1. RELATIONAL DATABASES & Relational Algebra
Dr.M.Pyingkodi
Dept of MCA
Kongu Engineering College
Erode,Tamilnadu,India
pyingkodikongu@gmail.com
2. Query Languages
• It is a language in which a user requests
information from the db.
2
Query Languages
Procedural Language Non Procedural Language
Specifies what data are
required & specify how to get
those data
e.g Relational Algebra
Specifies what data are
required without specify how
to get those data
e.g. Relational calculus
3. Relational Algebra
• It consists of set of operations that take one or
more relations as input and produces new
relation as output.
• The operations can be divided into,
– Basic operations: Select, Project, Union, rename,
set difference and Cartesian product
– Additional operations: Set intersections, natural
join, division and assignment.
– Extended operations: Aggregate operations and
outer join
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4. Basic operations
• Select
– It selects tuples that satisfy a given predicate, To
denote selection.
(Sigma) is used.
4
Syntax: condition (Table name)
6. Project Operation
• It selects attributes from the relation.
• – Symbol for project
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∏
Syntax: ∏
<Attribute list>
(Table name)
E.g…
eid,sal
(employee)
∏
7. Project Operation
• Combining Select & Project Operation
E.G..
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eid,sal
(employee)
∏ Sal>1000
( )
- Selects tuples where sal>1000 & from them only eid
and salary attributes are selected.
8. Mathematical Set Operations
• Union Operation:
– R1 U R2 - implies that tuples either from R1 or
tuples from R2 or both R1 & R2.
• E.G..
eid ename
1
2
3
4
A
B
C
D
8
eid dno dname
1
4
5
6
10
50
60
70
XXX
YYY
ZZZ
UUU
EMP1 EMP2
10. SET DIFFERENCE
• R1 – R2 implies tuples present in R1 but not in
R2.
10
eid
(EMP1)
∏
eid
(EMP2)
∏
-
E.G…
eid
2
3
11. CARTESIAN PRODUCT
• R1 R2 allows to combine tuples from any two
relations.
• E.G.. Emp1 Emp2
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12. CARTESIAN PRODUCT
Emp1.ei
d
ename Emp2.eid dno dname
1
1
1
1
2
A
A
A
A
B
1
4
5
6
1
10
50
60
70
10
XXX
YYY
ZZZ
UUU
XXX 12
eid ename
1
2
3
4
A
B
C
D
eid dno dname
1
4
5
6
10
50
60
70
XXX
YYY
ZZZ
UUU
EMP1 EMP2
Emp1 Emp2
13. Rename Operation
• To rename the name of a relation or the name of an
attribute.
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X
Relational Algebra Expression
rho
Returns the result of E under
the name x and with the
attributes renamed
A1,A2,……. An
(E)
15. (B) NATURAL JOIN OR EQUI JOIN
• Used to combine related tupules from two
relations.
• It requires that the two join attributes have
the same name, otherwise renaming
operation is applied first and then join
operation is applied.
• Symbol:
15
17. (B) NATURAL JOIN OR EQUI JOIN
• E.g..
eid ename sal dno
1
2
3
4
X
Y
Z
A
10000
20000
30000
16000
10
20
10
40
Dno Dname mgr
10
20
30
XXX
YYY
ZZZ
ABC
PQR
MNO
17
EMPLOYEE DEPARTMENT
18. (B) NATURAL JOIN OR EQUI JOIN
• Employee
eid ename sal dno dname mgr
1
2
3
X
Y
Z
10000
20000
30000
10
20
10
XXX
YYY
xxx
ABC
PQR
ABC
18
Dept or
Employee
Employee.dno = department.dno
Dept
19. OUTER JOIN
• It is an extension of the join operation to deal
with missing information.
• In natural join, only the matching tuples
comes in the result and the unmatched tuples
are lost. To avoid this loss of information we
use outer join.
• There are 3 forms of outer join operation They
are Left outer join, Right outer join and full
outer join
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20. LEFT OUTER JOIN-
• It takes all tuples in the left relation that did
not match with any tuple in the right relation
and pads the tuples with null values for all
other attributes from the right relation and
adds them to the result of the natural join.
