Week-1&2(CSPC-202)

Introduction to DBMS

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DEPARTMENT: COMPUTER SCIENCE AND ENGINEERING

 COURSE CODE: CSPC-202

 COURSE TITLE: DATABASE MANAGEMENT SYSTEM
 COURSE DESIGNATION: REQUIRED
 PRE-REQUISITES: File Management Using any Programming
Language

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 COURSE OUTCOMES

 To understand the different issues involved in the design and implementation

of a database system. To study the physical and logical database designs,
database modeling, relational, hierarchical, and network models
 To understand and use data manipulation language to query, update, and
manage a database
 To develop an understanding of essential DBMS concepts such as: database
security, integrity, concurrency, distributed database, and intelligent database,
Client/Server (Database Server), Data Warehousing.
 To design and build a simple database system and demonstrate competence
with the fundamental tasks involved with modeling, designing, and
implementing a DBMS.

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 TOPICS To Be COVERED

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 TOPICS To Be COVERED contd..

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 TEXT BOOKS, AND/OR REFERENCE
MATERIAL
 Korth, Silbertz, Sudarshan, “Database Concepts”, McGraw Hill
 Elmasri, Navathe, “Fundamentals Of Database Systems”, Addision
Wesley
 Date C J, “An Introduction To Database System”, Addision Wesley
 Bipin C. Desai, “An introduction to Database Systems”, Galgotia
Publication
 Rob and Coronel, “Database Systems 5thEdition”,Cengage Learning,
New Delhi

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 Outline

 The Need for Databases

 Data Models
 Relational Databases
 Database Design
 Storage Manager
 Query Processing
 Transaction Manager

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 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: 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 can be very large.
 Databases touch all aspects of our lives

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 University Database Example
 Application program examples
 Add new students, instructors, and courses
 Register students for courses, and generate class rosters
 Assign grades to students, compute grade point averages
(GPA) and generate transcripts
 In the early days, database applications were built directly on
top of file systems

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 Drawbacks of using file systems to store data

 Data redundancy and inconsistency

 Multiple file formats, 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

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 Drawbacks of using file systems to store data (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 access needed for performance
 Uncontrolled concurrent accesses can lead to inconsistencies
 Example: 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

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 Levels of Abstraction
 Physical level: describes how a record (e.g., instructor) is stored.
 Logical level: describes data stored in database, and the relationships
among the data.
type instructor = record
ID : string;
name : string;
dept_name : string;
salary : integer;
end;
 View level: application programs hide details of data types. Views can
also hide information (such as an employee’s salary) for security
purposes.

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 View of Data

An architecture for a database system

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 View of Data- Three Level Architecture

An architecture for a database system

 Instances and Schemas
 Similar to types and variables in programming languages
 Logical Schema – the overall logical structure of the database
 Example: The database consists of information about a set of
customers and accounts in a bank and the relationship between them
 Analogous to type information of a variable in a program
 Physical schema– the overall physical structure of the database
 Instance – the actual content of the database at a particular point in time
 Analogous to the value of a variable

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Mapping and Data Independence
 Mapping: The process to convert a request from external
level and data stored is called mapping. The mapping
defines the correspondence between three levels.
 External/Conceptual Mapping
 Conceptual/Internal Mapping
Mapping and Data Independence
 Data Independence: The ability to modify a scheme
definition in one level without affecting a scheme definition in a
higher level is called data independence.
 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.
 Logical data Independence- The ability to change logical
schema without changing external schema is called logical data
independence. E.g by adding or removing attributes , tables etc.
Architecture of DBMS

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 Internal Schema
 Lowest level of Abstraction and has knowledge of both h/w and s/w.
 Defines how data physically stored in database.
 Also called Physical Schema
 Location
 Name
 Format

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 Conceptual Schema
 It deals with the modeling of whole database.
 Don’t know how and where the data is stored but knows “WHAT
DATA IS STORED”.
 The users of this level are not concerned with how these logical data
structures will be implemented at the physical level, rather they just
are concerned about what information is to be kept in the database.
 Example When we use the Describe Command.

