Functional Dependency in DBMS: What is, Types and Examples

A functional dependency is denoted by an arrow “→”. The functional dependency of X on Y is represented by X → Y. Let’s understand Functional Dependency in DBMS with example.

Example:

Employee number	Employee Name	Salary	City
1	Dana	50000	San Francisco
2	Francis	38000	London
3	Andrew	25000	Tokyo

In this example, if we know the value of Employee number, we can obtain Employee Name, city, salary, etc. By this, we can say that the city, Employee Name, and salary are functionally depended on Employee number.

Key terms

Here, are some key terms for Functional Dependency in Database:

Key Terms	Description
Axiom	Axioms is a set of inference rules used to infer all the functional dependencies on a relational database.
Decomposition	It is a rule that suggests if you have a table that appears to contain two entities which are determined by the same primary key then you should consider breaking them up into two different tables.
Dependent	It is displayed on the right side of the functional dependency diagram.
Determinant	It is displayed on the left side of the functional dependency Diagram.
Union	It suggests that if two tables are separate, and the PK is the same, you should consider putting them. together

Rules of Functional Dependencies

Below are the Three most important rules for Functional Dependency in Database:

• Reflexive rule –. If X is a set of attributes and Y is_subset_of X, then X holds a value of Y.
• Augmentation rule: When x -> y holds, and c is attribute set, then ac -> bc also holds. That is adding attributes which do not change the basic dependencies.
• Transitivity rule: This rule is very much similar to the transitive rule in algebra if x -> y holds and y -> z holds, then x -> z also holds. X -> y is called as functionally that determines y.

Multivalued Dependency in DBMS

Multivalued dependency occurs in the situation where there are multiple independent multivalued attributes in a single table. A multivalued dependency is a complete constraint between two sets of attributes in a relation. It requires that certain tuples be present in a relation. Consider the following Multivalued Dependency Example to understand.

Example:

Car_model	Maf_year	Color
H001	2017	Metallic
H001	2017	Green
H005	2018	Metallic
H005	2018	Blue
H010	2015	Metallic
H033	2012	Gray

In this example, maf_year and color are independent of each other but dependent on car_model. In this example, these two columns are said to be multivalue dependent on car_model.

This dependence can be represented like this:

car_model -> maf_year

car_model-> colour

Trivial Functional Dependency in DBMS

The Trivial dependency is a set of attributes which are called a trivial if the set of attributes are included in that attribute.

So, X -> Y is a trivial functional dependency if Y is a subset of X. Let’s understand with a Trivial Functional Dependency Example.

For example:

Emp_id	Emp_name
AS555	Harry
AS811	George
AS999	Kevin

Consider this table of with two columns Emp_id and Emp_name.

{Emp_id, Emp_name} -> Emp_id is a trivial functional dependency as Emp_id is a subset of {Emp_id,Emp_name}.

Non Trivial Functional Dependency in DBMS

Functional dependency which also known as a nontrivial dependency occurs when A->B holds true where B is not a subset of A. In a relationship, if attribute B is not a subset of attribute A, then it is considered as a non-trivial dependency.

Company	CEO	Age
Microsoft	Satya Nadella	51
Google	Sundar Pichai	46
Apple	Tim Cook	57

Example:

(Company} -> {CEO} (if we know the Company, we knows the CEO name)

But CEO is not a subset of Company, and hence it’s non-trivial functional dependency.

Transitive Dependency in DBMS

A Transitive Dependency is a type of functional dependency which happens when “t” is indirectly formed by two functional dependencies. Let’s understand with the following Transitive Dependency Example.

Example:

Company	CEO	Age
Microsoft	Satya Nadella	51
Google	Sundar Pichai	46
Alibaba	Jack Ma	54

{Company} -> {CEO} (if we know the compay, we know its CEO’s name)

{CEO } -> {Age} If we know the CEO, we know the Age

Therefore according to the rule of rule of transitive dependency:

{Company} -> {Age} should hold, that makes sense because if we know the company name, we can know his age.

Note: You need to remember that transitive dependency can only occur in a relation of three or more attributes.

What is Normalization?

Normalization is a method of organizing the data in the database which helps you to avoid data redundancy, insertion, update & deletion anomaly. It is a process of analyzing the relation schemas based on their different functional dependencies and primary key.

Normalization is inherent to relational database theory. It may have the effect of duplicating the same data within the database which may result in the creation of additional tables.

Advantages of Functional Dependency

• Functional Dependency avoids data redundancy. Therefore same data do not repeat at multiple locations in that database
• It helps you to maintain the quality of data in the database
• It helps you to defined meanings and constraints of databases
• It helps you to identify bad designs
• It helps you to find the facts regarding the database design

Summary

• Functional Dependency is when one attribute determines another attribute in a DBMS system.
• Axiom, Decomposition, Dependent, Determinant, Union are key terms for functional dependency
• Four types of functional dependency are 1) Multivalued 2) Trivial 3) Non-trivial 4) Transitive
• Multivalued dependency occurs in the situation where there are multiple independent multivalued attributes in a single table
• The Trivial dependency occurs when a set of attributes which are called a trivial if the set of attributes are included in that attribute
• Nontrivial dependency occurs when A->B holds true where B is not a subset of A
• A transitive is a type of functional dependency which happens when it is indirectly formed by two functional dependencies
• Normalization is a method of organizing the data in the database which helps you to avoid data redundancy