Optimization
 Two times when first three phases of QP can be carried
out:
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– dynamically every time query is run;
– statically when query is first submitted.
 Advantages of dynamic QO arise from fact that information
is up to date.
 Disadvantages are that performance of query is affected,
time may limit finding optimum strategy.
 Advantages of static QO are removal of runtime overhead,
and more time to find optimum strategy.
 Disadvantages arise from fact that chosen execution
strategy may no longer be optimal when query is run.
 Could use a hybrid approach to overcome this.
Cost Estimation for RA Operations
 Many different ways of implementing RA operations.
 Aim of QO is to choose most efficient one.
 Use formulae that estimate costs for a number of options,
and select one with lowest cost.
 Consider only cost of disk access, which is usually
dominant cost in QP.
 Many estimates are based on cardinality of the relation,
so need to be able to estimate this.
Database Statistics
 Success of estimation depends on amount and currency of
statistical information DBMS holds.
 Keeping statistics current can be problematic.
 If statistics updated every time tuple is changed, this
would impact performance.
 DBMS could update statistics on a periodic basis, for
example nightly, or whenever the system is idle.
Typical Statistics for Relation R
nTuples(R) - number of tuples in R.
bFactor(R) - blocking factor of R.
nBlocks(R) - number of blocks required to store R:
nBlocks(R) = [nTuples(R)/bFactor(R)]
Query Optimization in Oracle
 Oracle supports two approaches to query optimization:
rule-based and cost-based.
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Rule-based
 15 rules, ranked in order of efficiency. Particular
access path for a table only chosen if statement contains
a predicate or other construct that makes that access
path available.
 Score assigned to each execution strategy using these
rankings and strategy with best (lowest) score selected.
 When 2 strategies produce same score, tie-break resolved
by making decision based on order in which tables occur
in the SQL statement.
QO in Oracle – Rule-based: Example
SELECT propertyNo
WHERE rooms > 7 AND city = ‘London’
 Single-column access path using index on city from WHERE
condition (city = ‘London’). Rank 9.
 Unbounded range scan using index on rooms from WHERE
condition (rooms > 7). Rank 11.
 Full table scan - rank 15.
 Although there is index on propertyNo, column does not
appear in WHERE clause and so is not considered by
optimizer.
 Based on these paths, rule-based optimizer will choose to
use index based on city column.
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QO in Oracle – Cost-Based
 To improve QO, Oracle introduced cost-based optimizer in
Oracle 7, which selects strategy that requires minimal
resource use necessary to process all rows accessed by
query (avoiding above tie-break anomaly).
 User can select whether minimal resource usage is based
on throughput or based on response time, by setting the
OPTIMIZER_MODE initialization parameter.
 Cost-based optimizer also takes into consideration hints
that the user may provide.
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Chapter 9
Transaction Processing:
Transaction:
Action, or series of actions, carried out by user or
application, which accesses or changes contents of database.
It Transforms database from one consistent state to another,
although consistency may be violated during transaction.
Single User Vs. Multi User Systems:
A DBMS is a single user if at most one user at a time can
use the system, and it is multi user if many users can use the
system and hence access the database concurrently. Multiple
users can access databases and use the computer systems
Simultaneously because of the concept of Multiprogramming,
which allows the computer to execute multiple programs or
processes at the same time. If only a single central
processing unit(CPU) exists, it can actually executes at most
one process at a time. However multiprogramming operating
systems executes some commands from one process then suspend
that process and execute some commands from the next process,
and so on. A process is resumed at the point where it was
suspended whenever it gets its turn to use the CPU again.
Hence concurrent execution of process is actually interleaved
as illustrated in the following figure, which shows two
processes A and B executing concurrently in an interleaved
fashion.
Fig. Interleaved processing Vs. Parallel Processing of
concurrent transactions.
Interleaving also prevents the long process from delaying
other processes. If the computer system has multiple hardware
processors(CPUs), parallel processing of multiple processing
is possible as illustrated the process C and D in the figure.
Transaction Support
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 Can have one of two outcomes:
– Success - transaction commits and database reaches a
new consistent state.
– Failure - transaction aborts, and database must be
restored to consistent state before it started.
– Such a transaction is rolled back or undone.
 Committed transaction cannot be aborted.
 Aborted transaction that is rolled back can be restarted
later.
State Transition Diagram for Transaction
Properties of Transactions
Four basic (ACID) properties of a transaction are:
Atomicity :A transaction is an atomic unit of processing;
it is either performed in its entirely or not
performed at all.
Consistency :Must transform database from one consistent
state to another.
Isolation :Partial effects of incomplete transactions
should not be visible to other transactions.
Durability :Effects of a committed transaction are
permanent and must not be lost because of later
failure.
DBMS Transaction Subsystem
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Concurrency control:
Processes of managing simultaneous operations on the
database without having them interfere with one another.
 Prevents interference when two or more users are
accessing database simultaneously and at least one is
updating data.
 Although two transactions may be correct in themselves,
interleaving of operations may produce an incorrect
result.
Need for Concurrency Control
Three examples of potential problems caused by concurrency:
 Lost update problem.
 The temporary update or Dirty Read Problem.
 Incorrect summary problem.
Fig. 1 : Two sample transaction Transaction 1 and Transaction
2
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The problem occurs when two transactions that access the
same database items have their operations interleaved in a way
that makes the value of the some database items incorrect.
Suppose that transactions T1 and T2 are submitted at
approximately the same time, and suppose that their operations
are interleaved as shown in the figure a, then the final value
of X is incorrect. Because T2 reads the value of X before T1
changes it in the database and hence the updated value
resulting from T1 is lost. For example if X=80 at the start
(originally there were 80 reservation on the flight) N=5 (T1
transfers 5 seats reservations from the flight corresponding
to X to the flight corresponding to Y) and M=4 (T2 reserves 4
seats on X), the final result should be X=79, but the
interleaving of operation shown in the figure a it is X=84
because the update in T1 that removed the 5 seats from X was
lost.
Fig.a. The lost update problem.
The temporary update or Dirty Read Problem.
This problems occurs when one transaction updates a
database item and then the transaction fails for some reason.
The updated item is accessed by another transaction before it
is changed back to its original value.
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Figure shows an example where T1 updates items X then fails
before completion, so the system must change X back to its
original. Before it can do so , however transaction T2 reads
the temporary value of X., which will not be recorded
permanently in the database because of the failure of T1. The
value of item X that is read by T2 is called the dirty data.
Incorrect summary problem
If one transaction is calculating an aggregate summary
function on a number of records while other transactions are
updating some of these records, the aggregate function may
calculate some values before they are updated and others after
they are updated. (refer the Elmasri Navathe’s Fundamentals of
Database Systems for more input)
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FIGURE
Some problems that occur when concurrent execution is
uncontrolled. (c) The incorrect summary problem.
Serializability
 Objective of a concurrency control protocol is to
schedule transactions in such a way as to avoid any
interference.
 Could run transactions serially, but this limits degree
of concurrency or parallelism in system.
 Serializability identifies those executions of
transactions guaranteed to ensure consistency
Schedule
Sequence of reads/writes by set of concurrent
transactions.
Serial Schedule
Schedule where operations of each transaction are
executed consecutively without any interleaved operations from
other transactions.
 No guarantee that results of all serial executions of a
given set of transactions will be identical.
Nonserial Schedule
 Schedule where operations from set of concurrent
transactions are interleaved.
 Objective of serializability is to find nonserial
schedules that allow transactions to execute concurrently
without interfering with one another.
 In other words, want to find nonserial schedules that are
equivalent to some serial schedule. Such a schedule is
called serializable.
Serializability
 In serializability, ordering of read/writes is important:
(a) If two transactions only read a data item, they do not
conflict and order is not important.
(b) If two transactions either read or write completely
separate data items, they do not conflict and order is not
important.
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(c) If one transaction writes a data item and another reads or
writes same data item, order of execution is important.
