Unit 1 DBMS

CS8492 – Database Management
System
Ms.S.DHIVYA
AP/CSE
KNCET

Objectives
• To learn the fundamentals of data models and to represent a database
system using ER diagrams.
• To study SQL and relational database design.
• To understand the internal storage structures using different file and
indexing techniques which will help in physical DB design.
• To understand the fundamental concepts of transaction processing-
concurrency control techniques and recovery procedures.
• To have an introductory knowledge about the Storage and Query
processing Techniques

UNIT I - RELATIONAL DATABASES
Purpose of Database System – Views of data –
Data Models – Database System Architecture –
Introduction to relational databases – Relational
Model – Keys – Relational Algebra – SQL
fundamentals – Advanced SQL features –
Embedded SQL– Dynamic SQL

Database Management Systems
Definition:
“A Collection of interrelated data and a set
of programs to access those data”

Goal:
A way to store and retrieve database
information that is convenient and efficient.

Some examples of DBMS
• MySQL
• Oracle
• SQL Server
• IBM DB2
• PostgreSQL
• Amazon Simple DB (cloud based)
• MongoDB

Tasks:
 Store data or Visualize data
 Access data
 Querying data
 Update or manipulate data

Applications
 Banking
 Universities
 Reservation Systems
 Telecommunication

Applications
 Manufacturing
 Accounting
 Credit card transactions

Disadvantages of File Processing System
 Data redundancy
 Data inconsistency
 Data isolation

Disadvantages of File Processing System
 Data Integrity
 Atomicity
 Concurrency
 Security

Characteristics
 Systematic Management of information
 Representing the data by multiple views
 It maintains data for some specific purpose

Advantages
 remove the redundancy
 retrieve the desired data
 accessed efficiently
 Data integrity maintained

Disadvantages:
 Complex design
 Hardware and software cost
 Damaged part
 Conversion Cost
 Training

Views of Data
Abstraction is one of the main features of
database systems.
 Hiding irrelevant details from user and
providing abstract view of data to users, helps
in easy and efficient user-database interaction

Views of Data
Data Abstraction
 To ease the user interaction with database, the
developers hide internal irrelevant details from
users.

Views of Data - Data Abstraction

Views of Data - Data Abstraction
Physical level:
 how data is actually stored in database
Logical level:
 what data is stored in database and what relationships exist
among those data
View level:
 the user interaction with database system.

Schema and Instances
Schema:
 Design of a database
 only a structural view(design) of a database.
 The variable declaration
 The schema for student table
Reg. No Name Marks

Types of Schema:
 Physical Schema:
 how the data stored in blocks of storage
 Logical schema:
 data can be described as certain types of data records gets
stored in data structures.
 View schema or Subschema:
 end user interaction with database systems

Instance:
 The data stored in database may gets
changed while inserting or deleting.
The collection of information at particular
moment is called instance.
 The schema for student table
Reg. No Name Marks
1 AAA 410
2 BBB 446
3 CCC 480

Database Languages
 used to read, update and store data in a
database.
 Types:
 Data Definition Language (DDL)
 Data Manipulation Language (DML)
 Data Control language (DCL)
 Transaction Control Language(TCL)

Data Definition Language (DDL):
 Specifying the database schema by a set of
definitions
 operations:
 To create the database instance – CREATE
To alter the structure of database – ALTER
To drop database instances – DROP

To delete tables in a database instance –
TRUNCATE
To rename database instances – RENAME
To drop objects from database such as tables –
DROP
To Comment – Comment

Data Manipulation Language (DML)
accessing and manipulating data in a database
 operations:
To read records from table(s) – SELECT
To insert record(s) into the table(s) – INSERT
Update the data in table(s) – UPDATE
Delete all the records from the table – DELETE

Types
• 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 and without specifying
how to get those data.