• E.g…
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21. LEFT OUTER JOIN-
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eid ename sal dno
1
2
3
X
Y
Z
10000
20000
30000
10
20
40
EMPLOYEE
Dno Dname mgr
10
20
30
XXX
YYY
ZZZ
ABC
PQR
MNO
DEPARTMENT
EMPLOYEE DEPARTMENT
22. LEFT OUTER JOIN-
Eid Ename Sal Dno Dname mgr
1
2
3
X
Y
Z
10000
20000
30000
10
20
40
XXX
YYY
NULL
ABC
PQR
NULL
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23. RIGHT OUTER JOIN-
• It takes all tuples in the right relation that did
not match with any tuple in the left relation
and pads the tuples with null values for all
other attributes and adds them to the result
of the natural join.
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25. FULL OUTER JOIN
• Padding tuples from the left relation that
didn’t match any from the right relation, as
well as tuples from the right relation that did
not match any from the left relation & adding
them to the result of the join.
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27. Division Operation
• It is denoted by . It is suited to queries that
include the phrase ‘for all’.
• E.G.. Consider three relations
Acc-No. Branch-
name
Balance
B-101
B-102
B-103
B-104
B-105
P
Q
R
S
T
5000
4000
9000
7000
3500
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Account
29. Division Operation
• Suppose we want to find all customers who have an
account at all the branches located in Adayar.
• The query is,
•
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∏ Customer-name,
branch-name
(depositor account)
∏branch-name Branch_city
= “Adayar” (branch)
( )
30. Division Operation
• Step 1:
• List out all the branches located in Adayar
• Step2: R1=
• Find all customer name, branch_name pairs
for which the customer has an account at a
branch
• R2=
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∏branch-name Branch_city
( )
= “Adayar” (branch)
∏ Customer-name,
branch-name
(depositor account)
31. Division Operation
• Step3:
• We need to find customers who appear in r2
with every branch name in R1.
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∏ Customer-name,
branch-name
(depositor account)
∏branch-name Branch_city
= “Adayar” (branch)
( )
32. Aggregate Functions
• It takes a collection of values and return a single
value as a result.
• Avg., min., max., sum., count are few aggregate
functions.
• Ex:
eid ename Salary
1
2
3
4
5
A
B
C
D
E
10000
20000
30000
4000
5000
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O/p of
Sum (salary)
(r)
33. Relational Calculus
• It can be divided as Tuple Relational calculus
and Domain Relational Calculus
• Tuple relational Calculus:
• It is a non procedural query language
• Specifies what data are required without
describing how to get those data
• Each Query is in the form of {t | P(t)}.
• It is the set of all tuples ‘t’ such that predicate
P is true for ‘t’.
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34. Relational Calculus
• Notations Used:
• t is a tuple variable, t[A] denotes the value of
tuple t on attribute A
• t r denotes that tuple ‘t’ is in relation ‘r’.
• P is the formula similar to that of the
predicate calculus.
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35. Predicate calculus formula
• Set of attributes and constants
• Set of comparison operators(e.g. <, >, <, >, =,
=).
• Set of connectives: and(^), or( ), not( )
• Implication( ) : X Y, if X is true, then Y is
True
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36. Set of Quantifiers:
• - there exists
• Definition for there exists
• t r(Q(t)) = ‘there exists’ a tuple in relation
r such that predicate Q(t) is true.
V - For all
• Definition for ‘For all’
• V t r(Q(t))=Q(t) is true “ for all” tuples ‘t’ in
relation r.
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37. Domain Relational calculus
• The domain relational calculus uses domain variables
that take on values from an attribute domain rather
than values for entire tuple.
• Each Query is an expression of the form,
• {<x1,x2,…xn>/P(x1,x2,…xn)}
• Where x1,x2,…xn represent domain variables.
• P represents a formula similar to that of
predicate calculus.
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38. SQL
• Structured Query Language (SQL) is the standard
command set used to communicate with the
relational database management systems (RDBMS).
• The DBMS processes the SQL request, retrieves the
requested data from the Database, and returns it.
• This process of requesting data from a database and
receiving back the results is called a db query and
hence the name Structured Query Language.
• It is a non-procedural language..
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39. Advantages of SQL
• It is a high level language that provides a greater degree of
abstraction than procedural language.
• Applications written in SQL can be easily ported across the
systems, so it is the common language for all relational
databases.
• The queries are written by using English like language, so easy
to understand.
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40. Parts of SQL
• The SQL language has several parts:
• I. Data Definition Language (DDL):creates, changes and removes a
table’s structure.
• E.G.. CREATE,ALTER,DROP,RENAME and TRUNCATE
• 1. Create:
It is used to create a new table in oracle.
