Database Normalization

Normalization in DBMS: 1NF, 2NF, 3NF ,BCNF, 4NF and 5NF in Database

normalization_in_dbms:_1nf,_2nf,_3nf_,bcnf,_4nf_and_5nf_in_database

Normalization

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
166 Glenn Chennai D004

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
103 Ron Chennai 7778881212
104 Lester Bangalore 9990000123
8123450987

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
104 Lester Bangalore 8123450987

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)
  • A relation is in 2NF iff it has No Partial Dependency,ie. 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
333 Physics 40
333 Chemistry 40

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

The table is in 1NF 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
1201 Steve 222999

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
1002 American design and technical support D134 100
1002 American Purchasing department D134 600

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
design and technical support D134 100
Purchasing department D134 600

emp_dept_mapping table:

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

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.

Fourth Normal Form(4NF)

Tables cannot have multi-valued dependencies on a Primary Key.

Fifth Normal Form(5NF)

A composite key shouldn't have any cyclic dependencies.

Well, this is a highly simplified explanation for Database Normalization. One can study this process extensively though. After working with databases for some time, you'll automatically create Normalized databases, as it's logical and practical.

Denormalization:

Denormalization is also the method which is used in database. It is used to add the redundancy to execute the query quickly. It is technique in which data are combined to execute the query quickly. By using denormalizion the number of tables is decreased which oppose to the normalization.

Pros of Denormalization:-

  1. Retrieving data is faster since we do fewer joins
  2. Queries to retrieve can be simpler(and therefore less likely to have bugs), since we need to look at fewer tables.

Cons of Denormalization:-

  1. Updates and inserts are more expensive.
  2. Denormalization can make update and insert code harder to write.
  3. Data may be inconsistent . Which is the "correct" value for a piece of data?
  4. Data redundancy necessities more storage.

In a system that demands scalability, like that of any major tech companies, we almost always use elements of both normalized and denormalized databases.

Difference between Normalization and Denormalization:

S.NO Normalization Denormalization
1. In normalization, Non-redundancy and consistency data are stored in set schema. In denormalization, data are combined to execute the query quickly.
2. In normalization, Data redundancy and inconsistency is reduced. In denormalization, redundancy is added for quick execution of queries.
3. Data integrity is maintained in normalization. Data integrity is not maintained in denormalization.
4. In normalization, redundancy is reduced or eliminated. In denormalization redundancy is added instead of reduction or elimination of redundancy.
5. Number of tables in normalization is increased. Denormalization, Number of tables in decreased.
6. Normalization optimize the uses of disk spaces. Denormalization do not optimize the disk spaces.


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