Data Control language (DCL)
 Granting and revoking user access on a database
 operations:
To grant access to user – GRANT
To revoke access from user – REVOKE

Transaction Control Language(TCL)
The changes in the database that we made using DML
commands are either performed or rollbacked using
TCL
 operations:
To persist the changes made by DML commands in
database – COMMIT
To rollback the changes made to the database –
ROLLBACK

Data Models
a logical structure of Database
 describes the design of database to reflect
entities, attributes, relationship among data,
constrains etc.
 Types:
 Relational, ER Model, Object based Model and
Semi-structured Model, hierarchical model

Data Models
 Relational Model:
 the data and relationships are represented by
collection of inter-related tables.
 Table - relation
 Each table is a group of column and rows, where
column represents attribute of an entity and rows
represents records.

Data Models
 Relational Model – Example:
 Reg. No, Name, Marks – Attributes-(Columns)
 Values in rows - Records
Reg. No Name Marks
1 AAA 90
2 BBB 100

Data Models
 Relational Model – Advantages:
 Structural Independence
 Conceptual Simplicity
 Query Capability
 Easy design, maintenance and usage

Data Models
 Relational Model – Disadvantages:
 requires powerful hardware and large data storage
devices
 slower processing time

Data Models
 Entity Relationship Model:
 the notion of real-world entities and relationships
among them.
 based on −
Entities and their attributes.
Relationships among entities.

Data Models

Data Models
 Entity:
 An entity in an ER Model is a real-world entity
having properties called attributes.
 Relationship:
 The logical association among entities.
 mapped with entities

Data Models
 Entity Relationship Model – Advantages:
 Simple
 Easy to understand
 Effective
 Integrated
 Easy conversion

Data Models
 Entity Relationship Model – Disadvantages:
 Loss of information
 Limited relationship
 No industry standard

Data Models
Object based Data Model:
 The object oriented languages like C++, Java, C#
are becoming dominant in software development.
 combines object oriented features with relational
model.

Data Models
Object based Data Model:

Data Models
Object based Data Model - Advantages:
 Enriched modeling
 Reusability
 Support for schema evolution
 Improved Performance

Data Models
Object based Data Model - Disadvantages:
 Lack of universal data model
 Lack of experience

Data Models
 Semi-structured data model
 permits specification of data where individual data
items of same type may have different sets of
attributes.
Ex: Extensible Markup Language

Data base architecture
• Two tier architecture.
• Three tier architecture

Database System Architecture
 Top Parts:
 Users – Application interfaces
 Application programs – Application programmers
 Sophisticated Users – Query tools
 Database Administrator – Administrator tools

 Query Processor:
 helps the database system to simplify and facilitate
access to data.
 DDL interpreter – a translator which interprets the
DDL statements in data dictionaries.
 DML compiler – translates DML statements query
language into an evaluation plan.

 Query evaluation engine: executed the low level
instructions generated by the DML Compiler.
 An execution plan is a blueprint for evaluating a
query. It is evaluated by query evaluation engine.

 Storage Manager:
 The component of database system that provides
interface between the low level data stored in the
database and the application programs and queries
submitted to the system.
 Responsible for storing, retrieving and updating data
in the database.

 Storage Manager Components:
 Authorization and integrity manager:
 Validates the users who want to access the data and tests for
integrity constraints.
 Transaction Manager:
 Ensures that the database remains in consistent despite
of system failures and concurrent transaction execution
proceeds without conflicting.

File Manager:
 Manages allocation of space on disk storage and
representation of the information on disk.
 Buffer Management:
 Manages the fetching of data from disk storage
into main memory. It also decides what data to
cache in main memory.

Data Independence
 To modify the schema at one level of the database
system without altering the schema at the next higher
level.
 Physical Independence:
 to change the physical data without impacting the schema
or logical data.
 For example, to replace hard-disks with SSD − it should
not have any impact on the logical data or schemas.

Data Independence
 Logical Independence:
 it stores information about how data is managed
inside.
 Example: If we do some changes on table format,
it should not change the data residing on the disk.

