DBMS Unit 1 nice content please download it

Introduction
 Purpose of Database Systems
 Database Languages
 Relational Databases
 Database Design
 Data Models
 Database Internals

 Database and SQL Overview
https://www.youtube.com/watch?v=FR4QIeZaPeM&feature=em
b_logo
 Difference between Relational DBMS and Flat File System
https://www.youtube.com/watch?v=B-l3VQuxYrs
 Basics o DBMS
https://www.youtube.com/watch?v=d11viALaCvA
 ER Diagram Steps
https://www.youtube.com/watch?v=_iVp0n8ALRA
Video references

Database Concepts
 Database is a collection of inter-related data
 Database Management System (DBMS) is a set of programs to use
and / or modify this data
 A computer based systematic record keeping system
 Database systems are used to manage collections of data that are:
 Highly valuable
 Relatively large
 Accessed by multiple users and applications, often at the same time.
 A modern database system is a complex software system whose task
is to manage a large, complex collection of data.

Database Management System (DBMS)
 DBMS contains information about a particular enterprise
 Collection of interrelated data
 Set of programs to access the data
 An environment that is both convenient and efficient to use
 Database Applications:
 Banking: all transactions
 Airlines: reservations, schedules
 Universities: registration, grades
 Sales: customers, products, purchases
 Online retailers: order tracking, customized recommendations
 Manufacturing: production, inventory, orders, supply chain
 Human resources: employee records, salaries, tax deductions
 Databases touch all aspects of our lives

 Enterprise Information
 Sales: customers, products, purchases
 Accounting: payments, receipts, assets
 Human Resources: Information about employees, salaries, payroll taxes.
 Manufacturing: management of production, inventory, orders, supply
chain.
 Banking and finance
 customer information, accounts, loans, and banking transactions.
 Credit card transactions
 Finance: sales and purchases of financial instruments (e.g., stocks and
bonds; storing real-time market data
 Universities: registration, grades
 Telecommunication: records of calls, texts, and data usage,
generating monthly bills, maintaining balances on prepaid calling
cards
Database Applications Examples

Purpose of Database Systems
In the early days, database applications were built directly on top of file
systems, which leads to
 Data redundancy and inconsistency: data is stored in multiple file
formats resulting in duplication of information in different files
 Difficulty in accessing data
 Need to write a new program to carry out each new task
 Data isolation
 Multiple files and formats
 Integrity problems
 Integrity constraints (e.g., account balance > 0) become “buried” in
program code rather than being stated explicitly
 Hard to add new constraints or change existing ones

Purpose of Database Systems (Cont.)
 Atomicity of updates
 Failures may leave database in an inconsistent state with partial
updates carried out
 Example: Transfer of funds from one account to another should
either complete or not happen at all
 Concurrent access by multiple users
 Concurrent accessed needed for performance
 Uncontrolled concurrent accesses can lead to inconsistencies
– Ex: Two people reading a balance (say 100) and updating it by
withdrawing money (say 50 each) at the same time
 Security problems
 Hard to provide user access to some, but not all, data
Database systems offer solutions to all the above problems

Simplified Database System
Application Programs / Queries
S/W to process Queries
S/W to access data
Users / Programmers
Data
Meta -Data

Simplified Database System
Meta-Data Data about data
Database A collection of related data
DBMS
A collection of programs that enable users to
create & maintain a Database
Database System The database & software together

Data Abstraction
Data abstraction means to hide certain details of how the data is stored
and maintained.
Level of abstraction
 Physical level: describes how a record (e.g., customer) is stored.
 Logical level: describes data stored in database, and the relationships
among the data.
type customer = record
customer_id : string;
customer_name : string;
customer_street : string;
customer_city : string;
end;
 View level: application programs hide details of data types. Views can
also hide information (such as an employee’s salary) for security
purposes.

View of Data
An architecture for a database system

Instances and Schemas
 Similar to types and variables in programming languages
 Schema – the logical structure of the database
 Example: The database consists of information about a set of
customers and accounts and the relationship between them)
 Analogous to type information of a variable in a program
 Physical schema: database design at the physical level
 Logical schema: database design at the logical level
 Instance – the actual content of the database at a particular point in time
 Analogous to the value of a variable
 Physical Data Independence – the ability to modify the physical schema
without changing the logical schema
 Applications depend on the logical schema
 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.

