Introduction To Database Management System

Chanderprabhu Jain College of Higher Studies & School of Law
Plot No. OCF, Sector A-8, Narela, New Delhi – 110040
(Affiliated to Guru Gobind Singh Indraprastha University and Approved by Govt of NCT of Delhi & Bar Council of India)
Semester: SECOND Semester
Name of the Subject:
DATABASE MANAGEMENT SYSTEM
INTRODUCTION TO DBMS
UNIT-I

Introduction to DBMS
• Purpose of Database Systems
• View of Data
• Data Models
• Data Definition Language
• Data Manipulation Language
• Transaction Management
• Storage Management
• Database Administrator
• Database Users
• Overall System Structure

Unit 1
Introduction to DBMS
(Database Management Systems)
Application
program End-user
DBMS

Database Management System (DMBS)
• Collection of interrelated data
• Set of programs to access the data
• DMBS contains information about a particular enterprise
• DBMS provides an environment that it both convenient and efficient to
use

• Major components of a database system:
• Data: integrated and shared.
• Hardware: disk, CPU, Main Memory, ...
• Software: DBMS
• Users:
1. Application programmers
2. End users
3. Database administrator (DBA)
» Defining external schema
» Defining conceptual schema
» Defining internal schema
» Liaison with users
» Defining security and integrity checks
» Defining backup and recovery procedures

Purpose of Database Systems
Database management systems were developed to handle the
following difficulties of typical file-processing systems
supported by conventional operating systems:
• Data redundancy and inconsistency
• Difficulty in accessing data
• Data isolation – multiple files and formats
• Integrity problems
• Atomicity of updates
• Concurrent access by multiple users
• Security problems

Why Database ?
• Redundancy can be reduced
• Inconsistency can be avoided
• The data can be shared
• Standards can be enforced
• Security restrictions can be applied
• Integrity can be maintained
• Provision of data independence

Functions of the DBMS
• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Data Security and Integrity
• Data Recovery and Concurrency
• Data Dictionary
• Performance

Levels 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
name: string;
street: string;
city: integer;
end;
• View level: application programs hide details of data types. Views can
also hide information (e.g. salary) for security purposes.

Instances and Schemas
• Similar to types and variables in programming languages
• Schema – the logical structure of the database (e.g., set of customers
and accounts and the relationship between them)
• Instance – the actual content of the database at a particular point in
time

Data Independence
• Ability to modify a schema definition in one level without affecting a
schema definition in the other levels.
• The interfaces between the various levels and components should be
well defined so that changes in some parts do not seriously influence
others.
• Two levels of data independence
– Physical data independence
– Logical data independence

Data Models
• A collection of tools for describing:
– Data
– Data relationships
– Data semantics
– Data constraints
• Object-based logical models
– Entity-relationship model
– Object-oriented model
– Semantic model
– Functional model
• Record-based logical models
– Relational model (e.g., SQL/DS, DB2)
– Network model

Distributed Databases
Database Distributed System
• Distributed database is a database that is not stored in its entirety at a
single physical location, but rather is spread across a network of
computer.

Distributed Databases
• Advantages:
– efficiency of local processing
– data sharing
• Disadvantages:
– communication overhead
– implementation difficulties

Data Definition Language (DDL)
• Specification notation for defining the database schema
• DDL compiler generates a set of tables stored in a data dictionary
• Data dictionary contains metadata (data about data)
• Data storage and definition language – special type of DDL in which
the storage structure and access methods used by the database system
are specified.

Data Manipulation Language (DML)
• Language for accessing and manipulating the data organized by the
appropriate data model
• Two classes of languages
– Procedural – user specifies what data is required and how to get
those data
– Nonprocedural – user specifies what data is required without
specifying how to get those data

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.

Storage Management
• A 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 for the following tasks:
– Interaction with the file manager
– Efficient storing, retrieving, and updating of data

Database Administrator
• Coordinates all the activities of the database system; the database
administrator has a good understanding of the enterprise’s information
resources and needs:
• Database administrator’s duties include:
– Schema definition
– Storage structure and access method definition
– Schema and physical organization modification
– Granting user authority to access the database
– Specifying integrity constraints
– Acting as liaison with users
– Monitoring performance and responding to changes in requirements

Semester: Second Semester
DATABASE MANAGEMENT SYSTEM
INTRODUCTION TO SQL
UNIT-II

History of SQL
• SQL: Structured Query Language
• SQL is based on the relational tuple calculus
• SEQUEL: Structured English QUEry Language; part of SYSTEM R, 1974
• SQL/86: ANSI & ISO standard
• SQL/89: ANSI & ISO standard
• SQL/92 or SQL2: ANSI & ISO standard
• SQL3: in the works.

