Rdbms-2 Practical File

Practical-1

Normalization in DBMS: 1NF, 2NF, 3NF and BCNF in Database

Normalization is a process of organizing the data in database to avoid data redundancy, insertion anomaly, update anomaly & deletion anomaly. Let’s discuss about anomalies first then we will discuss normal forms with examples.

Anomalies in DBMS

There are three types of anomalies that occur when the database is not -*normalized. These are – Insertion, update and deletion anomaly. Let’s take an example to understand this.

Example: Suppose a manufacturing company stores the employee details in a table named employee that has four attributes: emp_id for storing employee’s id, emp_name for storing employee’s name, emp_address for storing employee’s address and emp_dept for storing the department details in which the employee works. At some point of time the table looks like this:

emp_id	emp_name	emp_address	emp_dept
101	Rick	Delhi	D001
101	Rick	Delhi	D002
123	Maggie	Agra	D890
166	Glenn	Chennai	D900

The above table is not normalized. We will see the problems that we face when a table is not normalized.

Update anomaly: In the above table we have two rows for employee Rick as he belongs to two departments of the company. If we want to update the address of Rick then we have to update the same in two rows or the data will become inconsistent. If somehow, the correct address gets updated in one department but not in other then as per the database, Rick would be having two different addresses, which is not correct and would lead to inconsistent data.

Insert anomaly: Suppose a new employee joins the company, who is under training and currently not assigned to any department then we would not be able to insert the data into the table if emp_dept field doesn’t allow nulls.

Delete anomaly: Suppose, if at a point of time the company closes the department D890 then deleting the rows that are having emp_dept as D890 would also delete the information of employee Maggie since she is assigned only to this department.

To overcome these anomalies we need to normalize the data. In the next section we will discuss about normalization.

Normalization

Here are the most commonly used normal forms:

·         First normal form(1NF)

·         Second normal form(2NF)

·         Third normal form(3NF)

·         Boyce & Codd normal form (BCNF)

First normal form (1NF)

As per the rule of first normal form, an attribute (column) of a table cannot hold multiple values. It should hold only atomic values.

Example: Suppose a company wants to store the names and contact details of its employees. It creates a table that looks like this:

emp_id	emp_name	emp_address	emp_mobile
101	Herschel	New Delhi	8912312390
102	Jon	Kanpur	8812121212 9900012222

Two employees (Jon & Lester) are having two mobile numbers so the company stored them in the same field as you can see in the table above.

This table is not in 1NF as the rule says “each attribute of a table must have atomic (single) values”, the emp_mobile values for employees Jon & Lester violates that rule.

To make the table complies with 1NF we should have the data like this:

emp_id	emp_name	emp_address	emp_mobile
101	Herschel	New Delhi	8912312390
102	Jon	Kanpur	8812121212
102	Jon	Kanpur	9900012222
103	Ron	Chennai	7778881212
104	Lester	Bangalore	9990000123

Second normal form (2NF)

A table is said to be in 2NF if both the following conditions hold:

·         Table is in 1NF (First normal form)

·         No non-prime attribute is dependent on the proper subset of any candidate key of table.

An attribute that is not part of any candidate key is known as non-prime attribute.

Example: Suppose a school wants to store the data of teachers and the subjects they teach. They create a table that looks like this: Since a teacher can teach more than one subjects, the table can have multiple rows for a same teacher.

teacher_id	subject	teacher_age
111	Maths	38
111	Physics	38
222	Biology	38

Candidate Keys: {teacher_id, subject}
Non prime attribute: teacher_age

The table is in 1 NF because each attribute has atomic values. However, it is not in 2NF because non prime attribute teacher_age is dependent on teacher_id alone which is a proper subset of candidate key. This violates the rule for 2NF as the rule says “no non-prime attribute is dependent on the proper subset of any candidate key of the table”.

To make the table complies with 2NF we can break it in two tables like this:
teacher_details table:

teacher_id	teacher_age
111	38
222	38
333	40

teacher_subject table:

teacher_id	subject
111	Maths
111	Physics
222	Biology
333	Physics
333	Chemistry

Now the tables comply with Second normal form (2NF).

Third Normal form (3NF)

A table design is said to be in 3NF if both the following conditions hold:

·         Table must be in 2NF

·         Transitive functional dependency of non-prime attribute on any super key should be removed.

An attribute that is not part of any candidate key is known as non-prime attribute.

