DBMS - Introduction

DATABASE
MANAGEMENT
SYSTEM
A. JOSEPHINE, M.C.A.,M.Phil.,
Asst.Professor
E.M.G.Yadava Women’s College,
Madurai
1

Outline
 The Need for Databases
 Data Models
 Relational Databases
 Database Design
 Storage Manager
 Query Processing
 Transaction Manager

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

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

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

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

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.

View of Data
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
 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

Data Models
 A collection of tools for describing
 Data
 Data relationships
 Data semantics
 Data constraints
 Relational model
 Entity-Relationship data model (mainly for
database design)
 Object-based data models (Object-oriented and
Object-relational)
 Semistructured data model (XML)
 Other older models:
 Network model
 Hierarchical model

Relational Model
 All the data is stored in various tables.
 Example of tabular data in the relational model
Columns
Rows

Data Definition Language (DDL)
 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 Manipulation Language (DML)
 Language for accessing and manipulating
the data organized by the appropriate data
model
 DML also known as query language
 Two classes of languages
 Pure – used for proving properties about
computational power and for optimization
 Relational Algebra
 Tuple relational calculus
 Domain relational calculus
 Commercial – used in commercial systems
 SQL is the most widely used commercial language

SQL
 The most widely used commercial language
 SQL is NOT a Turing machine equivalent
language
 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

Database Design
 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
The process of designing the general structure of the database:

Database Design (Cont.)
 Is there any problem with this relation?

Design Approaches
 Need to come up with a methodology to
ensure that each of the relations in the
database is “good”
 Two ways of doing so:
 Entity Relationship Model (Chapter 7)
 Models an enterprise as a collection of entities and
relationships
 Represented diagrammatically by an entity-
relationship diagram:
 Normalization Theory (Chapter 8)
 Formalize what designs are bad, and test for them

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.

XML: Extensible Markup Language
 Defined by the WWW Consortium (W3C)
 Originally intended as a document markup
language not a database language
 The ability to specify new tags, and to create
nested tag structures made XML a great way
to exchange data, not just documents
 XML has become the basis for all new
generation data interchange formats.
 A wide variety of tools is available for parsing,
browsing and querying XML documents/data

Database Engine
 Storage manager
 Query processing
 Transaction manager

Storage Management
 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 OS file manager
 Efficient storing, retrieving and updating of data
 Issues:
 Storage access
 File organization
 Indexing and hashing

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

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

Transaction Management
 What if the system fails?
 What if more than one user is concurrently
updating the same data?
 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.

Database Users and Administrators
Database

Database Architecture
The architecture of a database systems is greatly
influenced by
the underlying computer system on which the
database is running:
 Centralized
 Client-server
 Parallel (multi-processor)
 Distributed

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 begins Ingres prototype
 High-performance (for the era) transaction
processing

History (cont.)
 1980s:
 Research relational prototypes evolve into commercial
systems
 SQL becomes industrial standard
 Parallel and distributed database systems
 Object-oriented database systems
 1990s:
 Large decision support and data-mining applications
 Large multi-terabyte data warehouses
 Emergence of Web commerce
 Early 2000s:
 XML and XQuery standards
 Automated database administration
 Later 2000s:
 Giant data storage systems
 Google BigTable, Yahoo PNuts, Amazon, ..

DBMS - Introduction

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