Example of Conflict Serializability
Serializability
 Conflict serializable schedule orders any conflicting
operations in same way as some serial execution.
 Under constrained write rule (transaction updates data
item based on its old value, which is first read), use
precedence graph to test for serializability.
Precedence Graph
 Create:
– node for each transaction;
– a directed edge Ti  Tj, if Tj reads the value of an
item written by TI;
– a directed edge Ti  Tj, if Tj writes a value into
an item after it has been read by Ti.
 If precedence graph contains cycle schedule is not
conflict serializable.
Example - Non-conflict serializable schedule
 T9 is transferring £100 from one account with balance
balx to another account with balance baly.
 T10 is increasing balance of these two accounts by 10%.
 Precedence graph has a cycle and so is not serializable.
Example - Non-conflict serializable schedule
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View Serializability
 Offers less stringent definition of schedule equivalence
than conflict serializability.
 Two schedules S1 and S2 are view equivalent if:
– For each data item x, if Ti reads initial value of x
in S1, Ti must also read initial value of x in S2.
– For each read on x by Ti in S1, if value read by x
is written by Tj, Ti must also read value of x
produced by Tj in S2.
– For each data item x, if last write on x performed
by Ti in S1, same transaction must perform final
write on x in S2.
 Schedule is view serializable if it is view equivalent to
a serial schedule.
 Every conflict serializable schedule is view
serializable, although converse is not true.
 It can be shown that any view serializable schedule that
is not conflict serializable contains one or more blind
writes.
 In general, testing whether schedule is serializable is
NP-complete.
Example - View Serializable schedule
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Recoverability
 Serializability identifies schedules that maintain
database consistency, assuming no transaction fails.
 Could also examine recoverability of transactions within
schedule.
 If transaction fails, atomicity requires effects of
transaction to be undone.
 Durability states that once transaction commits, its
changes cannot be undone (without running another,
compensating, transaction).
Concurrency Control Techniques
 Two basic concurrency control techniques:
– Locking,
– Timestamping.
 Both are conservative approaches: delay transactions in
case they conflict with other transactions.
 Optimistic methods assume conflict is rare and only check
for conflicts at commit.
Locking
Transaction uses locks to deny access to other transactions
and so prevent incorrect updates.
 Most widely used approach to ensure serializability.
 Generally, a transaction must claim a shared (read) or
exclusive (write) lock on a data item before read or
write.
 Lock prevents another transaction from modifying item or
even reading it, in the case of a write lock.
Locking - Basic Rules
 If transaction has shared lock on item, can read but not
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 If transaction has exclusive lock on item, can both read
and update item.
 Reads cannot conflict, so more than one transaction can
hold shared locks simultaneously on same item.
 Exclusive lock gives transaction exclusive access to that
item.
 Some systems allow transaction to upgrade read lock to an
exclusive lock, or downgrade exclusive lock to a shared
lock.
Example - Incorrect Locking Schedule
 For two transactions above, a valid schedule using these
rules is:
S = {write_lock(T9, balx), read(T9, balx), write(T9, balx),
unlock(T9, balx), write_lock(T10, balx), read(T10, balx),
write(T10, balx), unlock(T10, balx), write_lock(T10, baly),
read(T10, baly), write(T10, baly), unlock(T10, baly),
commit(T10), write_lock(T9, baly), read(T9, baly), write(T9,
baly), unlock(T9, baly), commit(T9) }
Example - Incorrect Locking Schedule
 If at start, balx = 100, baly = 400, result should be:
– balx = 220, baly = 330, if T9 executes before T10,
or
– balx = 210, baly = 340, if T10 executes before T9.
 However, result gives balx = 220 and baly = 340.
 S is not a serializable schedule.
 Problem is that transactions release locks too soon,
resulting in loss of total isolation and atomicity.
 To guarantee serializability, need an additional protocol
concerning the positioning of lock and unlock operations
in every transaction.
Two-Phase Locking (2PL)
Transaction follows 2PL protocol if all locking operations
precede first unlock operation in the transaction.
 Two phases for transaction:
– Growing phase - acquires all locks but cannot
release any locks.
– Shrinking phase - releases locks but cannot acquire
any new locks
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Preventing Lost Update Problem using 2PL
Deadlock
An impasse that may result when two (or more) transactions are
each waiting for locks held by the other to be released.
 Only one way to break deadlock: abort one or more of the
transactions.
 Deadlock should be transparent to user, so DBMS should
restart transaction(s).
 Three general techniques for handling deadlock:
– Timeouts.
– Deadlock prevention.
– Deadlock detection and recovery.
Timeouts
 Transaction that requests lock will only wait for a
system-defined period of time.
 If lock has not been granted within this period, lock
request times out.
 In this case, DBMS assumes transaction may be deadlocked,
even though it may not be, and it aborts and
automatically restarts the transaction.
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Deadlock Prevention
 DBMS looks ahead to see if transaction would cause
deadlock and never allows deadlock to occur.
 Could order transactions using transaction timestamps:
– Wait-Die - only an older transaction can wait for
younger one, otherwise transaction is aborted (dies)
and restarted with same timestamp.
– Wound-Wait - only a younger transaction can wait for
an older one. If older transaction requests lock
held by younger one, younger one is aborted
(wounded).
Recovery from Deadlock Detection
 Several issues:
– choice of deadlock victim;
– how far to roll a transaction back;
– avoiding starvation.
Timestamping
 Transactions ordered globally so that older transactions,
transactions with smaller timestamps, get priority in the
event of conflict.
 Conflict is resolved by rolling back and restarting
transaction.
 No locks so no deadlock.
Timestamp
A unique identifier created by DBMS that indicates
relative starting time of a transaction.
 Can be generated by using system clock at time
transaction started, or by incrementing a logical counter
every time a new transaction starts.
 Read/write proceeds only if last update on that data item
was carried out by an older transaction.
 Otherwise, transaction requesting read/write is restarted
and given a new timestamp.
 Also timestamps for data items:
– read-timestamp - timestamp of last transaction to
read item;
– write-timestamp - timestamp of last transaction to
write item.
Optimistic Techniques
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 Based on assumption that conflict is rare and more
efficient to let transactions proceed without delays to
ensure serializability.
 At commit, check is made to determine whether conflict
has occurred.
 If there is a conflict, transaction must be rolled back
and restarted.
 Potentially allows greater concurrency than traditional
protocols.
 Three phases:
– Read
– Validation
– Write
Optimistic Techniques - Read Phase
 Extends from start until immediately before commit.
 Transaction reads values from database and stores them in
local variables. Updates are applied to a local copy of
the data.
Optimistic Techniques - Validation Phase
 Follows the read phase.
 For read-only transaction, checks that data read are
still current values. If no interference, transaction is
committed, else aborted and restarted.
 For update transaction, checks transaction leaves
database in a consistent state, with serializability
maintained.
Optimistic Techniques - Write Phase
 Follows successful validation phase for update
transactions.
 Updates made to local copy are applied to the database.
Granularity of Data Items
 Size of data items chosen as unit of protection by
concurrency control protocol.
 Ranging from coarse to fine:
– The entire database.
– A file.
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– A page (or area or database spaced).
– A record.
– A field value of a record.
Granularity of Data Items
 Tradeoff:
– coarser, the lower the degree of concurrency;
– finer, more locking information that is needed to be
stored.
 Best item size depends on the types of transactions
Hierarchy of Granularity
 Could represent granularity of locks in a hierarchical
structure.
 Root node represents entire database, level 1s represent
files, etc.
 When node is locked, all its descendants are also locked.
 DBMS should check hierarchical path before granting lock.
 Intention lock could be used to lock all ancestors of a
locked node.
 Intention locks can be read or write. Applied top-down,
released bottom-up.
Database Recovery
Process of restoring database to a correct state in the event
of a failure.
 Need for Recovery Control
– Two types of storage: volatile (main memory) and
nonvolatile.
– Volatile storage does not survive system crashes.
– Stable storage represents information that has been
replicated in several nonvolatile storage media with
independent failure modes.