Relational DBMS
• Relation / tables
• Tuple / record / row
• Field / column
• Domain
• Constraint
• Data type
• Primary key
• Foreign key
• Stored procedure
• Indices
• Relational operations
• Normalization

Relational Databases
 Collection of tables having unique names.
RollNo Name Phone
001 AAA 111
002 BBB 222
003 CCC 333
CourseID CourseName Credits
101 Mech 4
102 CSE 3
103 IT 5

 Collection of tables having unique names.
RollNo CourseID
001 101
002 102
003 103

 Table or relation:
 a collection of data represented in rows and columns
 cannot have duplicate data or rows.
Roll No Name Marks Phone
001 AAA 88 111
002 BBB 83 222
003 CCC 98 333
004 DDD 67 444

 Tuple or record or row
 Each row of a table is known as record
 a set of related data
004 DDD 67 444

 Attribute or columns:
 Each record can be broken down into several small parts of
data.
 Ex: RollNo, Sname, Marks, Phone
 Relation Schema and Instance:
 Schema: structure of the relation
 Instance: a specific set of rows

 Domain:
 a set of permitted values for an attribute in table.
 Example: a set of all possible marks of the students.
 Domain of marks (88,83,98)
Atomic:
 elements of the domain are considered to be indivisible
units.

 NULL attribute:
 a special symbol, independent of data type, either unknown
or inapplicable.
 It does not mean zero or blank.
Emp# Job Name Salary
E10 Sales 12500
E12 Null 25000

Degree:
Total number of columns present in the relational
database
Cardinality:
Total number of rows present in the relational
database
In the above table, degree is 3 and cardinality is 3.
Emp# Job Name Salary
E10 Sales 12500
E12 Null 25000

Keys
 used for identifying unique rows from table
 keys are used to establishes relationship
among tables.
Types:
Super key, candidate keys, primary keys,
foreign keys, composite keys.

Why we need Keys?
• In a real world, in many applications huge data
has to store in tables.
• Tables extends to thousands of records stored
in them, unsorted, and unorganized
• To avoid duplication of data and to easily
identify the data's.

Super key:
 a set of attributes within a table that can uniquely
identify each record in a table.
Student:
 Super Key: student_id, (student_id, name), phone
student_id name phone age marks
1 A 9876723452 17 89
2 Aa 9991165674 19 95
3 B 7898756543 18 55
4 C 8987867898 19 68
5 D 9990080080 17 89

 Candidate Key:
A super key without redundancy. (should not allow duplication)
the minimal set of fields which can uniquely identify each record
in a table
 Example: student_id, phone
 A candidate key can be NULL. And its value should be unique.
There can be more than one candidate keys for a table.
A candidate key can be a combination of more than one
columns(attributes).

 Primary Key
 a key that can uniquely identify each record in a table.
Each table must have a primary key.
Primary key should not be NULL.
 Example: student_id
 Foreign key
 it is an attribute which establish relationship between
another table.
to point to the primary key of another table.
It helps in mantaining data integrity for tables in
relationship.

Composite key
• Any key with more than one attribute is called
as composite key
• Ex:
(s_id ,phone), (name,phone), (s-id,name)

Integrity Constraints
 Integrity constraints are a set of rules. It is used to
maintain the quality of information and checks
whether the table is valid or not.
 Types:
 Domain Constraints
 Entity Integrity Constraints
 Referential Integrity Constraints
 Key Constraints or entity

 a valid set of values for an attribute.
 The data type of domain includes string, character, integer,
time, date, currency, etc. The value of the attribute must be
available in the corresponding domain.

 A – not allowed, it is an integer attribute
ID Name Semester Age
1 A I 17
2 B II 16
3 C III A

 Entity integrity constraints
 Primary key value can't be null.
Emp_Id Emp_Name Salary
111 AAA 10000
222 BBB 20000
333 CCC 30000
NULL DDD 40000

 A referential integrity constraint is specified between two
tables.
 if a foreign key in Table 1 refers to the Primary Key of
Table 2, then every value of the Foreign Key in Table 1
must be null or be available in Table 2.

 Key Constraints
 Keys are the entity set that is used to identify an entity
within its entity set uniquely.

Relational Algebra
 A procedural language which is used to access database
to read tables to read data in different ways.
 denoted using operators.
 each relational query describes a step by step procedure
for computing the desired answer, based on the order in
which operators are applied in the query.

Relational Algebra
 Operations:
 Selection
 projection
 Cartesian product
 Set Operations
Join

Relational Algebra
 Selection:
 to fetch rows or tuple from the table.
 syntax:
σpredicate(relation)
 predicate – logic using which the data from the relation is
selected.