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 External Schema
 It is very close to users and is the highest level of Abstraction.
 Allows to see only the data of interest to them.
 Users – Application programmers or end-users.
 Any no. of external views – external schema.

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 Types of Database Models
 A Database model defines the logical design and structure of a database
and defines how data will be stored, accessed and updated in a database
management system
 A collection of tools for describing
 Data
 Data relationships
 Data semantics
 Data constraints

Types of Data Models

 Hierarchical Model
 Network Model
 Entity-relationship Model
 Relational Model
 Object-based data models (Object-oriented and Object-relational)
 Semistructured data model (XML)

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 Hierarchical Model

 This database model organizes data into a tree-like-structure, with a

single root, to which all the other data is linked. The hierarchy starts
from the Root data, and expands like a tree, adding child nodes to the
parent nodes.
 In hierarchical model, data is organized into tree-like structure with
one-to-many relationship between two different types of data, for
example, one department can have many courses, many professors
and of-course many students

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Hierarchical Model

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 Network Model
 This is an extension of the Hierarchical model. In this model data is
organized more like a graph, and are allowed to have more than one
parent node.
 In this database model data is more related as more relationships are
established in this database model. Also, as the data is more related,
hence accessing the data is also easier and fast. This database model
was used to map many-to-many data relationships.

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Network Model

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 Entity-Relationship Model
 In this database model, relationships are created by dividing object of
interest into entity and its characteristics into attributes.
 Different entities are related using relationships.
 E-R Models are defined to represent the relationships into pictorial
form to make it easier for different stakeholders to understand.
 This model is good to design a database, which can then be turned
into tables in relational model.

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Entity-Relationship Model

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 Relational Model
 In this model, data is organized in two-dimensional tables and the
relationship is maintained by storing a common field.
 The basic structure of data in the relational model is tables. All the
information related to a particular type is stored in rows of that table.
 Hence, tables are also known as relations in relational model.

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Relational Model

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 Object-Relational Data Models
 Relational model: flat, “atomic” values
 Object Relational Data Models
 Extend the relational data model 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.
 Preserve relational foundations, in particular the declarative access to
data, while extending modeling power.
 Provide upward compatibility with existing relational languages.

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 Semistructured Data Model
 Permits the specification of data where individual data items of the
same type have different set of attributes.
 XML(Extensible Mark up Language) is used to represent semi
structured data.

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 RDBMS
 A Relational Database Management System is the DBMS that is
based on the relational model.
 It stores the data in the form of related tables. A table is a collection of
related data entries and it consists of columns and rows.
 RDBMS is the basis for SQL, and for all modern database systems
such as:MS SQL Server, IBM DB2, Oracle, MySQL, Microsoft Access.

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 SQL
 SQL stands for Structured Query Language.
 It is used to access and manipulate database.
What Can SQL do?
 SQL can execute queries against a database
 SQL can retrieve data from a database
 SQL can insert records in a database
 SQL can update records in a database
 SQL can delete records from a database
 SQL can create new databases
 SQL can create new tables in a database

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 DDL
 DDL is the Data Definition Language, which deals with database
schemas and descriptions, of how the data should reside in the
database.
 CREATE - to create a database and its objects like (table, index,
views, store procedure, function, and triggers)
 ALTER - alters the structure of the existing database
 DROP - delete objects from the database
 TRUNCATE - remove all records from a table, including all spaces
allocated for the records are removed
 COMMENT - add comments to the data dictionary
 RENAME - rename an object

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 DML
 DML is the Data Manipulation Language which deals with data
manipulation and includes most common SQL statements such
SELECT, INSERT, UPDATE, DELETE, etc., and it is used to store,
modify, retrieve, delete and update data in a database.
 SELECT - retrieve data from a database
 INSERT - insert data into a table
 UPDATE - updates existing data within a table
 DELETE - Delete all records from a database table
 CALL - call a PL/SQL or Java subprogram

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 DCL
 DCL is the Data Control Language which includes commands such as
GRANT and mostly concerned with rights, permissions and other
controls of the database system.
 GRANT - allow users access privileges to the database
 REVOKE - withdraw users access privileges given by using the
GRANT command.