• Syntax:
Create table table_name(colunmn_name1 data type1
[constraint], column_name2 data type2[constraint],…
column_ name n datatype n [constraint]);
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41. Data Types
• SQL supports following data types,
• 1. varchar2(size) – to store variable length character data.
• 2. char(size) – to store fixed length character data.
• 3. number(P) – to store integer values, P- represents
maximum
• length.
• 4. number(P,S) – to store fixed point decimal values, P is the
precision and S is the scale.
5. Date - to store date and time values.
Format is 14-MAR-08, 2:50:40
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42. Data Definition Language (DDL):
• 3 types of constraints are used,
• NOT NULL - Prevents null values entered into a
column.
• UNIQUE – Prevents duplicate values entered into a
column.
• PRIMARY – Requires all values entered into the
column be unique as well as not null.
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43. Data Definition Language (DDL):
• Example:
• Create table customer(customer_name varchar(10),
cust_id number(5) primary key, dob date not
null,customer_address varchar(20));
• To see the structure of a table,
• desc table name;
• E.G.. desc customer;
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44. Data Definition Language (DDL):
• 2. ALTER:
• It is used to add a new column to the table, modifying the
existing column, including or dropping an integrity constraints
(primary key, not null, etc..)
• A. Adding new column:
• Syntax:
• alter table tablename add column_name data_type;
• E.G. alter table customer add pno number(10);
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45. Data Definition Language (DDL):
• B. MODIFYING AN EXISTING COLUMN:
• Syntax:
• alter table tablename modify column_name newdata_type;
• E.G…
• B. DROPPING A COLUMN:
• It is used to delete a table.
• Syntax:
• alter table tablename drop column column_name;
• E.g.. alter table customer drop column cust_add;
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alter table customer modify cust_id varchar2(6);
46. Data Definition Language (DDL):
• 3.DROPPING A TABLE
• It is used to drop a table.
• Syntax: drop table table_name;
• E.G.. Drop table customer;
• All data and table structure of a customer table is
permanently removed.
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47. Data Definition Language (DDL):
• 3.RENAMING A TABLE
• SYNTAX: rename oldtablename to newtablename;
• E.G.. Rename customer to cust_det;
• 4.TRUNCATE A TABLE
• It removes all records or rows from the table.
• SYNTAX: truncate table tablename;
• E.G.. Truncate table cust_det;
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48. BASIC STRUCTURE
• II. Data Retrievalretrieves data from the database.
• E.g.. Select
• The basic structure of an SQL expression consists of 3 clauses,
• Select
• From
• Where
• SELECT: It is used to list the attributes desired in the result of a query.
Syntax: select A1,A2,…..An from R1,R2,…Rm where P
E.g.. Select cust_name from customer;
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49. BASIC STRUCTURE
• E.g.2 Select distinct cust_name from customer;
• if we want duplicates removed.
• E.g.3 Select all cust_name from customer;
• To specify explicitly that duplicates are not allowed
• From Clause: It is used to list the relations involved in the query.
• E.G.. Select * from customer;
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50. BASIC STRUCTURE
• Where clause: It is used for specifying the
condition.
• E.g.. Find the names of all customer whose city is
“chennai”.
• select customer_name from customer where city=“chennai”.
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51. RENAME OPERATION
• Syntax:
• oldname as newname
• E.g..
• Select customer-name,borrower.loan_number from
borrower,loan where borrower.loan-number= loan.loan
number
• If we want the attribute name loan-number to be replaced
with the name loan-id, we can rewrite the preceding query as,
• Select customer-name,borrower.loan_number as loan-id from
borrower,loan where borrower.loan-number= loan.loan
number
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52. Tuple variables
• Tuple variables are defined in the from clause via the use of
the as clause.
• E.g .. For all customers who have a loan from a bank, find their
names and loan numbers as,
• Select customer-name,T.loan-number from
borrower as T, loan as S where T.loan-
number=S.loan-number
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53. STRING OPERATIONS
• SQL includes a string matching operator for comparisons on
character strings. Patterns are described using 2 special
characters,
• Percent(%) : The % character, matches any substring.
• Underscore( _ ) : The _ character matches any character.
• E.G.. Find the names of customers where the first
and second characters are ‘Ba’.
• Select customer_name from customer where customer_name
like ‘Ba%’;
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54. STRING OPERATIONS
• Eg..
• Find the names of customer where the second
character is ‘n’ or ‘a’.
• select customer_name from customer where
customer-name like ’_n%’ or ‘_a%’;
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55. Set Operations
• The set operations union, intersect and except
operate on relations and correspond to the relational
algebra operations U, , and -.