Relational Algebra
 Ex: Student - Table
σage>15(Student)
Reg. No Sname Age
1 AAA 15
2 BBB 18
3 CCC 16
Sname
BBB
CCC

Relational Algebra
 Projection:
 to project only a certain set of attributes of a
relation.
 remove duplicate data from the columns.
 Syntax: ΠA1,A2,…(r)

Relational Algebra
 Projection: Ex: Student - Table
Πage(Student)
Reg. No Sname Age
1 AAA 15
2 BBB 18
3 CCC 16

Relational Algebra
 Cartesian product:
 to combine data from two different relations into
one and fetch data from the combined relation
Syntax: A x B

Relational Algebra
 Cartesian product: Example
Syntax: A1 x B1
Table A1
A B
1 1
2 2
Table B1
C D E
1 2 7
2 6 8
5 7 9
A1 x B1
A B C D E
1 1 1 2 7
1 1 2 6 8
1 1 5 7 9
2 2 1 2 7
2 2 2 6 8
2 2 5 7 9

• Set Operations
Union
Intersection
Set difference
Relational Algebra

Relational Algebra
 Union:
 to fetch data from two relations or temporary relation.
 the relations specified should have same number of
attributes and same attribute domain.
 duplicated tuples are automatically eliminated from
the result.
 Syntax: A ᴜ B

Relational Algebra
 Union: Example
Syntax: A1 ᴜ B1
Table A1
A B
1 1
1 2
2 1
Table B1
A B
1 2
2 3
A1 ᴜ B1
A B
1 1
1 2
2 1
2 3

Relational Algebra – Additional Operators
 Intersection:
 to fetch data from both tables which is common in
both the tables.
Syntax: A ∩ B

Relational Algebra
 Intersection: Example
Syntax: A1 ∩ B1
Table A1
Name id
aaa 1
bbb 2
ccc 1
Table B1
Name course
aaa C
ccc C++
A1 ∩ B1
Name
aaa
ccc

Relational Algebra
 Set-Difference:
 the result of set difference is tuples, which are
present in one relation but are not in the second
relation.
Syntax: A - B

Relational Algebra
 Set-Difference: Example
Syntax: A1 - B1
Table A1
A B
1 1
1 2
2 1
Table B1
A B
1 2
2 3
A1 - B1
A B
1 1
2 1

Relational Algebra
 Rename Operation:
 to rename the output relation for any query
relation which returns the result.
Syntax: ρ (RelationNew, RelationOld)

Relational Algebra
 Rename: Example
ρ (B1,A1)
Table A1
A B
1 1
2 2
Table B1
A B
1 1
2 2

 Join:
 to fetch data from both tables which is common in
both the tables.
Operator: *
https://www.tutorialspoint.com/sql/sql-inner-
joins.htm#:~:text=They%20are%20also%20referred%
20to,which%20satisfy%20the%20join%2Dpredicate.

Relational Algebra
 Natural Join: Example
Syntax: A1* B1
Table A1
A B
1 1
1 2
2 1
Table B1
A C
1 2
2 3
A1 * B1
A B C
1 1 2
1 2 2
2 1 3

 Division:
 to evaluate queries which contain the keyword
ALL.
 denoted by A / B.

Table A1
A B C D
1 5 2 7
1 5 3 7
1 6 3 7
2 6 2 7
2 6 3 7
3 6 2 7
3 6 3 7
3 5 3 7
Table B1
C D
2 7
3 7
A1 / B1
A B
1 5
2 6
3 6

SQL Fundamentals
 Structured Query Language is a database
query language used for storing and managing
data in Relational DBMS.