SQL
SQL consists of the following parts:
• Data Definition Language (DDL)
• Interactive Data Manipulation Language (Interactive DML)
• Embedded Data Manipulation Language (Embedded DML)
• Views
• Integrity

Objectives
• Explore basic commands and functions of SQL
• How to use SQL for data administration (to create tables, indexes, and
views)
• How to use SQL for data manipulation (to add, modify, delete, and retrieve
data)
• How to use SQL to query a database to extract useful information

Introduction to SQL
• SQL functions fit into two broad categories:
– Data definition language
• SQL includes commands to:
– Create database objects, such as tables, indexes, and views
– Define access rights to those database objects
– Data manipulation language
• Includes commands to insert, update, delete, and retrieve data
within database tables

• SQL is relatively easy to learn
• Basic command set has vocabulary of less than 100 words
• Nonprocedural language
• American National Standards Institute (ANSI) prescribes a standard SQL
• Several SQL dialects exist

Introduction to SQL

Introduction to SQL (continued)

Data Definition Commands
• Examine simple database model and database tables that will form basis for
many SQL examples
• Understand data environment

The Database Model

Creating the Database
• Following two tasks must be completed:
– Create database structure
– Create tables that will hold end-user data
• First task:
– RDBMS creates physical files that will hold database
– Tends to differ substantially from one RDBMS to another

The Database Schema
• Authentication
– Process through which DBMS verifies that only registered users
are able to access database
– Log on to RDBMS using user ID and password created by
database administrator
• Schema
– Group of database objects—such as tables and indexes—that are
related to each other

Data Types
• Data type selection is usually dictated by nature of data and by intended use
• Pay close attention to expected use of attributes for sorting and data
retrieval purposes

Data Types (continued)

Creating Table Structures
• Use one line per column (attribute) definition
• Use spaces to line up attribute characteristics and constraints
• Table and attribute names are capitalized
• NOT NULL specification
• UNIQUE specification

SQL Constraints
• NOT NULL constraint
– Ensures that column does not accept nulls
• UNIQUE constraint
– Ensures that all values in column are unique
• DEFAULT constraint
– Assigns value to attribute when a new row is added to table
• CHECK constraint
– Validates data when attribute value is entered

SQL Indexes
• When primary key is declared, DBMS automatically creates unique index
• Often need additional indexes
• Using CREATE INDEX command, SQL indexes can be created on basis of
any selected attribute
• Composite index
– Index based on two or more attributes
– Often used to prevent data duplication

Summary
• Many SQL constraints can be used with columns.
• The column list represents one or more column names separated by
commas.
• WHERE clause can be used with SELECT, UPDATE, and DELETE
statements to restrict rows affected by the DDL command.

DBMS
RELATIONAL DATA MODEL
UNIT-III

Relational Data Model
• In the relational data model the database is represented as a group of
related tables.
• The relational data model was introduced in 1970 by E. F. Codd of IBM
published a paper in CACM entitled "A Relational Model of Data for
Large Shared Data Banks".
• It is currently the most popular model. The mathematical simplicity and
ease of visualization of the relational data model have contributed to its
success.

Definitions of Terminology
Formal relational term Informal equivalents
relation table
tuple row or record
cardinality number of rows
attribute column or field
degree number of columns
(unique) identifier Primary key
domain pool of legal values

Characteristics of a Relation (table)
• Two-dimensional structure with rows and columns
• A relation represent a single entity
• Each table must have an attribute to uniquely identify each row
• Column values all have same data type
• Order of the rows and columns is immaterial to the DBMS

Properties of a Relation
• Based on the set theory
• 1. There are no duplicate tuples (rows).
– The body of the relation is a mathematical set (i.e., a set of
tuples), and sets in mathematics by definition do not include
duplicate elements.
– If a "relation" contains duplicate tuples, then it is not a relation.

• 2. Tuples (rows) are unordered (top to bottom).
– Sets in mathematics are not ordered. So, even if a relation A's
tuples are reversely ordered, it is still the same relation.
– Thus, there is no such thing as "the 5th tuple" or the last tuple.
In other words, there is no concept of positional addressing.

4. All attribute values are atomic.
– At every row-and-column position within the table, there always
exists precisely one value, never a list of values. Or equivalently,
relations do not contain repeating groups.
– A relation satisfying this condition is said to be in First Normal Form.

Primary Key
• A PK is an attribute, or collection of attributes, whose values
uniquely identify each tuple in a relation.
• To being unique, a PK must be minimal (contain no
unnecessary attributes)
• and must not change in value.