Types of Failures
 System crashes, resulting in loss of main memory.
 Media failures, resulting in loss of parts of secondary
storage.
 Application software errors.
 Natural physical disasters.
 Carelessness or unintentional destruction of data or
facilities.
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Transactions and Recovery
 Transactions represent basic unit of recovery.
 Recovery manager responsible for atomicity and
durability.
 If failure occurs between commit and database buffers
being flushed to secondary storage then, to ensure
durability, recovery manager has to redo (rollforward)
transaction’s updates.
 If transaction had not committed at failure time,
recovery manager has to undo (rollback) any effects of
that transaction for atomicity.
 Partial undo - only one transaction has to be undone.
 Global undo - all transactions have to be undone.
Example
 DBMS starts at time t0, but fails at time tf. Assume data
for transactions T2 and T3 have been written to secondary
storage.
 T1 and T6 have to be undone. In absence of any other
information, recovery manager has to redo T2, T3, T4, and
T5.
Recovery Facilities
 DBMS should provide following facilities to assist with
recovery:
– Backup mechanism, which makes periodic backup copies
of database.
– Logging facilities, which keep track of current
state of transactions and database changes.
– Checkpoint facility, which enables updates to
database in progress to be made permanent.
– Recovery manager, which allows DBMS to restore
database to consistent state following a failure.
Log File
 Contains information about all updates to database:
– Transaction records.
– Checkpoint records.
 Often used for other purposes (for example, auditing).
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 Transaction records contain:
– Transaction identifier.
– Type of log record, (transaction start, insert,
update, delete, abort, commit).
– Identifier of data item affected by database action
(insert, delete, and update operations).
– Before-image of data item.
– After-image of data item.
– Log management information.
Sample Log File
 Log file may be duplexed or triplexed.
 Log file sometimes split into two separate random-access
files.
 Potential bottleneck; critical in determining overall
performance.

Checkpoint
Point of synchronization between database and log file.
All buffers are force-written to secondary storage.
 Checkpoint record is created containing identifiers of
all active transactions.
 When failure occurs, redo all transactions that committed
since the checkpoint and undo all transactions active at
time of crash.
 In previous example, with checkpoint at time tc, changes
made by T2 and T3 have been written to secondary storage.
 Thus:
 only redo T4 and T5,
 undo transactions T1 and T6.pdfMachine - is a pdf writer that produces quality PDF files with ease!
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Recovery Techniques
 If database has been damaged:
– Need to restore last backup copy of database and
reapply updates of committed transactions using log
file.
 If database is only inconsistent:
– Need to undo changes that caused inconsistency. May
also need to redo some transactions to ensure updates
reach secondary storage.
– Do not need backup, but can restore database using
before- and after-images in the log file.
Main Recovery Techniques
 Three main recovery techniques:
– Deferred Update
– Immediate Update
– Shadow Paging
Deferred Update
 Updates are not written to the database until after a
transaction has reached its commit point.
 If transaction fails before commit, it will not have
modified database and so no undoing of changes required.
 May be necessary to redo updates of committed
transactions as their effect may not have reached
database.
Immediate Update
 Updates are applied to database as they occur.
 Need to redo updates of committed transactions following
a failure.
 May need to undo effects of transactions that had not
committed at time of failure.
 Essential that log records are written before write to
database. Write-ahead log protocol.
 If no “transaction commit” record in log, then that
transaction was active at failure and must be undone.
 Undo operations are performed in reverse order in which
they were written to log.
Shadow Paging
 Maintain two page tables during life of a transaction:
current page and shadow page table.
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 When transaction starts, two pages are the same.
 Shadow page table is never changed thereafter and is used
to restore database in event of failure.
 During transaction, current page table records all
updates to database.
 When transaction completes, current page table becomes
shadow page table.
Advanced Transaction Models
 Protocols considered so far are suitable for types of
transactions that arise in traditional business
applications, characterized by:
– Data has many types, each with small number of
instances.
– Designs may be very large.
– Design is not static but evolves through time.
– Updates are far-reaching.
– Cooperative engineering.
 May result in transactions of long duration, giving rise
to following problems:
– More susceptible to failure - need to minimize
amount of work lost.
– May access large number of data items - concurrency
limited if data inaccessible for long periods.
– Deadlock more likely.
– Cooperation through use of shared data items
restricted by traditional concurrency protocols.
Chapter 10
Normalization
 Main objective in developing a logical data model for
relational database systems is to create an accurate
representation of the data, its relationships, and
constraints.
 To achieve this objective, must identify a suitable set
of relations.
 Four most commonly used normal forms are first (1NF),
second (2NF) and third (3NF) normal forms, and Boyce–Codd
normal form (BCNF).
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 Based on functional dependencies among the attributes of
a relation.
 A relation can be normalized to a specific form to
prevent possible occurrence of update anomalies.
Data Redundancy
 Major aim of relational database design is to group
attributes into relations to minimize data redundancy and
reduce file storage space required by base relations.
 Problems associated with data redundancy are illustrated
by comparing the following Staff and Branch relations
with the StaffBranch relation.
Data Redundancy
 StaffBranch relation has redundant data: details of a
branch are repeated for every member of staff.
 In contrast, branch information appears only once for
each branch in Branch relation and only branchNo is
repeated in Staff relation, to represent where each
member of staff works.
Update Anomalies
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 Relations that contain redundant information may
potentially suffer from update anomalies.
 Types of update anomalies include:
– Insertion,
– Deletion,
– Modification.
Lossless-join and Dependency Preservation Properties
 Two important properties of decomposition:
- Lossless-join property enables us to find any instance
of original relation from corresponding instances in the
smaller relations.
- Dependency preservation property enables us to enforce
a constraint on original relation by enforcing some constraint
on each of the smaller relations.
Functional Dependency
 Main concept associated with normalization.
 Functional Dependency
– Describes relationship between attributes in a
relation.
– If A and B are attributes of relation R, B is
functionally dependent on A (denoted A  B), if each
value of A in R is associated with exactly one value
of B in R.
 Property of the meaning (or semantics) of the attributes
in a relation.
 Diagrammatic representation:
 Determinant of a functional dependency refers to
attribute or group of attributes on left-hand side of the
arrow.
Example - Functional Dependency
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 Main characteristics of functional dependencies used in
normalization:
– have a 1:1 relationship between attribute(s) on left
and right-hand side of a dependency;
– hold for all time;
– are nontrivial.
 Complete set of functional dependencies for a given
relation can be very large.
 Important to find an approach that can reduce set to a
manageable size.
 Need to identify set of functional dependencies (X) for a
relation that is smaller than complete set of functional
dependencies (Y) for that relation and has property that
every functional dependency in Y is implied by functional
dependencies in X.
 Set of all functional dependencies implied by a given set
of functional dependencies X called closure of X (written
X+).
 Set of inference rules, called Armstrong’s axioms,
specifies how new functional dependencies can be inferred
from given ones.
 Let A, B, and C be subsets of the attributes of relation
R. Armstrong’s axioms are as follows:
1. Reflexivity If B is a subset of A, then A  B
2. Augmentation If A  B, then A,C  B,C
3. Transitivity If A  B and B  C, then A  C
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The Process of Normalization
 Formal technique for analyzing a relation based on its
primary key and functional dependencies between its
attributes.
 Often executed as a series of steps. Each step
corresponds to a specific normal form, which has known
properties.
 As normalization proceeds, relations become progressively
more restricted (stronger) in format and also less
vulnerable to update anomalies.
Relationship Between Normal Forms
Unnormalized Form (UNF)
 A table that contains one or more repeating groups.
 To create an unnormalized table:
– transform data from information source (e.g. form)
into table format with columns and rows.
First Normal Form (1NF)
 A relation in which intersection of each row and column
contains one and only one value.
UNF to 1NF
 Nominate an attribute or group of attributes to act as
the key for the unnormalized table.
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 Identify repeating group(s) in unnormalized table which
repeats for the key attribute(s).