EmpNo Name Dept Sal
1234 Vikas Admin 10000
5678 Sachin Finance
30000
1357 Kiran HR 20000
2468 Manoj Admin 15000
Instance
Schema
Example: Schema & Instance

Data Models
 A collection of tools for describing
 Data
 Data relationships
 Data semantics
 Data constraints
Semantic Models
Semantic Models
Data Models
Object Based
Logical Models
Record Based Logical
Models
Hierarchical Model
Object
Oriented model
Relational Model
ER-Model
Network Model
Data Models
Object Based
Logical Models
Record Based Logical
Models
Hierarchical Model
Object
Oriented model
Relational Model
ER-Model
Network Model

The Entity-Relationship Model
 Models an enterprise as a collection of entities and relationships
 Entity: a “thing” or “object” in the enterprise that is distinguishable
from other objects
 Described by a set of attributes
 Relationship: an association among several entities
 Represented diagrammatically by an entity-relationship diagram:

Relational Model
 Example of tabular data in the relational model
Attributes

Network Data Model
 Data is represented by collection of records
 Similar to an entity in the E-R model
 Records & their fields are represented as a record type
 Relationships among data are represented by links

Hierarchical Data Model
 A Hierarchical database consists of a collection of records that are
connected to one another through links
 A record is a collection of fields, and each field contains only one data
value
 Differs from the Network Model in that the records are organized as
collections of trees rather than as arbitrary graphs
 A link is an association between two records

Hierarchical Data Model (Contd…)

Object-Relational Data Models
 Built on top of relational data models by including object orientation and
constructs to deal with added data types
 Allow attributes of tuples to have complex types, including non-atomic
values such as nested relations
 Provide a convenient migration path for users of RDBMS who wish to use
OO features
 Compatibility with existing relational languages such as SQL

Object-Relational Data Models (Contd…)
 Example  Library Information System
 Each book has:
 Title
 A set of authors
 Publisher
 A set of keywords
 Relation BOOKS with composite attributes

Purpose of ER Model
 To express the logical properties of the database in an enterprise
schema
 To express the logical structure of the database
 Used mainly as a design tool and documentation for the system
 Describes the environment in terms of entities

Identifying Entities
 Given a system description we can identify entities by:
 Select all nouns
 Eliminate the one’s not necessary
 Filter those for which system needs to store records
 Identify the attributes that describe the entity
 Check how many records could be created for the same

ERM – Important Definitions
 An entity is an object or concept about which we store information
 Entity Set
 An entity set is a set of entities of the same type that share the
same properties or attributes
 Attributes
 Attributes are descriptive properties possessed by each member
of an entity set
 Possible attribute data values, including ‘null ‘ values are called
as Domain Sets
 Depending on the domain sets and storage related properties the
attributes of an entity are divided into several categories

Attribute Types
 Simple / Atomic: The value of the attribute can not be further divided
into components It is treated as a single atomic value
e.g. Empno
 Composite: The value of the attribute can be further divided into
components
e.g. Ename – fname, mname, lname
Empno
Emp-Name
F_Name
M_Name
L_Name

Attribute Types (Contd…)
 Single-Valued : Those attributes that can have only one value at a
time
e.g. Studno
 Multi-Valued : Those attributes that can have more than one value
at a time
e.g. Contact_Number
Studno
Contact_Number

 Stored : These attributes are stored in a database table
e.g. DtOfBirth
 Derived : These attributes are not stored in database table. Their
value can be derived by using values of one or more stored
attributes
e.g. Age (could be derived from Date of Birth)
Age
DtOfBirth

 Null Attributes
 Null attributes means those which can have NULL value i.e. the
attribute value may not be present for a tuple
 By default all attributes can have NULL values unless one
specifies either Primary Key Constraint or Not Null Constraint for
the attribute
 Null attributes can not be indicated / represented in E-R diagram
and must be documented separately
 An attribute value is left NULL in the following situations
 The attribute is not applicable for the tuple
 The attribute is applicable for the tuple however its value is not
known currently or can not be found out currently

Entity
Strong Entity / Dominant Entity: An entity which has its own identifier,
and is not built from another entities’ identifier
Weak Entity: An entity which borrows its identity from at
least one other entity
Employee
Dependent

Entity Relationship
Relationship
 A relationship is an association among several entities. Applies when
you want to keep track of connection between two distinct entities
Identifying a Relationship
 Special type of relationship that applies when we have weak entity
EMP DEPT
Belongs to
EMP Has Dependant