SQL Fundamentals
 Data types used in SQL:
 char(n) – fixed length character string, the storage size
of the char value is equal to the maximum size for this
column.
 varchar(n) - a variable-length data type, the storage
size of the varchar value is the actual length of the data
entered, not the maximum size for this column

SQL Fundamentals
 Data types used in SQL:
 int: store integer values without precision.
 numeric: a fixed point number with user defined
precision.
 Ex: numeric(3,2) – 333.11

SQL Fundamentals
 DDL Commands:
 to define the database schema.
 deals with descriptions of the database schema and is used to create and
modify the structure of the database objects in database.
CREATE
ALTER
RENAME
TRUNCATE
DROP

SQL Fundamentals
 DDL Commands – create:
 to create the database or its objects such as table, function, views
and so on.
Syntax: CREATE TABLE <TABLE_NAME>
( column_name1 datatype1,
column_name2 datatype2,
column_name3 datatype3,
column_name4 datatype4
);

SQL Fundamentals
 DDL Commands – create:
 to create the database or its objects such as table, function,
views and so on.
Example: CREATE TABLE Student( student_id INT,
name VARCHAR(100), age INT);

SQL Fundamentals
 DDL Commands – alter:
 used for altering the table structure, such as,
to add a column to existing table
to rename any existing column
to change datatype of any column or to modify its
size.
to drop a column from the table.

SQL Fundamentals
 Syntax: ALTER TABLE table_name ADD(
column_name datatype);
 Example: ALTER TABLE student ADD(
address VARCHAR(200) );

SQL Fundamentals
 Syntax: ALTER TABLE table_name modify (
column_name datatype);
 Example: ALTER TABLE student MODIFY (
address VARCHAR(300) );

SQL Fundamentals
 Syntax: ALTER TABLE table_name
RENAME old_column_name TO
new_column_name;
 Example: ALTER TABLE student RENAME
address TO location;

SQL Fundamentals
 DDL Commands – Truncate:
 removes all the records from a table.
 will not destroy the table's structure
 Syntax: TRUNCATE TABLE table_name
 Example: TRUNCATE TABLE student;

SQL Fundamentals
 DDL Commands – DROP :
 completely removes a table from the database.
 will also destroy the table structure and the data
stored in it.
 Syntax: DROP TABLE table_name
 Example: DROP TABLE student;

SQL Fundamentals
 DDL Commands – RENAME:
 used to set a new name for any existing table.
 Syntax: RENAME TABLE old_table_name to
new_table_name
 Example: RENAME TABLE student to
students_info;

SQL Fundamentals
 Data Manipulation Language (DML)
 managing data in database
not auto-committed
 changes made by DML command are not
permanent to database, it can be rolled back.

SQL Fundamentals
DML Commands – INSERT:
to insert data into a table
 Syntax: INSERT INTO table_name
VALUES(data1, data2, ...)
 INSERT INTO student VALUES(101, 'Adam',
15);

SQL Fundamentals
DML Commands – UPDATE :
to update any record of data in a table
 Syntax: UPDATE table_name SET column_name
= new_value WHERE some_condition;
 UPDATE student SET age=18 WHERE
student_id=102;

SQL Fundamentals
DML Commands – DELETE:
used to delete data from a table.
 Syntax: DELETE FROM table_name;
 DELETE FROM student;
DELETE FROM student WHERE s_id=103;

SQL Fundamentals
SQL Command – WHERE:
to specify/apply any condition while retrieving,
updating or deleting data from a table.
 This clause is used mostly with SELECT, UPDATE
and DELETE query.
 Syntax: DELETE FROM table_name WHERE
[condition];
Example: DELETE FROM student WHERE s_id=103;

SQL Fundamentals
SQL Command – WHERE:
Example:
 SELECT s_id, name, age, address FROM student
WHERE s_id = 101;

SQL Fundamentals
string operations
SQL Command – LIKE:
used in the condition in SQL query with the
WHERE clause.
 LIKE clause compares data with an expression
using wildcard operators to match pattern given in
the condition.

SQL Fundamentals
Wildcard operators;
 Percent sign %: represents zero, one or more than
one character.
Underscore sign _: represents only a single
character.

SQL Fundamentals
 Example;
 SELECT * FROM Student WHERE s_name
LIKE 'A%';
 will return all records where s_name starts with
character 'A'.

Example;
LIKE „s%a„;
 will return all records where s_name starts with
character „s„ and end with „a‟.

SQL Fundamentals
 Example;
LIKE '_d%';
 will return all records from Student table where
s_name contain 'd' as second character.

SQL Fundamentals
SQL Command – ORDER BY:
used with SELECT statement for arranging
retrieved data in sorted order.
default sorts the retrieved data in ascending order.
 To sort the data in descending order DESC
keyword is used with Order by clause.