 Remove repeating group by:
– entering appropriate data into the empty columns of
rows containing repeating data (‘flattening’ the
table).
Or by
– placing repeating data along with copy of the
original key attribute(s) into a separate relation.
Second Normal Form (2NF)
 Based on concept of full functional dependency:
– A and B are attributes of a relation,
– B is fully dependent on A if B is functionally
dependent on A but not on any proper subset of A.
 2NF - A relation that is in 1NF and every non-primary-key
attribute is fully functionally dependent on the primary
key.
1NF to 2NF
 Identify primary key for the 1NF relation.
 Identify functional dependencies in the relation.
 If partial dependencies exist on the primary key remove
them by placing them in a new relation along with copy of
their determinant
Third Normal Form (3NF)
 Based on concept of transitive dependency:
– A, B and C are attributes of a relation such that if
A  B and B  C,
– then C is transitively dependent on A through B.
(Provided that A is not functionally dependent on B
or C).
 3NF - A relation that is in 1NF and 2NF and in which no
non-primary-key attribute is transitively dependent on
the primary key.
2NF to 3NF
 Identify the primary key in the 2NF relation.
 Identify functional dependencies in the relation.
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 If transitive dependencies exist on the primary key
remove them by placing them in a new relation along with
copy of their determinant.
General Definitions of 2NF and 3NF
 Second normal form (2NF)
– A relation that is in 1NF and every non-primary-key
attribute is fully functionally dependent on any
candidate key.
 Third normal form (3NF)
– A relation that is in 1NF and 2NF and in which no
non-primary-key attribute is transitively dependent
on any candidate key.
Boyce–Codd Normal Form (BCNF)
 Based on functional dependencies that take into account
all candidate keys in a relation, however BCNF also has
additional constraints compared with general definition
of 3NF.
 BCNF - A relation is in BCNF if and only if every
determinant is a candidate key.
 Difference between 3NF and BCNF is that for a functional
dependency A  B, 3NF allows this dependency in a
relation if B is a primary-key attribute and A is not a
candidate key.
 Whereas, BCNF insists that for this dependency to remain
in a relation, A must be a candidate key.
 Every relation in BCNF is also in 3NF. However, relation
in 3NF may not be in BCNF.
 Violation of BCNF is quite rare.
 Potential to violate BCNF may occur in a relation that:
– contains two (or more) composite candidate keys;
– the candidate keys overlap (i.e. have at least one
attribute in common).
Review of Normalization (UNF to BCNF)
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Fourth Normal Form (4NF)
 Although BCNF removes anomalies due to functional
dependencies, another type of dependency called a multi-
valued dependency (MVD) can also cause data redundancy.
 Possible existence of MVDs in a relation is due to 1NF
and can result in data redundancy.
 Dependency between attributes (for example, A, B, and C)
in a relation, such that for each value of A there is a
set of values for B and a set of values for C. However,
set of values for B and C are independent of each other.
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 MVD between attributes A, B, and C in a relation using
the following notation:
A > B
A > C
 MVD can be further defined as being trivial or
nontrivial.
– MVD A ¾¾ØØ B in relation R is defined as being
trivial if (a) B is a subset of A or (b) A  B = R.
– MVD is defined as being nontrivial if neither (a) nor (b)
are satisfied.
– Trivial MVD does not specify a constraint on a relation,
while a nontrivial MVD does specify a constraint.
 Defined as a relation that is in BCNF and contains no
nontrivial MVDs.
4NF - Example
Fifth Normal Form (5NF)
 A relation decomposed into two relations must have
lossless-join property, which ensures that no spurious
tuples are generated when relations are reunited through
a natural join.
 However, there are requirements to decompose a relation
into more than two relations.
 Although rare, these cases are managed by join dependency
and fifth normal form (5NF).
 A relation that has no join dependency.
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5NF - Example
More Notes:
Inference Rules for FD
 Reflexive Rule :
if X ≤ Y, then XY
 Augmentation Rule :
{XY} = XZ
 Transitive Rule:
{XY, YZ} = XZ
 Decomposition or Projective Rule
{XYZ } = XY
 Union or additive Rule :
{XY , X Z} = XYZ
 Pseudo Transitive Rule:
{XY, WYZ} = WXZ
Minimal sets of FD
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 Algorithm: Finding minimal cover F for a set of
Functional Dependencies E
1. set F:=E
2. Replace each FD X {A1,A2,..An} in F by the n FD
XA1, XA2……XAn
3. For each FD XA in F
for each attribute B that is an element of X
if {{F-{XA}} Ù {(X- {B}A}} is equivalent to
F then
replace XA with (X-{B})A in F
4. for each FD X A in F
if {F-{XA }} is equivalent to F then remove XA
from F
 First Normal Form:
 A relation is said to be in First NF if it is
already in un normalized from and it has no
repetitive group
 Second NF
 A relation is said to be in second NF if it is
already in the First NF and it has no partial
dependency
 Third NF
 A relation is said to be in Third NF if it is
already in second NF and it has no transitive
dependency
 BCNF
 A relation is said to be in Boyce Codd NF if it
already in the third NF and every determinant is a
candidate key. It is a stronger version of the third
NF.
 Fourth NF
 A relation is said to be in Fourth NF if it is
already in BCNF and it has no multi valued
dependency
 Multi Valued Dependency
 Consider three fields X,Y and Z in a relation.
If for each value of X, there is a well defined
set of values Y and well defined set of values
Z and the set of values of Y is independent of
the set of values of Z, then multi valued
dependency exists.
 Fifth NF
 A relation is said to be in Fifth NF if it is
already in fourth NF and it has no join dependency
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Note: This chapter only for Bsc.IT
Introduction to PL/SQL
PL/SQL stands for procedural language extensions to SQL. It is available as an
enabling technology within other Software Products; It does not exists as a stand –
alone language with PL/SQL, we can use SQL statements to manipulate oracle data and
flow of control statements to process the data. Also we can declare constants and
variables defined in subprograms and trap runtime errors. Thus PL/SQL combines the
data manipulating power of SQL with the data processing power of procedural
languages.
Why Use Pl/Sql?
PL/SQL is a complete transaction processing language that provides the following
advantages:
1. It acts as a support for SQL. Since it supports SQL data manipulation statement,
SQL transaction processing statement, SQL functions and SQL predicates we can
access the ORACLE database and manipulate its data flexibly and easily.
2. PL/SQL allows to declare variables and constants and use them in SQL and
procedural statements, any where an expression can be used. PL/SQL variables and
constants have attributes (%Type, %Rowtype) by which we can reference the data
type and structure of an object with out repeating its definition.
3. Control structures are the most important PL/SQL extension of SQL. It allows you
process the data using the conditional control statement(IF-THEN-ELSE), interative
control statement (LOOP END LOOP, FOR-LOOP and WHILE-LOOP) and
sequential control statement (GOTO), with the help of these statements we can
handle any situation.
4. A PL/SQL cursor gives you a way to fetch and process database information. There
are two types of cursors SXPLICIT and IMPLICIT CURSORS. PL/SQL declares
cursor implicitly for all SQL manipulation statements including queries that returns
one row. FOR queries that returns more than one row an explicit cursor has to be
declared.
5. Modularity is promoted because PL/SQL lets you break an application down into
manageable, well-defined logic modules.
6. Errors are easily detected and handled.
What Commands Can Be Executed From Sql*Plus?
You can enter three kinds of commands from the SQL*Plus command prompt:
SQL*Plus commands – for formatting query results, setting options, and editing and
storing SQL commands and PL/SQL blocks. Eg.
SHOW USER
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SQL commands – like select, create, alter, drop etc. Eg:
SELECT*FROM TAB;
PL/SQL blocks – a set of line that do a particular task. Eg:
BEGIN
DBMS_OUTPUT.PUT_LINE(‘Hello World”);
END;
/
SQL*Plus Basics
Oracle’s SQL*Plus is a command line tool that allows a user to type SQL statements to
be executed directly against an Oracle database. SQL*Plus has the ability to format
database output, save often used commands and can be invoked from other Oracle tools
or from the operating system prompt.