ERM - Degree of Relationship
 Unary: Degree 1
 Binary: Degree 2
 Ternary: Degree 3
 N-ary: Degree n
manages
EMPLOYEE
ORDER ITEM
Has
Order_dtls
CUSTOMER LOAN
Soc SNo
cust-city
loan-no
amount
borrower
Cust-name

Mapping Cardinalities
 Mapping Cardinalities expresses the number of entities to which another
entity can be associated via a relationship set
 Possible mapping cardinalities:
 One-to-One: An entity in A is associated with only one entity in B and
vice-versa e.g.
College & Principal, Customer & Account with the condition that a
customer can handle only one account
 One-to-Many: An entity in A is associated with any number (zero or
more) of entities in B, however an entity in B is associated with at most
one entity in A
College Ha
s
Principal
1 1
Work
s in
Department Faculty
1 M

Mapping Cardinalities (Contd…)
 Many-to-Many: Entities in A and B are associated with any number
of entities from each other
e.g. Employees can be assigned to more than one Project & a
Project should be assigned to at least 3 employees
Course Teaches Instructor
1
M
 Many-to-One: An entity in A is associated with at most one entity in B & an
entity in B is associated with any number in A
e.g. Customers & Joint Account, Employee & Department or an
Instructor & Courses taught
M
Employee Assign
To
Project
N

Participation of Entity Sets in Relations
 Total: The participation of an entity set E in a relationship set R is said
to be total if every entity in E participates in at least one relationship R
 Partial: Part of the set of entities participate in some relationship
EMP Has Dependant
EMP DEPT
Belongs to

ERM – A Recap
 Overall logical structure of a database can be expressed graphically
by an ER diagram
 Rectangles: Entity Sets
 Ellipses: Attributes (Simple, Single)
 Ellipses connected with many Ellipses: Composite Attributes
 Concatenated Ellipse: Multi-value Ellipse
 Dotted Ellipse : Derive Ellipse

ERM – A Recap (Contd…)
 Diamonds: Relationship Sets
 Lines: link Attributes to Entity Sets and Entity Sets to
Relationship Sets
 Double Lines: Total Participation
 Double Rectangle: Weak Entity
 Attribute with an underline: Primary Key ____

Entity Relationship Model
Customer loan
Cust-name
Soc-SNo
cust-city
loan-no
amount
borrower

ERM – An Example
1
1
PROJECT
DEPT
Belongs to
Assigned to
N 1
N
M
N
1
loc
dname
detno
projname
proj_id
Manages
No of hrs
EMP
DEPENDENTS
has
empno
ename sal
lname
fname mname
Dep_name relation

ERM - Explanation
 This schema has 3 entity types:
 EMPLOYEE
 DEPT
 PROJECT
& 1 weak entity type DEPENDENT
 There is an M:N relationship between PROJECT & EMP entity sets
 There is a 1:1 relationship between DEPT & PROJECT
 There is a 1:M relationship between DEPT & EMP entity sets
 No of hrs is an attribute on the relation “assigned to” between EMP &
PROJECT
 There is total participation for DEPENDENT, since we can not have a
Dependent without an Employee

ER Model for National Hockey League (NHL)
 The NHL has many teams,
 Each team has a name, a city, a coach, a captain,
and a set of players,
 Each player belongs to only one team,
 Each player has a name, a position (such as left
wing or goalie), a skill level, and a set of injury
records,
 A team captain is also a player,
 A game is played between two teams (referred to as
host_team and guest_team) and has a date (such
as May 11th, 1999) and a score (such as 4 to 2).

Extended E-R Diagram: Specialization
 A top-down design process
 Designates sub groupings within an entity set that are distinctive from
other entities in the set
 These sub groupings become lower-level entity sets that have special
attributes or participate in relationships
 Depicted by a triangle component labeled ISA
e.g. Customer “is a” Person

Extended E-R Diagram: Generalization
 A bottom-up design process
 A number of entity sets that share the same features are combined
into a higher-level entity set
 Simple inversion of Specialization
 Represented in an E-R diagram in the same way

Extended E-R Diagram: Aggregation
 In aggregation the aggregate entity set is treated as a single unit without
concern for the details of its internal structure
 E-R model doesn’t express Represent relationships among relationships

Aggregation (Contd…)
 Suppose we want to record managers for tasks performed by an
employee at a branch