Entity Integrity Rule
• Guarantees that each entity will have a unique identity and ensures that
foreign key values can properly reference primary key values.
• Requirement
– No component of the primary key is allowed to accept nulls.
– By "null" here, we mean that information is missing for some reason.

Foreign Key
• An attribute in one table whose values must either match the
primary key in another table or be null.
• Attribute FK of base relation R2 is a foreign key if and only if it
satisfies the following two time-independent properties:
– Each value of FK is either wholly null or wholly non-null.
– Each non-null value of FK is identical to the value of PK in
some tuple of R1.

Referential Integrity Rule
• The database must not contain any unmatched foreign key values.
• Just as primary key values represent entity identifiers, so foreign key
values represent entity references.
• The referential integrity rule simply says that if B references A, then
A must exist.

Select Operation
• Notation:  p(r) lowercase Greek sigma 
• p is called the selection predicate
• Defined as:
p(r) = {t | t  r and p(t)}
Where p is a formula in propositional calculus consisting of terms connected by :
 (and),  (or),  (not)
Each term is one of:
<attribute> op <attribute> or <constant>
where op is one of: =, , >, . <. 

Union Operation
• Notation: r  s
• Defined as:
r  s = {t | t  r or t  s}
For r  s to be valid.
1. r, s must have the same arity (same number of attributes)
2. The attribute domains must be compatible (e.g., 2nd column
of r deals with the same type of values as does the 2nd
column of s).
Although the field types must be the same, the names can be different. For example
I can union professor and lecturer where:

The Data Normalization Process
• A methodology for organizing attributes into tables so that
redundancy among the nonkey attributes is eliminated.
• The output of the data normalization process is a properly structured
relational database.

The Data Normalization Technique
• Input:
– all the attributes that must be incorporated into the database
– a list of all the defining associations between the attributes (i.e.,
the functional dependencies).
• a means of expressing that the value of one particular
attribute is associated with a single, specific value of another
attribute.
• If we know that one of these attributes has a particular value,
then the other attribute must have some other value.

Full Functional dependency:
• If A and B are attributes(columns)of a table, B is fully functionally dependent
on A if B is functionally dependent on A, but not on any proper subset of A.
SalesPerson#--SalesPersonName
•Partial Functional Dependency:
• If A and B are attributes of a table, B is partially dependent on A if there is
some attribute that can be removed from A and yet the dependency still holds.
SP#, SPName -------> Comm%
•Transitive Functional Dependency:
• A , B and C are attributes of a table. If A is functionally dependent on B, and B
is functionally dependent on C, then C is Transitively dependent on A via B.
.

Logical Design of Relational Database Systems
• The conversion of E-R diagrams into relational tables.
• The data normalization technique.
• The use of the data normalization technique to test the tables resulting from the E-
R diagram conversions.

Second Normal Form
A Table is said to be in 2NF if it is in 1NF and there are no partial
dependencies
No Partial Functional Dependencies
Every non primary key attribute of the table must be fully functionally
dependent on the entire primary key of that table.
A non-key attribute cannot depend on only part of the key.

Third Normal Form
• A Table that is in 1NF and 2NF and in which no non primary key attribute is
transitively dependent on the primary key.
• Does not allow transitive dependencies in which one nonkey attribute is
functionally dependent on another.
• Nonkey attributes are not allowed to define other nonkey attributes.
• "Each attribute must be a fact about the key, the whole key, and nothing but the
key."

Boyce-codd Normal Form (BCNF)
• A Table is in BCNF if and only if every determinant (i.e., the attribute or a
group of attributes on which some other attribute is fully functionally
dependent) is a candidate key. BCNF is a stronger form of 3NF.
• The difference between 3NF and BCNF is that for a Functional dependency
A--->B, 3NF allows this dependency in a table if attribute B is a primary key
attribute and attribute A is not a candidate key, where as BCNF insists that
for this dependency to remain in a table, attribute A must be a candidate key.