SQL Fundamentals
SQL Command – ORDER BY:
Syntax: SELECT column-list|* FROM table-name
ORDER BY ASC | DESC;
 Example: SELECT * FROM Emp ORDER BY
salary;

SQL Fundamentals
SQL Command – Group By:
to group the results of a SELECT query based on
one or more columns
 used with SQL functions to group the result from
one or more tables

SQL Fundamentals
Syntax:
 SELECT column_name, function(column_name)
FROM table_name WHERE condition GROUP
BY column_name
 SELECT name, age FROM Emp GROUP BY
salary

SQL Fundamentals
SELECT name, salary FROM Emp WHERE age >
25 GROUP BY salary

SQL Fundamentals
SQL Command –HAVING:
to give more precise condition for a statement.
 used to mention condition in Group by based SQL
queries, just like WHERE clause is used with
SELECT query

SQL Fundamentals
 Syntax:
 SELECT column_name, function(column_name)
FROM table_name WHERE column_name
condition GROUP BY column_name HAVING
function(column_name) condition

SQL Fundamentals
 Example:
 SELECT * FROM sale GROUP BY customer
HAVING sum(previous_balance) > 3000

SQL Fundamentals
SQL Command –DISTINCT :
to retrieve unique values from the table.
 Distinct removes all the duplicate records while
retrieving records from any table in the database.
 Synatx: SELECT DISTINCT column-name
FROM table-name;

SQL Fundamentals
SQL Command –DISTINCT :
Syntax: SELECT DISTINCT column-name
FROM table-name;
 Example: SELECT DISTINCT salary FROM
Emp;

SQL Fundamentals
SQL Command – AND:
 to set multiple conditions with the WHERE clause,
alongside, SELECT, UPDATE or DELETE SQL queries.
 Example:
 SELECT * FROM Emp WHERE salary < 10000 AND age
> 25

SQL Fundamentals
SQL Command – OR:
 to combine multiple conditions with WHERE clause.
 Difference:
 When we use AND to combine two or more than two conditions,
records satisfying all the specified conditions will be there in the
result.
 But in case of OR operator, atleast one condition from the
conditions specified must be satisfied by any record to be in the
resultset.

SQL Fundamentals
SQL Command – OR:
 SELECT * FROM Emp WHERE salary >
10000 OR age > 25
eid name age salary
401 Anu 22 5000
402 Shane 29 8000
403 Rohan 34 12000
404 Scott 44 10000

SQL Fundamentals
SQL Command – Aggregate Functions:
 return a single value after performing calculations on a
group of values
 AVG() Function:
 returns average value after calculating it from values in a
numeric column.
 SELECT avg(salary) from Emp;

SQL Fundamentals
 COUNT() Function:
 returns the number of rows present in the table either based
on some condition or without condition.
 SELECT COUNT(name) FROM Emp WHERE salary =
8000;
 SELECT COUNT(DISTINCT salary) FROM emp;

SQL Fundamentals
 MAX() Function:
 returns maximum value from selected column of the table.
 SELECT MAX(salary) FROM emp;
 MIN() Function:
 returns minimum value from a selected column of the table.
 SELECT MIN(salary) FROM emp;

SQL Fundamentals
 SUM() Function:
 returns total sum of a selected columns numeric values.
 SELECT SUM(salary) FROM emp;

SQL Fundamentals
SQL Command – SET Operations:
 UNION Operation:
to combine the results of two or more SELECT statements
 eliminate duplicate rows from its resultset.
 number of columns and datatype must be same in both the
tables, on which UNION operation is being applied.