Pl/Sql Block Structure
PL/SQL is a block structured language. A block lets you group logically related
declarations and statements. The declarations are local to the block and cease to exist
when the block completes. A block has three parts: a declarative part, and executable
part, and an exception handling part (error handling).
DECLARE
<declaration>
BEGIN
<executable statements>
EXCEPTION
<exception handlers>
END;
The declarative part comes first in which the objects can be declared. Once declared
the objects can be manipulated in the executable part. Exceptions raised during
execution can be handled in the exception-handling part. We can nest sub-blocks in the
executable, exception part and not in declarative part. You can define subprograms
(Functions, Procedures) in the declarative part of any block. But it can be called with in
the block only. See example:
DECLARE
<declaration>
BEGIN
SUB-BLOCK1
DECLARE
<declarations>
BEGIN
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EXCEPTION
<Exception handlers>
END;
SUB-BLOCK2
DECLARE
<declarations>
BEGIN
<executable statements>
<EXCEPTION>
<exception handlers>
END;
END;
Referring to the following sample PL/SQL block we have the following example.
DECLARE
Num_in_stock NUMBER(5);
BEGIN
SELECT quantity INTO num-in-stock FROM inventory_table
WHERE product = ‘TENNIS RACQUET’;
IF num_in_stock>0 THEN
UPDATE INVENTORY_TABLE SET quantity = quantity – 1
WHERE product = ‘THNNIS RECQUET’;
INSERT INTO purchase_record
VALUES (‘TENNIS RACQUET PURCHASED’., SYSDATA);
ELSE
INSERT INTO purchase_record
VALUES(‘OUT OF TENNIS RACQUETS.’,SYSDATA);
END IF;
COMMIT;
END;
Advantages of PL/SQL
PL/SQL is a completely portable high performance transaction processing language
which provides the following advantages:
Support for Sql:-
PL/SQL lets us to use all SQL data manipulation, cursor control and transaction
control commands as well as SQL functions, operators, and pseudocolumns.
Higher Productivity:-
PL/SQL adds functionality to non-procedural tools such as SQL*Forms, SQL*
Reportwriter SQL*Menu. With PL/SQL in these tools you can use familiar proceduralpdfMachine - is a pdf writer that produces quality PDF files with ease!
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119
construct to build applications. Thus it increases productivity by putting better tools in
your hands.
Better Performance:-
With our PL/SQL Oracle must process SQL statements one at a time. It increases
network traffic. But with PL/SQL an entire block of SQL statements (more than one)
can be sent to Oracle at one time. Also with the procedural capabilities in enhances
performance.
Portability:-
Applications written in PL/SQL are portable to any operating system and platform in
which Oracle runs.
Character Set
A PL/SQL program consists of sequence of statements, each of which is made up of one
or more lines of text. Text is made up of combinations of the characters shown below:
 The numerals 0-9
 Tab, space, carriage return
 The symbols ( ) + - * / <> =; :@ %,” # $ ^ & _ ! { } [ ]
 The upper and lower-case letters A…..Z, a…..z.
PL/SQL is not case sensitive, so lower-case letters are equivalent to the corresponding
upper-case letters except within string (when surrounded by single quotes) or represent
the value of a character variable.
Delimiters:-
A delimiters is a simple or compound symbol that has a special meaning to PL/SQL. A
few examples are given below.
; statement terminator
% Attribute indicator (cursor attributes like % is open and indirect declaration
attributes like % Rowtype
- Single underscore: (single byte wildcard symbol as in SQL.
: Host variable indicator
<<and>>Label delimiters
:= Alignment operator
__ Single line comment
/* and */ Beginning and ending multilane comment block delimiters.
Identifiers:-
An indentifier is a name for a PL/SQL object which includes constants, variables,
exceptions, cursors, subprograms, packages, and reserved words. The properties of
identifiers are:
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1. Upto 30 characters in length
2. Must start with a letter
3. Can include $ sign, underscore, and #(pound sign)
4. Cannot contain spaces.
Binary Integer:-
It allows to stroe signed integers (-2^31 –1 through 2 ^31- Natural and positive are both
subtypes of Binary-integer
Natural allows 0 through 2 ^31
Positive allows 1 through 2 ^31
Number
Use the number data type to store fixed or floating point numbers of any size. The
maximum precision of a variable with Number type is 38 digits. While declaring we can
optionally specify the valuable precision as follows:
Number (precision, Scale)
The precision of a Number is the total number of digits. The scale dictates the number
of digits to the right or left of the decimal point at which rounding occurs. The
remaining data types in number are all sub types of number. They have the same range
of values as their base type. The remaining data types in number are all sub types of
number. They have the same range of values as their base type.
Subtype Datatype
Dec(prec.scale) Number(prec, Scale)
Decimal(prec.scale) Number(prec.scale)
Double Precision Number
Float (Binary) Number
Int Number(38)
Integer Number(38)
Numeric (Prec, Scales) Number(prec, Scale)
Real Number
Smallint Number(38)
Char
Variables with character data types store text and are manipulated by character
functions. The CHAR data type takes an optional parameter that lets you specify a
maximum length upto 32767 bytes. The syntax is:-
CHAR (maximum_length)
Char Subtypes
The character subtypes have the same rang of values as their base type.
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Character
Varchar 2
It has the same syntax as Char. But the difference is, if we assign a character value to a
Char variable and if the length is shorter than the declared length of the variable
PL/SQL blankpads the value to the declared length. The VARCHAR is a subset of
VARCHAR2.
If we assign a character value to a Varchar2 variable, and if the value is shorter than
the declared length of the variable, PL/SQL neither blankpads the value nor strips
trailing blanks.
Name1 Char (10);
Name2 Varchar2(10);
If we give name1 and name2 as Frank, name 1 will be stored internally as ‘Frank’ and
name2 as ‘Frank’. So even though the names are same since the data types are different
they are not same.
Long:-
The variable LONG can store variable-length strings of up to 32760 bytes which is
seven fewer bytes longer than allowed in Varchar2 type variables.
Date:-
The Date data type is to store fixed length data values. It also takes no parameters.
Valid dates include from Jan 1,4712 BC to December 31,4712 AD. When stored in a
database column, date values include to the first day of the current month; the time
portion defaults to midnight.
Declare Variable:-
Variable are declared in the DECLARE section of the PL/SQL block. Declaration
involves the name of the variable followed by its data type. All statements must end
with a semicolon. Initial values can also be assigned at the time of declaration.
Constants are declared by specifying the key work CONSTANT before the data type.
The syntax is:-
Indentifier [Constant] Data Type [Not Null] [:=Pl/Sql Expression];
Example:
Declare Number
Count Number (9,2)
Secs_Per_Day Constant Number:= 60*60*24
First_Name Char
Last Name Varchar2
Birth_Day Date
Available Boolean := Null;
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……………..
……………..
Comparisions
PL/SQL supports the comparison of variables and constants in SQL and PL/SQL
statements. These comparisons called Boolean expressions, generally consists of simple
expressions separated by Relational operators (=,!=,<,>, >=, <=). Boolean expressions
are often connected by the logical operators AND, OR, NOT. In PL/SQL a Boolean
expression always evaluates to TRUE, FALSE OR NULL. In a SQL statement Boolean
expression allow you to control which rows in a table are affected by the statement. In a
non SQL statement, Boolean expressions are the basis for conditional control.
There are three kinds of Boolean expression Numeric, Character, Date.
Boolean Expression:-
A character string is a sequence of characters stung together. Like numeric
expressions, character string can be compared. The comparison is based on the
alphabetic ordering.
ename>empname
ename != ‘frank’
Operator Meaning
= Is the Same As
!= Is Not The Same As
< Comes Alphabetically before
> Comes Alphabetically After
<= Comes Alphabetical Before
Declaring Variables and Constants:-
PL/SQL supports a variety of data types that you can use for declaring variables and
constants. You can assign values to variables as you declare them and you can change
the value of a variable through further assignments.