• Each above operations automatically eliminates the
duplicates and to retain all duplicates use union all,
intersect all, except all.
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U
56. Set Operations
• 1. Find all customers who have a loan or an
account,or both
• (select customer_name from depositor) union
(select customer_name from borrower);
• 2. Find all customers who have both loan and an
account,
• select customer_name from depositor) intersect
(select customer_name from borrower);
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57. Set Operations
• 3. Find all customers who have an account but no
loan,
• (select customer_name from depositor) except
(select customer_name from borrower);
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58. AGGREGATE FUNCTIONS
• It takes collection of values as input and
return a single value as output.
• SQL has 5 built in agg.fns,
• avg – Find average value.
• min – Find minimum value.
• max – Find Maximum value.
• Sum – Find sum of values.
• Count – Counts number of values
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59. AGGREGATE FUNCTIONS
• Find the average account balance at the
adayar branch.
• Select avg(balance) from account where
branch_name=“adayar”;
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60. Complex Queries
• Complex queries are often hard or impossible to
write a single SQL block.
• There are 2 ways for composing multiple SQL blocks
to express a complex query.
• 1.Derived relations 2. With clause.
• Derived Relations:
• SQL allows a subquery expression to used in
the from clause. If we use such an expression, then
we must give the result relation a name, and we can
rename the attributes.
• For renaming as clause is used.
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61. Complex Queries
• For e.g.. Find the average account balance of those
branches where the average account balance is
greater than 10,000
• select branch_name, avg_balance from(select branch_name,
avg(balance) from account group by branch_name) as
branch_avg(branch_name,avg_balance) where avg_balance >
10000;
• Here subquery result is named branch_avg with the attributes
branch_name and avg_balance.
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62. Complex Queries
• With Clause:
• It provides a way of defining a temporary view,
whose definition available only to the query in which
the with clause occurs.
• Consider the following query, which selects accounts
with the maximum balance. If there are many
accounts with the same maximum balance, all of
them are selected.
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63. Complex Queries
• with max_balance(value) as
select max(balance)
from account
select account_no from account, max_balance
where account.balance=max_balance.value;
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64. Parts of SQL
• III.Data Manipulation Language (DML):
• insert new rows, changes existing rows and
removes unwanted rows.
• E.G.. INSERT,UPDATE and DELETE
• Insert into account values(101,adayar,1000);
• Update account set balance=balance*100;
• Delete from account where
branch_name=‘adayar’;
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65. Parts of SQL
• IV.Transaction control Language (TCL):
• Manages and changes the logical transactions.
Transactions are changes made to the data by
DML statements group together.
• E.G.. COMMIT,ROLLBACK and SAVEPOINT.
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66. Parts of SQL
• Data Control Language (DCL): gives and removes right to
oracle objects.
• E.g.. GRANT,REVOKE
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67. Parts of SQL
• Embedded SQL and Dynamic SQL:
• defines how SQL statements can be
embedded within general purpose
programming languages such as C, C++, VB,
Java etc..
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68. Embedded SQL
• They are SQL statements included in the programming
language. The Programming language in which
the SQL statements are included is called the
host language.
• host language – C, COBOL, PASCAL etc…
• The Program is written in the host language and
whenever data access or data manipulation is need
the SQL statements are embedded. This embedded
source code is submitted to an SQL precompiler, which
process the SQL statements.
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69. Embedded SQL
• Variables of the host language can be referenced in
the embedded SQL statements thus allowing the
values calculated by the program to be used by the
SQL statements.
• The host variables are used by the embedded SQL
statements to receive the results of the SQL queries .
• Special program variables ( null indicators) are used
to assign and retrieve the NULL values to and from
the database.
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70. Embedded SQL
• E.g.. The following code segment shows how an SQL
statement is included in ‘C’ Program,
• #include<stdio.h>
Void main()
{
char name1[20];
…………
EXEC SQL
Select name into: name1 from employee where empno=1001;
…………..
}
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71. Dynamic SQL
• The dynamic SQL component of SQL allows programs
to construct and submit SQL queries at run time.
• This statements must be completely present at
compile time, they are compiled by the embedded
SQL preprocessor.
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72. Dynamic SQL
• E.g…
• #include<stdio.h>
void main( )
{
char *sqlprog=“update account set balance=balance * 1.10 where
acno=?”