SQL Fundamentals
 UNION Operation – Example:
 SELECT * FROM First UNION SELECT *
FROM Second;
ID Name
1 Abi
2 Ashok
ID Name
2 Ashok
3 Chandran

SQL Fundamentals
 INTERSECT Operation:
to combine two SELECT statements, but it only returns the
records which are common from both SELECT statements.
 the number of columns and datatype must be same.
 Example: SELECT * FROM First INTERSECT SELECT
* FROM Second;

SQL Fundamentals
SQL Command – JOIN:
 to fetch data from two or more tables, which is joined to
appear as single set of data.
 used for combining column from two or more tables by
using values common to both tables.
 Types: Inner, outer, Left and Full

SQL Fundamentals
SQL Command – JOIN – Inner:
 the result is based on matched data as per the equality
condition specified in the SQL query.
 Syntax;
 SELECT column-name-list FROM table-name1 INNER
JOIN table-name2 WHERE table-name1.column-name =
table-name2.column-name;

SQL Fundamentals
SQL Command – JOIN – Inner:
 Example: Table1: Class, Table2:Class_Info
SELECT * from class INNER JOIN class_info where class.id =
class_info.id;
ID Name
1 AAA
2 BBB
3 CCC
4 DDD
ID Address
1 XYZ
2 PQR
3 LMN

SQL Fundamentals
SQL Command – JOIN – Outer:
based on both matched and unmatched data
 Types:
 Left Outer Join
Right Outer Join
Full Outer Join

SQL Fundamentals
SQL Command – JOIN – Left Outer:
 returns a resultset table with the matched data from the two
tables and then the remaining rows of the left table and null
from the right table's columns.
 Syntax:
 SELECT column-name-list FROM table-name1 LEFT
OUTER JOIN table-name2 ON table-name1.column-name
= table-name2.column-name;

SQL Fundamentals
 SELECT * FROM class LEFT OUTER JOIN class_info ON
(class.id = class_info.id);
ID Name
1 AAA
2 BBB
3 CCC
4 DDD
5 EEE
ID Address
1 XYZ
2 PQR
3 LMN
7 DEF
8 GHI

SQL Fundamentals
 SELECT * FROM class LEFT OUTER JOIN class_info ON
 Result Set: ID Name ID Address
1 AAA 1 XYZ
2 BBB 2 PQR
3 CCC 3 LMN
4 DDD NULL NULL
5 EEE NULL NULL

SQL Fundamentals
SQL Command – JOIN – RIGHT Outer:
 returns a resultset table with the matched data from the two
tables and then the remaining rows of the right table and
null from the left table's columns.
 Syntax:
 SELECT column-name-list FROM table-name1 RIGHT

SQL Fundamentals
SQL Command – JOIN – Right Outer:
 SELECT * FROM class RIGHT OUTER JOIN class_info ON
ID Name
1 AAA
2 BBB
3 CCC
4 DDD
5 EEE
ID Address
1 XYZ
2 PQR
3 LMN
7 DEF
8 GHI

SQL Fundamentals
 SELECT * FROM class RIGHT OUTER JOIN class_info ON
 Result Set: ID Name ID Address
1 AAA 1 XYZ
2 BBB 2 PQR
3 CCC 3 LMN
NULL NULL 7 DEF
NULL NULL 8 GHI

SQL Fundamentals
SQL Command – JOIN – FULL Outer:
 returns a resultset table with the matched data of two table
then remaining rows of both left table and then the right
table.
 Syntax:
 SELECT column-name-list FROM table-name1 FULL

SQL Fundamentals
SQL Command – JOIN – Full Outer:
SELECT * FROM class FULL OUTER JOIN class_info ON
ID Name
1 AAA
2 BBB
3 CCC
4 DDD
5 EEE
ID Address
1 XYZ
2 PQR
3 LMN
7 DEF
8 GHI

SQL Fundamentals
SQL Command – JOIN – FULL Outer:
 SELECT * FROM class FULL OUTER JOIN class_info ON
 Result Set:
ID Name ID Address
1 AAA 1 XYZ
2 BBB 2 PQR
3 CCC 3 LMN
4 DDD NULL NULL
5 EEE NULL NULL
NULL NULL 7 DEF
NULL NULL 8 GHI

SQL Fundamentals
Nested Queries:
 a query is written inside a query
Types:
 Independent Nested Queries
 Co-related Nested Queries

SQL Fundamentals
Nested Queries - Independent Nested Queries:
query execution starts from innermost query to outermost
queries.
 The execution of inner query is independent of outer query,
but the result of inner query is used in execution of outer
query.
 Various operators like IN, NOT IN, ANY, ALL etc are used
in writing independent nested queries.