You must assign the value of a constant to it when you declare it, this value is fixed and
cannot be changed at run time.
Example variable declarations are given below:-
DECLARE
V_NUM1 NUMBER NOT NULL:=10109;
NUM8 NUMBER(3,1);
XYZ NUMBER(2,2):=31.8;
ABC12 NUMBER(9,2):=XYZ*131;
V_CHR1 CHAR(89);
V_CHR2 VARCHAR2(12):= “JANUARY”;
TODAY DATE:= SYSDATE;
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Example constant declarations are given below:-
DECLARE
PI CONSTANT NUMBER(9,3) : 3.142;
VAT CONSTANT NUMBER(4,2):=17.5;
When you declare PL/SQL variables to hold column values you must ensure that the
variable type is the same as the column type else you will get a run time error at
execution. You can use the %TYPE attribute to base a variable upon the column
definition as defined within the Oracle data dictionary. The attribute is prefixed with
the schema, table and column name and used where the datatype is required in the
DECLARE SECTION. PL/SQL determines the datatype and size of the variable when
the block is complied and so is always compatible with the column used to populate it.
An example follows:-
DECLARE
V_NUM1 JD11.BOOK.COST%TYPE;
PL/SQL Editor:-
PL/SQL Editor is nothing but where you type all the pl/sql programs. In the following
sections we see how to write different types of programs using the standard structures
available in PL/SQL.
Pl/SQL Structures
Using SQL statements, we can retrieve or manipulate data present in a table. Using
SQL statements above it is not possible to gain the power of procedural language
constructs. This aspect has been taken care of by PL/SQL, which is of a procedural
extension of SQL.
A PL/SQL block can contain DML & DCL statements, but not DDL statements – but
not DDL statements. A PL/SQL block can also contain any number of SQL statements
integrated with flow of control statements. Using PL/SQL we can also trap runtime
errors.
What are Pl/Sql structures?
With PL/SQL, you can use SQL statements to manipulate Oracle data and flow control
statements to process data to meet all your requirements. PL/SQL combines the data
manipulating power of SQL with the data-processing power of procedural languages.
PL/SQL provides many different structures to control the flow of your statement
execution and data handling.
Why use Pl/Sql Structures?
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Control structures are the most important PL/SQL extensions to SQL. Not only does
PL/SQL let you manipulate data, it lets you process the data using conditional, iterative,
sequential, and unconditional flow-control statements such as if-then-else, for loop,
while-loop, exit-when and goto.
Control Structures:-
There are three types of control statements. Conditional control, Iterative control and
Sequential control. The conditional control returns a Boolean value. The iterative
control executes a sequence of statements repeatedly. The sequential statements execute
a sequence of statements in the order in which they occur.
Conditional Control:-
The IF statement lets you execute a series of statements conditionally. There are three
forms IF-THEN, IF-THEN-ELSE, and IF-THEN-ELSIF.
If-then
IF associates a condition with a sequence of statements enclosed by the keywords THEN
and END IF. The statements are executed only when the condition is TRUE.
IF condition THEN
Sequence of statements;
END IF;
Example:
IF Sales>Quata THEN
Combute_bonus(empid);
UPDATE payrole SET pay + bonus WHERE EMPNO = EMP_ID;
END IF;
If-then-else
The second form of IF statements adds the keywords ELSE followed by alternative
sequence of statements. The sequence of statements in the ELSE clause will be executed
only when the condition evaluates to FALSE.
IF condition THEN
Sequence of statement
ELSE
Sequence of statements
END IF;
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Example:-
IF trans_type = ‘CR’ THEN
UPDATE accounts SET balance = balance + credit WHERE…..
ELSE
UPDATE accounts SET balance = balance – debit WHERE
END IF;
The third form introduces the keyword ELSIF to introduce additional conditions. If
any condition evaluates to TRUE then that sequence of statements get executed. We
can have any number of ELSIF clause but the last one is optional.
If conditiona 1 THEN
ELSIF
ELSIF
END IF;
An example of if-then-else structure is as follows.
SQL>declare
2 length number(3):=10;
3 breadth number(3):=12;
4 area number(5);
5 begin
6 if length>=0 then
7 if breadth <=0 then
8 dbms_output.put_line(‘Breadth cant be less than 0’);
9 else
10 area:=.5 * length * breadth;
11 dbms_output.put_line(‘Breadth cant be less than 0’);
12 end if;
13 else
14 dbms_output.put_line(‘Length cant be less than 0’);
15 end if;
16 end;
17/
Area is 60
PL/SQL procedure successfully completed.
Iterative Control
Loop statements lets you use the iterative type of control. There are three forms of
LOOP statements. LOOP, WHILE-LOOP and FOR-LOOP.
Loop
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The simplest form is the infinite LOOP, WHICH encloses sequence of statements
between the key words LOOP and END LOOP.
LOOP
END LOOP;
Exit
The EXIT statement is used to complete the loop. You can place one or more EXIT
statement inside a LOOP. The EXIT-WHEN statement allows a loop to complete
conditionally when the condition in the WHEN clause is evaluated.
Loop
If..then
Exit; - Exit Loop
End If;
End Loop;
Loop
Fetch C1 Into …
Exit When C1% Notfound…
End Loop;
Example
SQL>declare
2 counter binary_integer:=1;
3 begin
4 loop
5 dbms_output.put_line(‘Counter values is’ || counter);
6 demb_output.put_line( “);
7 counter:=counter+1;
8 exit when counter>5;
9 end loop;
10 end;
/
Counter values is 1
Counter values is 2
Counter values is 2
Counter values is 3
Counter values is 5
Loop Labels
Like PL/SQL blocks LOOPS can be labeled. The label, an undeclared identifier enclsed
by double angle brackets, must appear at the beginning of the LOOP statement as
follows:
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Loop
……….
End Loop Label_Name;
<<Outer Block>>
While-loop
The WHILE-LOOP statement associates a condition with a sequence of statement
enclosed by the keywords LOOP and END LOOP. Before each iteration of the LOOP,
the condition is evaluated. If the condition evaluates to TRUE the sequence of
statements are evaluated else the loop is by passed.
While Condition Loop
Sequence Of Statements:
Endloop;
Consider the following example. This block uses a simple WHILE loop to insert 5 rows
into a table. The values of a counter variable, and either of two character strings are
inserted. Which string is inserted depends on the value of the loop index.
<SQL> create table TEMP
2 (coll number(9,4));
Table created.
<SQL>DECLARE
2 x NUMBER: = 100;
3 BEGIN
4 WHILE x<501 LOOP
5 INSERT INTO TEMP VALUES (X);
6 x:= x+100;
7 END LOOP;
8 COMMIT;
9 END;
10 /
For-loop
The number of iterations through a WHILE-LOOP is unknown. The number of
iterates through a FOR-LOOP is known before it enters the LOOP, FOR-LOOP
iterates over a specified range of integers. The syntax is as follows:
For example consider the following example which will check the value I is even or odd
and inserts the data accordingly.
SQL>create table TEMP
2 (col1 number(9,4),
3 (col2 number(4),
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Table created
SQL>DECLARE
2 x NUMBER:=100;
3 BEGIN
4 FOR I IN 1..5 LOOP
5 IF MOD(I,2) =0 THEN – I is even
6 INSERT INTO temp VALUES(i,x, ‘i(even)’;
7 ELSE
8 INSERT INTO temp VALUES(i,x, ‘i(odd)’;
9 END IF;
10 x:=x+100;
11 END LOOP;
12 COMMIT;
13 END;
14/
Loop
Insert Into Temp (Coll.message) Values (Count, ‘Ever Green’):
Insert Ten Times Is Ever Green Into Temp End Loop.
Sequential Control
GOTO and NULL statements are the sequential control statements.