EXEC SQL prepare dynprog from: sqlprog;
char account[10]=“A101”;
EXEC SQL execute dynprog using account;
}
The dynamic SQL program contains a ?, which is a place holder
for a value that is provided when the SQL program is
executed.
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73. Integrity Constraints
• They ensure that changes made to the db by
authorized users so not result in a lost of data
consistency.
• The integrity constraints guard against accidental
damage to the db.
• E.G…
• An a/c balance cannot be null.
• No 2 accounts can have the same acc_no.
• Every acc-No in the depositor relation must have a matching
acc-no in the A/c relation.
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74. Constraints on a single relation
• In addition to Primary key constraint, the
allowed In.Consts are,
• Not null
• Unique
• Check(<predicate>)
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75. Constraints on a single relation
• Not null:
– The result as a legal value for every attribute.
• E.g… By restricting the domain of
attributes acc_no and balance to exclude null
values by declaring them as,
acc_no char(20) not null,
balance number(10,2) not null
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76. Constraints on a single relation
• Unique:
• unique(A1,A2,….An)
• No 2 tuples in the relation can be equal to all
primary key attributes.
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77. Constraints on a single relation
• Check:
• A common use of check constraint is to
ensure that attribute values satisfy specified
conditions.
• E.g…
• A clause check (assets>=0) in the create table command for
relation branch would ensure that the value of assets is non-
negative,
• Create table branch(branchname varchar(10), branchcity
varchar(10),assets number(15,2), primary key(branch_name)
check(assets>=0))
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78. Referential Integrity
• Ensures that a value appears in one relation
for a given set of attributes also appears for a
certain set of attributes in another relation.
• For E.g… in the banking database,
• create table account(acc_no char(10), branch_name
char(15), balance number(10,2), primary
key(acc_no), foreign key(branch_name) references
branch, check(balance>=0));
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79. Referential Integrity
• Referential Integrity in E-R Model:
• Consider the relationship set R between entity sets E1 and
E2. The relational schema for R includes the primary keys K1
of E1 and K2 of E2.
• Then K1 and K2 form foreign keys on the relational
schemas for E1 and E2 respectively.
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E1 R E2
80. Referential Integrity
• Referential Integrity in SQL:
• Primary key, candidate and foreign keys can be
specified as part of the SQL create table statement.
• E.g…
• Create table customer(cust_name
char(10),cust_street char(20),cus_city
char(30),primary key(cust_name));
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81. ASSERTIONS
• It IS a predicate expressing a condition that we
wish the database always to satisfy.
• An assertion in SQL takes the form,
• Create assertion <assertion_name> check
<predicate>
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82. TRIGGERS
• A Trigger is a statement that is executed
automatically by the system as a side effect of
modification to the db.
• To design a trigger mechanism, we must
* Specify the conditions under which the trigger is to be
executed.
* Specify the actions to be taken when the triggers
executed.
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83. TRIGGERS
• Suppose that instead of allowing negative account
balances, the bank deals with overdrafts by,
* Setting the account balance to zero.
* Creating the loan in the amount of overdraft.
* Giving this loan a loan no identical to the
account number to the overdrawn amount.
• The condition for executing the trigger is an update
to the account relation that results in a negative
balance value.
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84. TRIGGERS
• E.G…
create trigger overdraft_trigger after update on account
referencing new row as nrow
for each row when nrow.balance<0
begin atomic
insert into borrower(select customer_name,acc_no
from depositor where nrow.acc_no=depositor.acc_no);
insert into loan values
(nrow.acc_no,nrow.branch_name,nrow.balance);
update account set balance = 0 where
account.acc_no=nrow.acc_no
end.
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85. SECURITY
• Security of data is an important concept in DBMS because it is
essential to safeguard the data against any unwanted users.
• There are 5 different levels of security,
• 1. DB system level:
• Authentication (verification) and authorization mechanism to allow
specific users access only to required data.
• 2. Operating System Level:
• * Protection from invalid logins
• * File-level access protection
• * Protection from improper use of “superuser” authority,
• * Protection from imprope use of privileged machine instructions.
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86. SECURITY
3. Network level:
* Each site must ensure that it communicates with treated sites.
* Links must be protected from theft or modification of messages.
Mechanisms Used:
* Identification protocol (password based).
* Cryptography.
4. Physical Level:
* Protection of equipment from floods, power failure etc..
* Protection of disks from theft etc…
* Protection of network and terminal cables from wire tapes etc…
Solution:
* Physical security by locks etc…
* Software techniques to detect physical security breaches.
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