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 TCL
 TCL is the Transaction Control Language which deals with a
transaction within a database.
 COMMIT - commits a Transaction
 ROLLBACK - rollback a transaction in case of any error occurs.
Restores database to original state since the last COMMIT command
in transactions
 SAVEPOINT - to rollback the transaction making points within groups.
 SET TRANSACTION - specify characteristics of the transaction

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 Database Access from Application Program

 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.

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 Database Design

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

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 Overall Database Structure

 A database system is partitioned into modules that deal with each of the
responsibilities of the overall system.
 The functional components of a database system can be divided into
 The storage manager,
 The query processor component,

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 Database Architecture
(Centralized/Shared-Memory)

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 Storage Manager
 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 OS file manager
 Efficient storing, retrieving and updating of data
 The storage manager components include:
 Authorization and integrity manager: Tests for satisfaction of integrity
constraints and checks the authority of users to access data
 Transaction manager: Ensures that database remains in a consistent
state despite system failures and concurrent transaction processing
 File manager: Allocation of space on disk storage and data structures
used to represent information stored on disk
 Buffer manager: Responsible for fetching data from disk storage into
main memory.

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 Storage Manager (Cont.)

 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.

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 Query Processor

 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.

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 Query Processing

1. Parsing and translation

2. Optimization
3. Evaluation

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 Query Processing (Cont.)
 Alternative ways of evaluating a given query
 Equivalent expressions
 Different algorithms for each operation
 Cost difference between a good and a bad way of evaluating a
query can be enormous
 Need to estimate the cost of operations
 Depends critically on statistical information about relations
which the database must maintain
 Need to estimate statistics for intermediate results to compute
cost of complex expressions

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 Database Applications

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.

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Two-tier and three-tier architectures

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Database Users and Administrators

Database

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 Database Users
 Naïve Users: are unsophisticated users who interact with the system
by invoking one of the application programs that have been written
previously

 Application Programmers: are computer professionals who write

application programs.

 Sophisticated Users: These users interact with the database without

writing programs. Instead they form their requests in database query
language.

 Database Administrator: A person who has central control over the

system is called a database administrator (DBA)

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 Database Administrator

A person who has central control over the system is called a database
administrator (DBA). Functions of a DBA include:
 Schema definition
 Storage structure and access-method definition
 Schema and physical-organization modification
 Granting of authorization for data access
 Routine maintenance
 Periodically backing up the database
 Ensuring that enough free disk space is available for normal
operations, and upgrading disk space as required
 Monitoring jobs running on the database

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 History of Database Systems

 1950s and early 1960s:

 Data processing using magnetic tapes for storage
 Tapes provided only sequential access
 Punched cards for input
 Late 1960s and 1970s:
 Hard disks allowed direct access to data
 Network and hierarchical data models in widespread use
 Ted Codd defines the relational data model
 Would win the ACM Turing Award for this work
 IBM Research begins System R prototype
 UC Berkeley (Michael Stonebraker) begins Ingres prototype
 Oracle releases first commercial relational database
 High-performance (for the era) transaction processing

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 History of Database Systems (Cont.)

 1980s:
 Research relational prototypes evolve into commercial systems
 SQL becomes industrial standard
 Parallel and distributed database systems
 Wisconsin, IBM, Teradata
 Object-oriented database systems
 1990s:
 Large decision support and data-mining applications
 Large multi-terabyte data warehouses
 Emergence of Web commerce

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 History of Database Systems (Cont.)

 2000s
 Big data storage systems
Google BigTable, Yahoo PNuts, Amazon,

 “NoSQL” systems.

 Big data analysis: beyond SQL

 Map reduce and friends

 2010s
 SQL reloaded
 SQL front end to Map Reduce systems

 Massively parallel database systems

 Multi-core main-memory databases

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End of Week 1&2

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