Basic RDBMS Concepts

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RDBMS Concepts

BY

KINGSHUK SRIVASTAVA



Agenda

Introduction

Database Management Systems

Normalization

Codd’s Rules SQL



Introduction



Why Study Databases??

Shift from computation to information

always true for corporate computing

Web made this point for personal computing

more and more true for scientific computing

Need for DBMS has exploded in the last years Corporate: retail swipe/clickstreams, “customer

relationship mgmt”, “supply chain mgmt”, “data

warehouses”, etc.

Scientific: digital libraries, Human Genome

project, NASA Mission to Planet Earth, physicalsensors, grid physics network

DBMS encompasses much of CS in a practical

discipline

OS, languages, theory, AI, multimedia, logic

Yet traditional focus on real-world apps



What’s the intellectual content?

representing information data modeling

languages and systems for querying data

complex queries with real semantics*

over massive data sets concurrency control for data manipulation

controlling concurrent access ensuring transactional semantics

reliable data storage maintain data semantics even if you pull the

plug

* semantics: the meaning or relationship of meanings of a sign or set of signs



Data?

Information?



Initial Data Storage Methods

Data is represented in one or more flat files

Flat file is nothing but electronic representation of cardboard file.

Every business group has its own set of files



Disadvantages of Flat File Systems

No centralized control.

Data Redundancy

Data Inconsistency

Data can not be shared

Standards can not be enforced

Security issues

Integrity can not be maintained Data dependence




A system whose overall purpose is to record andmaintain information

A database is a repository for stored data andprograms to manipulate it.



Advantages of DBMS

Centralized control.

No Data Redundancy

Data Consistency

Data can be shared

Standards can be enforced

Security can be enforced

Integrity can be maintained Data independence



Data Models

A data model is a collection of concepts fordescribing data

A Schema is a description of a particular collection of

data using the given data model The relational model is the most widely used model

today



Levels of Abstraction

Many Views and single Conceptualand Physical Schema• Views Describe how users see the data

•Conceptual Schema defines the logicalstructure•Physical Schema defines the physicalfiles and Indexes



Example University Database

Conceptual Schema Students(sid: string, name: string, login: string, age:

integer, gpa:real)

Courses(cid: string, cname:string, credits:integer)

Physical Schema Relations stored as unordered files.

Index on first column of Students.

External Schema (View): Course_info(cid:string,enrollment:integer)



Data Independence

Applications insulated from how data is structuredand stored

Logical Data Independence: Protection from changesin logical structure of data

Physical Data Independence: Protection from

changes in physical structure of data



Structure of a DBMS



ACID Test

Atomicity

Consistency

Isolation

Durability



Transactions: ACID Properties

Key concept is a transaction: a sequence of database actions(reads/writes).

DBMS ensures atomicity (all-or-nothing property) even if systemcrashes in the middle of a Xact.

Each transaction, executed completely, must take the DB betweenconsistent states or must not run at all.

DBMS ensures that concurrent transactions appear to run in isolation.

DBMS ensures durability of committed Xacts even if system crashes.

Note: can specify simple integrity constraints on the data. The DBMSenforces these.

Beyond this, the DBMS does not understand the semantics of thedata.

Ensuring that a single transaction (run alone) preserves consistency is largely the user’s responsibility!



Types of DBMS

Hierarchical

Network

Relational



Hierarchical DBMS (Contd.)

Can not handle Many-Many relations

Can not reflect all real life situations

Anomalies in insert, delete and update operations.



Network DBMS

Data is represented by records and pointers

Addresses Many-Many relations

Insert,delete,update operations possible

Complex in design



Relational DBMS

Based on Relational Mathematics principles

Data is represented in terms of rows and columns of a table

Addresses all types of relations Easy to design

No anomalies for insert/delete/update



Relational Terminology

Tuple (Row) Attribute (Column)Relation (Table)

Integrity ConstraintsPrimary Key Alternate Key Foreign Key



Normalization



Normalization

Normalization - process of removing dataredundancy by decomposing relations in a Database.

De normalization - carefully introduced redundancy to improve query performance.



Normalization through decomposition

The decomposition approach starts with one relationand the relation is decomposed into more number of relations to remove insert, delete and updateanomalies.

1NF, 2NF, 3NF and BCNF can be achieved by thisapproach.



Un normalized Form

A relation is said to be in Un normalized Form (0NF)if the values of any of its attributes are non-atomic.In other words more than one value is associated

with each instance of the attribute.



Un normalized Relation

PQS#

P# QTY

S1 P1P2

P3

P4

300200

400

200

S2 P1

P2

300

400

S3 P2 200



First Normal Form

A Relation is said to be in First Normal Form (1 NF) if the values of each attribute of the relation areatomic. In other words, only one value is associated

with each attribute and the value is not a set or a listof values.