DBMS
TRANSACTION PROCESSING AND
CONCURRENCY CONTROL
UNIT-IV

Transaction
• Any action that reads from and/or writes to a database may consist of:
– Simple SELECT statement to generate list of table contents
– Series of related UPDATE statements to change values of attributes in
various tables
– Series of INSERT statements to add rows to one or more tables
– Combination of SELECT, UPDATE, and INSERT statements

• Transaction is logical unit of work that must be either entirely completed
or aborted
• Successful transaction changes database from one consistent state to
another
– One in which all data integrity constraints are satisfied
• Most real-world database transactions are formed by two or more
database requests
– Equivalent of a single SQL statement in an application program or
transaction

Evaluating Transaction Results
• Not all transactions update database
• SQL code represents a transaction because database was accessed
• Improper or incomplete transactions can have devastating effect on
database integrity
– Some DBMSs provide means by which user can define enforceable
constraints
– Other integrity rules are enforced automatically by the DBMS

The ACID Properties
• Atomicity
– Requires that all operations (SQL requests) of a transaction be
completed
• Consistency
– Indicates the permanence of database’s consistent state

• Isolation
– Data used during execution of a transaction cannot be used by
second transaction until first one is completed
• Durability
– Indicates permanence of database’s consistent state Isolation

Transaction Management with SQL
• ANSI standards require that, when a transaction sequence is initiated by a user
or an application program, it must continue through all succeeding SQL
statements until one of four events occurs
– COMMIT statement is reached
– ROLLBACK statement is reached
– Program is abnormally terminated

Concurrency Control
• Coordination of simultaneous transaction execution in a
multiprocessing database system
• Objective is to ensure serializability of transactions in a multiuser
database environment

Scheduling Algorithms
• Modify concurrency control schemes for use in distributed environment.
There are 3 basic methods for transaction concurrency control.
• Locking (two phase locking - 2PL).
• Timestamp ordering
• Optimistic
• Hybrid

Locking Protocols
• Majority Protocol
• Local lock manager at each site administers lock and unlock requests for
data items stored at that site.
• When a transaction wishes to lock an un replicated data item Q residing
at site Si, a message is sent to Si ‘s lock manager.
• If Q is locked in an incompatible mode, then the request is delayed
until it can be granted.
• When the lock request can be granted, the lock manager sends a
message back to the initiator indicating that the lock request has been
granted.

Timestamp Ordering
• Timestamp (TS): a number associated with each transaction
– Not necessarily real time
• Can be assigned by a logical counter
– Unique for each transaction
– Should be assigned in an increasing order for each new transaction

Concurrency Control
with Locking Methods
• Lock
– Guarantees exclusive use of a data item to a current transaction
– Required to prevent another transaction from reading inconsistent
data
• Lock manager
– Responsible for assigning and policing the locks used by
transactions

Lock Granularity
• Indicates level of lock use
• Locking can take place at following levels:
– Database
– Table
– Page
– Row
– Field (attribute)

Lock Types
• Binary lock
– Has only two states: locked (1) or unlocked (0)
• Exclusive lock
– Access is specifically reserved for transaction that locked object
– Must be used when potential for conflict exists
• Shared lock
– Concurrent transactions are granted Read access on basis of a
common lock

Serializability Challenges
Given a set of transactions that execute concurrently, the goal is:
• to find a schedule equivalent to some serial order
• maximize throughput and concurrency
Thus, exclude the trivial solution of running transactions one at a time.
But, theory is not perfect:
• given an arbitrary mix of reads and writes from different transactions,
finding out all possible serializable orders is NP-complete.

Serializability
Given all the reads and writes from all active transactions, a scheduling
of these operations is serializable if the schedule produces the same
effect on the database as some serial execution of the same transactions.
Why does it help?
By definition, a serial execution of transactions does not have any
concurrency control problem, since each transaction executes to
completion before the next one is allowed to start
If we can find a serializable schedule, then the isolation property is
satisfied.

Concurrency Control
with Time Stamping Methods
• Assigns global unique time stamp to each transaction
• Produces explicit order in which transactions are submitted to DBMS
• Uniqueness
– Ensures that no equal time stamp values can exist
• Monotonicity
– Ensures that time stamp values always increase

Wait/Die and Wound/Wait Schemes
• Wait/die
– Older transaction waits and younger is rolled back and
rescheduled
• Wound/wait
– Older transaction rolls back younger transaction and
reschedules it

Lock-Based Concurrency Control
• A DBMS ensures that only serializable schedules are allowed by using a
suitable CC protocol.
• Strict Two-phase Locking (Strict 2PL) Protocol
• Strict 2PL does however allow deadlocks, i.e. cycles of transactions
waiting for locks to be released. A DBMS must either prevent or detect
(and resolve) deadlocks.

Summary
• Concurrency control and recovery are among the most important functions
provided by a DBMS.
• Users need not worry about concurrency.
– System automatically inserts lock/unlock requests and schedules actions
of different Xacts in such a way as to ensure that the resulting execution
is equivalent to executing the Xacts one after the other in some order.
• Write-ahead logging (WAL) is used to undo the actions of aborted
transactions and to restore the system to a consistent state after a crash.
– Consistent state: Only the effects of commited Xacts seen.

Introduction To Database Management System

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