 Aggregation is an abstraction through which relationships are treated
as higher level entities
 Building Composite Objects from Component Objects 
 3 Cases:
 When the aggregate attribute values of an object forms a whole
object
 When we view an aggregate relationship as ordinary relationship
 When we represent relationship among relationships

1 COMPANY CANDIDATE
Interviews
2
JOB_OFFER
COMPANY CANDIDATE
Interviews
3
JOB_OFFER
Results_in
COMPANY CANDIDATE
Interviews

4
Aggregation
Relationship
Interviews
COMPANY CANDIDATE
JOB_OFFER
Results_in

Case Study – ER Model For a college DB
Assumptions :
 A college contains many departments
 Each department can offer any number of courses
 Many instructors can work in a department
 An instructor can work only in one department
 For each department there is a Head
 An instructor can be head of only one department
 Each instructor can take any number of courses
 A course can be taken by only one instructor
 A student can enroll for any number of courses
 Each course can have any number of students

Steps in ER Modeling
 Identify the Entities
 Find relationships
 Identify the key attributes for every Entity
 Identify other relevant attributes
 Draw complete E-R diagram with all attributes including Primary Key
 Review your results with your Business users

 Step 1: Identify the Entities
 DEPARTMENT
 STUDENT
 COURSE
 INSTRUCTOR
 Step 2: Find the relationships
 One course is enrolled by multiple students and one student enrolls for
multiple courses,hence the cardinality between course and student is Many
to Many.
 The department offers many courses and each course belongs to only one
department,hence the cardinality between department and course is One to
Many.
 One department has multiple instructors and one instructor belongs to one
and only one department , hence the cardinality between department and
instructor is one to Many.
 Each department there is a “Head of department” and one instructor is
“Head of department “,hence the cardinality is one to one .
 One course is taught by only one instructor, but the instructor teaches
many courses,hence the cardinality between course and instructor is many
to one.

Step 3: Identify the key attributes
 Deptname is the key attribute for the Entity “Department”, as it identifies
the Department uniquely.
 Course# (CourseId) is the key attribute for “Course” Entity.
 Student# (Student Number) is the key attribute for “Student” Entity.
 Instructor Name is the key attribute for “Instructor” Entity.
Step 4: Identify other relevant attributes
 For the department entity, the relevant attribute is location
 For course entity, course name,duration,prerequisite
 For instructor entity, room#, telephone#
 For student entity, student name, date of birth

Step 5 : Draw complete E-R diagram with all
attributes including Primary Key

ER Model for movie studio
A movie studio wishes to create a database to manage their files of
movies, actors and directors. The following are relevant facts
 Each actor has appeared in many movies
 Each director has directed many movies
 Each movie has one director and one and more actors
 Each actor and director may have several addresses and telephone
numbers.
Identify the entities and attributes. Draw ER diagram.

Merits and Demerits of ER Modeling
Merits
 Easy to understand. Represented in Business Users Language. Can
be understood by non-technical specialist.
 Intuitive and helps in Physical Database creation.
 Can be generalized and specialized based on needs.
 Can help in database design.
 Gives a higher level description of the system.
Demerits
 Physical design derived from E-R Model may have some amount of
ambiguities or inconsistency.
 Sometime diagrams may lead to misinterpretations

Reduction of an ER to Tables
 An E-R schema can be represented by collection of tables. Each
entity set and for each relationship set in the in the database
design, there is a unique table to which we assign the name of
the corresponding entity set or relationship set. Each table has
multiple columns, each of which has a unique name.
 Representation of Strong Entity Sets with Simple Attributes.
 Representation of Strong Entity Sets with Complex
Attributes
 Representation of Weak Entity Sets
 Representation of Relationship Sets
 Redundancy of Tables
 Combination of Tables
 Representation of Generalization
 Representation of Aggregation

 The table for the relationship set manages between the
aggregation of works-on and the entity set manager includes a
column for each attribute in the primary keys of the entity set
manager and the relationship set works-on.
 It would also include a column for any descriptive attributes, if
they exist, of the relationship set manages.
 We then transform the relationship sets and entity sets within
the aggregated entity.
Representation of Aggregation