In other words 3NF can be explained like this: A table is in 3NF if it is in 2NF and for each functional dependency X-> Y at least one of the following conditions hold:

·         X is a super key of table

·         Y is a prime attribute of table

An attribute that is a part of one of the candidate keys is known as prime attribute.

Example: Suppose a company wants to store the complete address of each employee, they create a table named employee_details that looks like this:

emp_id	emp_name	emp_zip	emp_state	emp_city	emp_district
1001	John	282005	UP	Agra	Dayal Bagh
1002	Ajeet	222008	TN	Chennai	M-City
1006	Lora	282007	TN	Chennai	Urrapakkam
1101	Lilly	292008	UK	Pauri	Bhagwan
1201	Steve	222999	MP	Gwalior	Ratan

Super keys: {emp_id}, {emp_id, emp_name}, {emp_id, emp_name, emp_zip}…so on
Candidate Keys: {emp_id}
Non-prime attributes: all attributes except emp_id are non-prime as they are not part of any candidate keys.

Here, emp_state, emp_city & emp_district dependent on emp_zip. And, emp_zip is dependent on emp_id that makes non-prime attributes (emp_state, emp_city & emp_district) transitively dependent on super key (emp_id). This violates the rule of 3NF.

To make this table complies with 3NF we have to break the table into two tables to remove the transitive dependency:

employee table:

emp_id	emp_name	emp_zip
1001	John	282005
1002	Ajeet	222008
1006	Lora	282007
1101	Lilly	292008

employee_zip table:

emp_zip	emp_state	emp_city	emp_district
282005	UP	Agra	Dayal Bagh
222008	TN	Chennai	M-City
282007	TN	Chennai	Urrapakkam
292008	UK	Pauri	Bhagwan
222999	MP	Gwalior	Ratan

 

 

Boyce Codd normal form (BCNF)

It is an advance version of 3NF that’s why it is also referred as 3.5NF. BCNF is stricter than 3NF. A table complies with BCNF if it is in 3NF and for every functional dependency X->Y, X should be the super key of the table.

Example: Suppose there is a company wherein employees work in more than one department. They store the data like this:

emp_id	emp_nationality	emp_dept	dept_type	dept_no_of_emp
1001	Austrian	Production and planning	D001	200
1001	Austrian	stores	D001	250

Functional dependencies in the table above:
emp_id -> emp_nationality
emp_dept -> {dept_type, dept_no_of_emp}

Candidate key: {emp_id, emp_dept}

The table is not in BCNF as neither emp_id nor emp_dept alone are keys.

To make the table comply with BCNF we can break the table in three tables like this:
emp_nationality table:

emp_id	emp_nationality
1001	Austrian
1002	American

emp_dept table:

emp_dept	dept_type	dept_no_of_emp
Production and planning	D001	200
stores	D001	250

emp_dept_mapping table:

emp_id	emp_dept
1001	Production and planning
1001	stores
1002	design and technical support

Functional dependencies:
emp_id -> emp_nationality
emp_dept -> {dept_type, dept_no_of_emp}

Candidate keys:
For first table: emp_id
For second table: emp_dept
For third table: {emp_id, emp_dept}

This is now in BCNF as in both the functional dependencies left side part is a key.

Practical-2 

AIM: - CASE STUDY ON NORMALIZATION.

The WORK relation illustrates data about employees, their job title and the department they are assigned to. From examining sample data and discussions with management we have found that employees can have multiple job titles and can be assigned to more than one department. Each department is completely sited in a single location but a city could have more than one department at some time.

JOB	ENAME	EADDR	E#	D#	DNAME	DLOCN
HELPER	DAVIS	111 FIRST ST	12	1	PRESSING	ALCOA
HELPER	SPENCHE	222 SECOND ST	78	1	PRESSING	ALCOA
ELECTRICIAN	MURPHY	100 MAIN ST	66	2	WELDING	NIOTA
FOREMAN	SMITH	300 BROAD ST	77	9	PACKING	LOUDON
CLERK	WILSON	111 FIRST ST	99	7	PAYROLL	MEMPHIS
CLERK	DAVIS	111 FIRST ST	12	1	PRESSING	ALCOA
CLERK	SPENCE	222 SECOND ST	78	1	PRESSING	ALCOA
CLERK	DAVIS	111 FIRST ST	12	5	MAILROOM	ONEIDA

For this relation, a composed key is required as no one attribute is a candidate. It turns out that the following SRN depicts the situation:

WORK ( Job, EName, EAddr, E#, D#, DName, DLocn )

and the functional dependency diagrams would be:

There are numerous problems with the data model as it currently stands. We can not add new employees until they have a job title and a department assignment. We can easily lose department data by removing an employee who is the sole person assigned to a department. Certain updates require careful propagation of changes throughout the database.Careful decomposition can take care of these problems. The employee data makes an obvious grouping and should be decomposed the get it into at least 2NF. It will actually go to BCNF as there are no further problems. It is ready to become a table.