Goto
The GOTO statement branches to a label unconditionally. The label must be unique
within its scope and must precede an executable statement or a PL/SQL block. When
executed, the GOTO statement transfers control to the labeled statement or block. The
syntax is below:-
BEGIN
……
GOTO test_line;
…...
<<test line>>
……
END;
 A GOTO can’t branch into an IF statement, LOOP statement or sub block.
 Also it can’t branch from one IF statement clause to another.
 A GOTO cant branch out of a subprogram.
 A GOTO statement cant branch from an exception handler into the current
block.
 Use of GOTO is a complex unstructured coding.pdfMachine - is a pdf writer that produces quality PDF files with ease!
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NULL STATEMENT
The NULL statement specified inaction, it does nothing other than pass control to the
next statement. It can, however, improve readability. In a construct allowing
alternative actions, the NULL statement serves as a place holder.
If
Score>100 Then
Compute_Bonus(Batsma_Id);
Else
Null;
End If:
Cursors
A cursor is a variable that runs through the tuples of some relation. This relation can
be a stored table, or it can be the answer to some query. By fetching into the cursor
each tuple of the relation, we can write a program to read and process the value of each
such tuple. If the relation is stored, we can also update or delete the tuple at the current
cursor position.
To process a SQL statement, PL/SQL opens work are called private SQL area. PL/SQL
allows user to name the private work areas and access the stored information. The
PL/SQL construct to identify each and every work area used is called CURSOR. There
are two types of cursors namely IMPLICIT and EXPLICIT.
Explicit Cursor
The set of rows returned by a query can consist of Zero, one or many rows, depending
upon the number of rows that meet the query’s search condition. When a query returns
multiple rows, a cursor can be explicitly defined to:
1) process beyond the first row returned by the query.
2) Keep track of which row is being processed.
There are four steps to DECLARE and USE A CURSOR:
DECLARE the cursor
Open the cursor
FETCH data from the cursor
CLOSE the cursor
Declare Cursor
Declare the cursor to associate its name with a select statement. Forward references are
not allowed in PL/SQL. So we must declare cursor before referencing in other
statements.
DECLARE
CURSOR<cursor_name>ISpdfMachine - is a pdf writer that produces quality PDF files with ease!
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< select statement>:
Example:
CURSOR MY_FIRST IS SELECT ENAME FROM EMP
WHERE SAL>7000;
 The select statement must not include the INTO clause
 Values can be assigned to cursor name
 Declared cursors are scoped just as variables
Open Cursor
Opening the cursor executes the query and identifies the active set which consists of all
rows that meet the query search criteria.
OPEN<cursor_name>;
Fetch Cursor
The fetch statement retrieves the rows in the active set one at a time. Each time the
fetch is executed the cursor and advances to the next row in the active set.
FETCH<cursor_name>INTO<var, var2..>;
For each column value returned by the query associated with the cursor, there must be
a corresponding variable in the INTO list. Their datatypes must also be compatible.
Close Cursor
Close the cursor to free up the resources. The close statement disables the cursor and
the active set becomes undefined. No more rows can be fetched from a closed cursor.
CLOSE<cursor_name>;
Example
SQL>DECLARE CURSOR c1 IS
2 SELECT ename, sal FROM emp;
3 name emp.ename%type;
4 salary emp.sal%type;
5 BEGIN
6 OPEN
7 for I in 1..5
8 LOOP
9 FETCH c1 INTO name, salary;
10 dbms_output.put_line(‘Name is’ || name || ‘Salary is’|| salary);
11 end loop;
12 close c1;
13 end;
14/
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Name is ALLEN Salary is 1600
Name is WARD Salary is 1250
Name is JONES Salary is 2975
Name is SMITH Salary is 1250
1) DECLARE
/* Output variables to hold the result of the query:*/
2) a T1.e%TYPE;
3) b T1.f%TYPE;
/*cursor declaration:*/
4) CURSOR T1Cursor IS
5) SELECT e,f
6) FROM T1
7) WHERE e<f
8) FOR UPDATE;
9) BEGIN
10 OPEN T1Cursor;
11 LOOP
/* retrieve each row of the result of the above query
into PL/SQL variables:*/
12) FETCH T1Cursor INTO a,b;
/* If there are no more rows to fetch, exit the loop:*/
13) EXIT WHEN T1 CURSOR%NOTFOUNT;
14) /DELETE FROM T1 WHERE CURRENT OF T1Cursor;
/* Insert the reverse tuple:*/
15) INSERT INTO T1 VALUES (b,a);
16) END LOOP;
/*Free cursor used by the query.*/
17) CLOSE T1 Cursor;
18) END;
19) .
20) run;
Subprograms
The PL/SQL programs can be stored in the database as stored programs and can be
invoked whenever required. This avoids repassing when multiple users invoke it. This
also provides security and integrity control by allowing access on the sub program and
not on the database objects directly. Actually subprograms are named PL/SQL blocks
that can take parameters and be invoked.
What Is A Subprogram?
Subprograms are PL/SQL blocks that can take parameter and can be invoked.
PL/SQL has two types of subprograms PROCEDURES and FUNCTIONS. A
procedure is used to perform an action and a function to compute a value.pdfMachine - is a pdf writer that produces quality PDF files with ease!
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Why Use Subprograms?
 Provides extensibility
 Provides modularity
 Promotes reusability and manageability
Using Subprograms
Subprograms have a declarative part, an executable part and an optional exception
handling part. The declarative part contains declarations of types, cursors, constants,
variables, exceptions, and nested subprograms. These objects are local and cease to
exist when exited from the subprogram. The executable part contains statements that
assign values, control execution, and manipulate ORACLE data. The exception
handling part contains exception handlers, which deal with exceptions raised during
exception.
A procedure is a subprogram that performs a specific action. The syntax is:
PROCEDURE name [parameter [,parameter])] IS local declaration
BEGIN
Executable statements
[EXCEPTIONS]
END [name];
Where parameter stands for
Var_name [IN/OUT/IN OUT] datatype
[{:=!DEFAULT} value]
A procedure has two parts the SPECIFICATION and the BODY. A specification
begins with the keyword PROCEDURE and ends with the procedure name or
parameter list. A body begins with the keywords IS and ends with the keyboard END.
A body has three parts a DECLARATIVE PART, an EXECUTABLE part, and an
optional EXCEPTION handling part.
PL/SQL procedures behave very must like procedures in other programming language.
Here is an example of a PL/SQL PROCEDURE addtuple1 that, given an integer I,
inserts the tuple (i, ‘xxx’) into the following example relation:
CREATE TABLE T2 (
a INTEGER,
b CHAR(10)
CREATE Procedure addtuple1 (i IN NUMBER) AS
BEGIN
INSERT INTO T2 VALUES(i. ‘xxx’);
END addtuple1;
A procedure is introduced by the keywords CREATE PROCEDURE followed by the
procedure name and its parameters. An option is to follow CREATE by OR
REPLACE. The advantage of doing so is that should you have already made the
definition, you will not get an error. On the other hand, should the previous definition
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be a different procedure of the same name, you will not be warned, and the old
procedure will be lost.
There can any number of parameters, each followed by a mode and a type. The
possible modes are IN (read-only), OUT(write-only), and INOUT(read and write).
BEGIN
Addtyple1(99);
END;
The following procedure also inserts a tuple into T2, but it takes both components as
arguments:
CREATE PROCEDURE addtuple2(
xT2,a%TYPE
y T2,b%TYPE)
AS
BEGIN
INSERT INTO T2(A,B)
VALUES(x,y);
END addtuple2;
Now, to add a tuple2(10, ‘abc’) to T2:
BEGIN
Addtyple2(10, ‘abc’);
END;
The following illustrates the use of an OUT parameter:
CREATE TABLE T3 (
a INTEGER,
b INTEGER
);
CREATE PROCEDURE addtuple3(a NUMBER, b OUT NUMBER)
AS
BEGIN
B :=4;
INSERT INTO T3 VALUES(A,B);
END;
DECLARE
v NUMBER;
BEGIN addtuple3(10,v);
END;
Note that assigning values to parameters declared as OUT or INOUT causes the
corresponding input arguments to be written. Because of this, the input argument for
an OUT or INOUT parameter should be something with an “lvaluse”, such as a
variable like v in the example above. A constant or a literal argument should not be
passed in for an OUT/INOUT parameter.