SQL Fundamentals
ID Name Age Address Salary
1 A 25 P 20000
2 B 45 Q 65000
3 C 35 R 37000
4 D 30 S 30000
5 E 31 T 30000
6 F 27 U 20000
7 G 42 V 60000

SQL Fundamentals
 Operator – IN – Example:
 SELECT * FROM CUSTOMERS WHERE ID IN (SELECT
ID FROM CUSTOMERS WHERE SALARY > 45000) ;

SQL Fundamentals
 Operator – NOT IN:– Example:
 Select S_ID from STUDENT where S_ID NOT IN
(Select S_ID from STUDENT_COURSE where C_ID IN
(SELECT C_ID from COURSE where C_NAME=‟DSA‟ or
C_NAME=‟DBMS‟));

SQL Fundamentals
Nested Queries - Co-related Nested Queries:
the output of inner query depends on the row which is being
currently executed in outer query.
 Select S_NAME from STUDENT S where EXISTS
(select * from STUDENT_COURSE SC where
S.S_ID=SC.S_ID and SC.C_ID=‟C1‟);

Advanced SQL Features
 Embedded SQL :
 The programming module in which the SQL statements are
embedded.
 to embed SQL statements inside the programming language such
as C, C++, JAVA and so on.
 allows the application languages to communicate with DB and
get requested result.
 The high level languages which supports embedding SQLs
within it – Host Language.

 Embedded SQL :
 The SQL statements and host language
statements make the source program which is fed to a SQL
precompiler for
processing the SQL statements.
 The host programming languages variables can be referenced
in the embedded SQL statements, which allows values
calculated by the programs
to be used by SQL statements.

 Embedded SQL :
 There are some special program variables which are
used to assign null values to database columns. These
program variables support the retrieval of null values
from the database.

 Embedded SQL Program Development:

 the embedded SQL source code is fed to the SQL
precompiler.
 The precompiler scans the program and processes
the embedded SQL statements present in the code.
 After processing the source code, the precompiler
produces 2 files as its output.

 The first file contains the source program without
embedded SQL statements,
The second file contains all the embedded SQL
statements used in the program

 The first file produced by precompiler is fed to the
compiler for the host programming language (like
C compiler). The compiler processes the source
code and produces object code as
its output.

 Now the linker takes the object modules produced
by the compiler and link them with various library
routines and produces an executable
program.

 The database request modules, produced by the
precompiler (in steps) are submitted to a special BIND
program.
 The BIND program examines
the SQL statements, parse them, validates them,
optimizes them and finally
produces an application plan for each statement.

 The result is a combined
application plan for the entire program, that represents
a DBMS-executable version of its embedded SQL
statements.
 The BIND program stores the plan in the
database, usually assigning it the name of the
application program that has created it.

 An Embedded SQL Example in C:
main(){
exec sql include sqlca;
exec sql declare table teacher (tid char(6) not null, tname char(20),
age number(3), salary number(7))
printf("updating teacher salary who are B.Tech");
exec sql update teacher set salary=1.1*salary where tid=„01';
printf(update done");
exit(); }

 Dynamic SQL:
 a programming technique which allows to build the
SQL statements dynamically at runtime.
 are not embedded in the source program but stored as
strings of characters that are manipulated during a
program‟s runtime.
 SQL statements are either entered by a programmer or
automatically generated by the program.

 Dynamic SQL:
 facilitates automatic generation and manipulation
of program modules for efficient automated
repeating task preparation and performance.
 facilitates the development of powerful
applications with the ability to create database
objects for manipulation according to user input.

 Dynamic SQL:
int main(){
EXEC SQL INCLUDE SQLCA;
EXEC SQL BEGIN DECLARE SECTION
int Reg_No;
char name[10][10];
float marks;
char grade;

EXEC SQL END DECLARE SECTION
EXEC SQL WHENEVER SQLERROR STOP
EXEC SQL SELECT Reg_No,name,marks,grade
FROM Student WHERE marks>90
INTO: Reg_No,:name,:marks,:grade;

Printf(“Registration Number”);
Printf(“Name”);
Printf(“Marks”);
Printf(“Grade”);
Exit();
EXEC SQL DISCONNECT }

Unit 1 DBMS

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