EMPLOYEE

E#	ENAME	EADDR
12	DAVIS	111 FIRST ST
78	SPENCE	222 SECOND ST
66	MURPHY	100 MAIN ST
77	SMITH	300 BROAD ST
99	WILSON	111 FIRST ST

The Dept relation is another logical decomposition to remove the partial dependency and move to 2NF. Careful examination reveals the transitive dependency still exists so further decomposition is necessary.

                                         DEPT

D#	DNAME	DLOCN
1	PRESSING	ALCOA
2	WELDING	NIOTA
9	PACKYING	LOUDAN
7	PAYROLL	MEMPHIS
5	MAILROMM	ONEIDA

Job-Worked winds up looking like the original relation’s key. All three attributes are still the composed key. Since there are no dependencies, there is nothing to prevent this relation from being BSNF so it is ready too.

JOB-WORKED

E#	D#	JOB
12	1	HELPER
78	1	HELPER
66	2	ELECTRICIAN
77	9	FOREMAN
99	7	CLERK
12	1	CLERK
78	1	CLERK
12	5	CLERK

To remove the transitive dependency, we will decompose Dept into Department and Dept-Locn. Each of these is now in BCNF.

DEPARTMENT

D#	D#
1	PRESSING
2	WELDING
9	PACKING
7	PAYROLL
5	MAILROOM

DEPT-LOCN

D#	D#
1	PRESSING
2	WELDING
9	PACKING
7	PAYROLL
5	MAILROOM

Practical 3

AIM: Introduction to query processing and query optimization.

Introduction to Query Processing: 

 Query processing: A 3-step process that transforms a high-level query (of relational calculus/SQL) into an equivalent and more efficient lower-level query (of relational algebra).

1. Parsing and translation – Check syntax and verify relations. – Translate the query into an equivalent relational algebra expression.

 2. Optimization – Generate an optimal evaluation plan (with lowest cost) for the query plan.

 3. Evaluation – The query-execution engine takes an (optimal) evaluation plan, executes that plan, and returns the answers to the query.

  •  The success of RDBMSs is due, in part, to the availability – of declarative query languages that allow to easily express complex queries without knowing about the details of the physical data organization and – of advanced query processing technology that transforms the high-level user/application queries into efficient lower-level query execution strategies.

 • The query transformation should achieve both correctness and efficiency – The main difficulty is to achieve the efficiency – This is also one of the most important tasks of any DBMS.

 • Distributed query processing: Transform a high-level query (of relational calculus/SQL) on a distributed database (i.e., a set of global relations) into an equivalent and efficient lower-level query (of relational algebra) on relation fragments.

 • Distributed query processing is more complex – Fragmentation/replication of relations – Additional communication costs – Parallel execution.

                                         

                                        Fig1: Query Processing

Example: Transformation of an SQL-query into an RA-query. Relations: EMP(ENO, ENAME, TITLE), ASG(ENO,PNO,RESP,DUR) Query: Find the names of employees who are managing a project ? – High level query SELECT ENAME FROM EMP,ASG WHERE EMP.ENO = ASG.ENO AND DUR > 37 – Two possible transformations of the query are: ∗ Expression 1: ΠENAME ( σDUR>37 ∧EMP.ENO =ASG.ENO (EMP × ASG)) ∗ Expression 2: ΠENAME (EMP ⋊⋉ENO ( σDUR>37 (ASG))).

Query optimization :Query optimization is a crucial and difficult part of the overall query processing.

• Objective of query optimization is to minimize the following cost function:

                          I/O cost + CPU cost + communication cost

• Two different scenarios are considered: –

∗ Wide area networks.

∗ Communication cost dominates.

∗ low bandwidth.

∗ low speed.

∗ high protocol overhead.

• Most algorithms ignore all other cost components –

∗ Local area networks.

∗ Communication cost not that dominant.

∗ Total cost function should be considered.

                                                  Fig 2: Query Optimization