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SQL>CREATE OR REPLACE PROCEDURE MYPROC1
2 IS
3 TEMP_SAL NUMBER;
4 BEGIN
5 SELECT SAL INTO TEMP_SAL FROM EMP WHERE EMPNO = 7369;
6 IF TEMP_SAL>0 THEN
7 UPDATE EMP SET SAL = (TEMP_SAL+200) WHERE EMPNO = 7369;
8 ELSE
9 UPDATE EMP SET SAL = 200 WHERE EMPNO = 7369;
10 END IF;
11 COMMIT;
12 EXCEPTION
13 WHEN NO_DATA_FOUND THEN
14 INSERT INTO ERR(CODE, MESSAGE) VALUES(1, ‘EMPLOYE 7369 NOT
FOUND’);
15 END MYPROC1;
Procedure created.
The procedure named MYPROC1 is defined in the first line. CREATE OR REPLACE
asks Oracle to create a new procedure or if a procedure with this name already exists in
this schema to replace it. You may leave out the OR REPLACE if you don’t want this
to happen. The DECLARE section is now implicitly the section between the procedure
definition and the BEGIN statement – it is declared after the procedure declaration.
The END statement now ends a named block rather than an unnamed block. Nothing
else is required for a procedure to run. To execute the stored procedure simply do the
fallowing.
SQL>EXEC MYPROC1;
Functions:
A Function is a subprogram that computes a value. It differs from Procedure as
function returns a value.
The Syntax is:-
Function Name [(Argument [,Argument…]
Return Datatype Is
[Local Declaration]
Begin
Executable Statements
[Exception]
END [name];
Where argument stands for the following:-
Var_name [in | out | in out] datatype
[{:=|DEFAULT} VALUE]
A function has two parts SPECIFICATION and Body. The specification begins with
the keywords FUNCTION and ends with the RETURN clause which specified the
detatype of the result value. Function BODY begins with the keyword IS and ends withpdfMachine - is a pdf writer that produces quality PDF files with ease!
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the keyword END with an optional name. BODY has three parts as that of the
procedure.
The function below demonstrates the syntax of a PL/SQL function block, note that as
with a procedure the OR REPLACE clause can be left out if you don’t want the
replacement of an existing function with the same name. Function definition vary from
procedure definitions in that you must explicitly name a variable to return and you
must return a value in the variable via the RETURN statement.
CREATE OR REPLACE FUNCTION MYFUNC1
RETURN NUMBER
IS salary NUMBER(5);
BEGIN
SELECT SAL INTO salary FROM EMP where empno = 7369;
RETURN (salary);
END MYFUNC1;
Functional Sal_Ok (Salary Real, Title Read)
Return Boolean Is
Min_Sal Real;
Max_Sal Real;
Begin
Select Losal, Hisal Into Min_Sal, Max_Sal
From Sals Where Job = Title;
Return (Salary>=Min_Sal) And
(Salary <=Max_Sal);
End Sal_Ok;
If the salary is out of range sal_ok is set to false; otherwise, sal_ok is set to true. A
function is called as a part of an expression. The function sal_ok is called as
IF SAL_OK(NEW_SAL, NEW_TITLE) THEN
……
ELSE
……
END IF;
Functions should not be called inside SQL statements.
A RETURN statement immediately completes the execution of subprogram and returns
control to the caller. A subprogram can contain several return statement. Executing
any one of them completes the program.
Function Balance (Acc_Id Integer) Return Real Is
Acct_Bal Real;
Begin
Select Bal Into Acct_Bal From Accts
Where Acct No = Acct_id;
Return Acct_Bal;
End Balance;
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Introduction to Triggers:-
Triggers are a special PL/SQL construct similar to procedures. However, a procedure
is executed explicitly from another block via a procedure call, while a trigger is executed
implicitly whenever the triggering event happens.
ORACLE allows you to define procedures that are implicitly executed when an insert,
update, delete statement is issued against the associated table. These procedures are
called database triggers. Triggers can be defined only on tables and not on views. The
triggers which we use in FORMS are different since they are fired only when a trigger
point is executed with in a specific application in FORMS. The database trigger is
executed against a table, no matter what user or application issues the statement (insert,
update, delete). So the triggering event is either a INSERT, DELETE, or UPDATE
command.
Database triggers can be used to:
 Audit data modifications
 Log events transparently
 Enforce complex business rules
 Derive column values automatically
 Implement complex security authorizations
 Maintain replicate tables
A trigger has three basic parts:-
 A triggering event or statement
 A trigger restriction
 A trigger action
A trigger event or statement is the SQL statement that causes a trigger to be fired. A
trigger event can be an insert, update or delete statement for a specific table.
A trigger restriction specifies a BOOLEAN expression that must be run for the trigger
to fire. The trigger action wont take place if it evaluates to false. For example, it will
fire when the condition is true.
Quantity_On_Hand<Reorder_Level
A trigger action is the procedure that contains the SQL statements and PL/SQL code to
be executed when a triggering statement is issued and the trigger restriction evaluates to
true.
Some important points to note:
 You can create only BEFORE and AFTER triggers for tables. (INSTEAD OF
triggers are only available for views; typically they are used to implement view
updates.)
 You may specify up to three triggering events using the keyword OR.
Furthermore, UPDATE can be optionally followed by the keyword OF a list of
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137
attribute(s) in <table_name>. If present, the OF clause defines the event to be
only an update of the attribute(s) listed after OF. Here are some examples:
… INSERT ON R …
… INSERT OR DELETE OR UPDATE ON R …
…UPDATE OF A,B OR INSERT ON R…
 If FOR EACH ROW option is specified, the trigger is row-level; otherwise, the
trigger is statement-level.
 For a row-level trigger, a trigger restriction can be specified in the WHEN
clause, enclosed by parentheses. The trigger restriction is a SQL condition that
must be satisfied in order for ORACLE to fire the trigger. This condition cannot
contain subqueries. Without the WHEN clause, a trigger is fired by every
triggering event.
 <trigger_body> is a PL/SQL block, rather than sequence of SQL statements.
Oracle has placed certain restrictions on what you do in <trigger_body>, in
order to avoid situations where one trigger performs an action that triggers a
second trigger, which then triggers a third, and so on, which could potentially
create an infinite loop.
The restrictions on <trigger_body> include:
 You cannot modify the same relation whose modifiction is the event triggering
the trigger.
 You cannot modify a relation connected to the triggering relation by another
constraint such as a foreign-key constraint.
Types of Triggers
There are two kinds of triggers namely
 DML triggers and
 Instead-of triggers
In the case of DML triggers you can write triggers for insert, update or delete
operations on a detabase table. In the case of instead-of triggers you can define triggers
even on views. This kind of triggers can be defined on views only.
The general syntax of triggers is given below:
Create [or replace] trigger trigger_name
{ Before | After | Instead of } triggering_event
referencing_clause
[when trigger_condition]
[for each row]
trigger_body;
In the above syntax trigger name refers to the name of the trigger, triggering event
specifies the event that fires the trigger. The referencing clause is used to refer to the
data in the row currently being modified, trigger condition is a valid condition in the
when clause and trigger_body is the main code of the trigger.
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138
There are two levels of triggers namely ROW level and STATEMENT level. A row
trigger is fired for each time the table is affected by the triggering statement. A
statement type trigger is fired once on behalf of the triggering statement regardless of
the number of rows in the table affected.
TRIGGER TIMING
BEFORE triggers execute the trigger action before the triggering statement. AFTER
triggers execute the action after the triggering statement is executed. So with these
combinations we can create four types as follows:
 BEFORE statement trigger
 BEFORE row trigger
 AFTER statement trigger
 After row trigger
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