 Specification notation for defining the database schema
Example: create table instructor (
ID char(5),
name varchar(20),
dept_name varchar(20),
salary numeric(8,2))
 DDL compiler generates a set of table templates stored in a data
dictionary
 Data dictionary contains metadata (i.e., data about data)
 Database schema
 Integrity constraints
 Primary key (ID uniquely identifies instructors)
 Authorization
 Who can access what
Data Definition Language (DDL)

 Language for accessing and updating the data organized by the
appropriate data model
 DML also known as query language
 There are basically two types of data-manipulation language
 Procedural DML -- require a user to specify what data are needed and
how to get those data.
 Declarative DML -- require a user to specify what data are needed without
specifying how to get those data.
 Declarative DMLs are usually easier to learn and use than are
procedural DMLs.
 Declarative DMLs are also referred to as non-procedural DMLs
 The portion of a DML that involves information retrieval is called a
query language.
Data Manipulation Language (DML)

 SQL query language is nonprocedural. A query takes as input several
tables (possibly only one) and always returns a single table.
 Example to find all instructors in Comp. Sci. dept
select name
from instructor
where dept_name = 'Comp. Sci.'
 SQL is NOT a Turing machine equivalent language
 To be able to compute complex functions SQL is usually embedded in
some higher-level language
 Application programs generally access databases through one of
 Language extensions to allow embedded SQL
 Application program interface (e.g., ODBC/JDBC) which allow SQL
queries to be sent to a database
SQL Query Language

 Non-procedural query languages such as SQL are not as powerful as
a universal Turing machine.
 SQL does not support actions such as input from users, output to
displays, or communication over the network.
 Such computations and actions must be written in a host language,
such as C/C++, Java or Python, with embedded SQL queries that
access the data in the database.
 Application programs -- are programs that are used to interact with
the database in this fashion.
Database Access from Application Program

The process of designing the general structure of the database:
 Logical Design – Deciding on the database schema. Database design
requires that we find a “good” collection of relation schemas.
 Business decision – What attributes should we record in the database?
 Computer Science decision – What relation schemas should we have and
how should the attributes be distributed among the various relation
schemas?
 Physical Design – Deciding on the physical layout of the database
Database Design

Database Management System Internals
 The design of a database system include consideration of the
interface between the database system and the operating
system.
 A database system is partitioned into modules that deal with
each of the responsibilities of the overall system.
 The functional components of Database system is broadly
classified into following categories.
 The storage manager,
 The query processor component,
 The transaction management component.

 Storage manager 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
 The storage manager components include:
 Authorization and integrity manager
 Transaction manager
 File manager
 Buffer manager
Storage Manager

 The storage manager implements several data structures as
part of the physical system implementation:
 Data files -- store the database itself
 Data dictionary -- stores metadata about the structure of the
database, in particular the schema of the database.
 Indices -- can provide fast access to data items. A database
index provides pointers to those data items that hold a particular
value.
Storage Manager

The query processor components include:
 DDL interpreter -- interprets DDL statements and records the
definitions in the data dictionary.
 DML compiler -- translates DML statements in a query
language into an evaluation plan consisting of low-level
instructions that the query evaluation engine understands.
 The DML compiler performs query optimization; that is, it picks the lowest
cost evaluation plan from among the various alternatives.
 Query evaluation engine -- executes low-level instructions
generated by the DML compiler.
Query Processor

1. Parsing and translation
2. Optimization
3. Evaluation
Query Processing

 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.
Transaction Management

 Centralized databases
 One to a few cores, shared memory
 Client-server,
 One server machine executes work on behalf of multiple client machines.
 Parallel databases
 Many core shared memory
 Shared disk
 Shared nothing
 Distributed databases
 Geographical distribution
 Schema/data heterogeneity
Database Architecture

Database Architecture
(Centralized/Shared-Memory)

Database applications are usually partitioned into two or three parts
 Two-tier architecture -- the application resides at the client machine,
where it invokes database system functionality at the server machine
 Three-tier architecture -- the client machine acts as a front end and
does not contain any direct database calls.
 The client end communicates with an application server, usually through a
forms interface.
 The application server in turn communicates with a database system to
access data.
Database Applications

Two-tier and three-tier architectures
Old
Modern
(web browser)

 Design a generaliztion – specialization hierarchy for a motor –
vehicle sales. The company sells motorcycles, passenger cars,
vans and buses. Justify the placement of attributes at each
level of hierarchy.
 Projects must be classified as either top secret or civilian (but
not both). There is information specific to top secret projects
and specific to civilian projects that we want to record.
Example

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