Functional Dependency

Given a relation R, attribute Y of R is functionallydependent on attribute X if and only if each X-valuein R has associated with it precisely one Y-value in R (at any one time)



Full Functional Dependency

Attribute Y is fully functionally dependent on attributeX if it is functionally dependent on X AndFunctionally dependent on any proper subset of X



Second Normal Form

A relation R is in Second Normal Form (2 NF) if it is inthe 1NF and every non key attribute is fullfunctionally dependent on the primary key.



Third Normal Form

A relation R is in Third Normal Form (3 NF) if andonly if it is in the 2NF and every non-key attribute isnon-transitively dependent on the primary key.



Boyce Codd Normal Form

A relation R is in Boyce/Codd Normal Form (BCNF) if and only if every determinant is a candidate key.

An attribute, possibly composite, on which some otherattribute is fully functionally dependent is adeterminant .



A subject can be taught to a student by only oneteacher.

Each teacher teaches only one subject.

Each subject is taught by several teachers.

Student Subject TeacherSmith Maths Prof. WhiteSmith Physics Prof. GreenJones Maths Prof. WhiteJones Physics Prof. Brown



No two students can get same position in samesubject.

Student Subject PositionSmith Maths 1Smith Physics 2Jones Maths 2

Jones Physics 1



Codd’s Rules



Codd’s Rules

• 1985

• Proposed to test DBMSs for confirmation to concept of Codd’s Relational model

• Hardly any commercial product follows all

• Oracle = 81 2 out of 12.



Rule Zero

• For a system to qualify as an RDBMS it must be able to manage its databases entirely through its Relationalcapabilities

• The other 12 rules derive from this rule



Rule 1: Information Rule

• All Information (inlcuding metadata) is to berepresented as data stored in cells of tables.

• The rows and columns have to be strictly unordered.



Rule 2: Guaranteed Access

• Each unique piece of data (atomic value) should be accesible by : TableName + Primary Key (Row) +

Attribute (Column)

• Violation: Ability to directly access via pointers



Rule3: Systematic treatment of NULL

• NULLs may mean: Missing data, Not applicable, No

value

• Should be handled consistently - Not Zero or Blank

• Primary keys — Not NULL

• expressions on NULL should give NULL

◭◭ ◮◮



Rule4: Active On-Line Catalogue

• Database dictionary (Catalogue) to have description of the Database

• Catalogue to be governed by same rules as rest of the database

• The same query language to be used on catalogue as on the application database



Rule5: Powerful language

• One well defined language to provide all manners of access to data

• Example: SQL

• If file supporting table can be accessed by any manner except a SQL Interface, then a violation



Rule6: View Updating Rule

• All views that are theoretically updatable should be

updatable

• View = "Virtual table", temporarily derived from base tables

• Example: If a view is formed as join of 3 tables, changes to view should be reflected in base tables

• Not updatable: View does not have NOT-NULL at-

tribute of base table

• Problems with computed fields in view e.g. Total Income = White income + Black income



Rule7: Relational level operations-

• There must be insert, update, delete operations at the

level of Relations

• Set operations like Union,Intersection and Minus

should be supported



Rule8: Physical Data Independence

• The physical storage of data should not matter to the

system

• If say, some file supporting table was renamed or moved from one disk to another, it should not effect the applications.



Rule9: Logical Data Independence

• If there is change in the logical structure (table struc-

tures) of the database the user view of the data should

not change

• implemented through views. Say, if a table is split into

two tables, a new view should give result as the join

of the two tables

• Difficult rule to satisfy



Rule10: Integrity Independence

• The database should be able to enforce its own integrity rather than using other programs

• Integrity rules = Filter to allow correct data, should

be stored in Data Dictionary

• Key and check constraints, triggers etc should be

stored in Data Dictionary• This also makes RDBMS independent of front end

R l 11 Di t ib ti I d d



Rule11: Distribution Independence

• A database should work properly regardless of its distributionacross a network

• This lays foundation of Distributed databases

• Similar to Rule8 only that applies to distribution on

a local Disk



Rule12: Non-subversion Rule

• If low level access is allowed to a system it should not be able to subvert or bypass integrity rules to change

data

• This may be achieved by some sort of locking or encryption

• Some low level access tools are provided by vendors that violate these rules for extra speed



Structured Query Language



Structured Query Language

DDL – Data Definition Language

DML – Data Manipulation language

DCL – Data Control Language



DDL

Create

Alter

Drop

Truncate



DML

Insert

Update

Delete

Select



DCL

Commit

Rollback

Save point

Set transaction



Integrity Constraints

Primary key (PK)

Foreign Key (FK)

Unique key (UK)

Not Null Check



Data Types

Character

Varchar2

Number

Date BLOB

BFILE



Arithmetic Operator

+

-

*

/ Mod

ABS



AND OR IN NOT IN < > <= >= <> BETWEEN



Set Operators

UNION UNION ALL

INTERSECTION

MINUS



Thank You

Basic RDBMS Concepts

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