Partial Dependency in DBMS

By Anjnee Bhatnagar|Updated : October 4th, 2022

Partial Dependency in DBMS occurs when a non-prime attribute depends on the proper subset of the candidate key. The functional dependency in DBMS may be partial or fully functional dependent. In the process of normalization of the database, partial dependency on FD is undesirable. Let us know more about partial dependency in DBMS.

In this article, we will learn thoroughly about the partial dependency in DBMS as per the GATE syllabus for CSE. We will first learn the meaning of partial dependency in DBMS and then understand it through examples and practice problems.

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What is Partial Dependency in DBMS?

The partial dependency in DBMS occurs when a non-prime attribute, say z, depends on the proper subset of attribute x, say y. A pictorial representation of the same is as follows:

Partial Dependency in DBMS

Here x is any candidate key and y ⊂ x, and z is some non-prime attribute, then y → z is a partial dependency.

Partial dependency is undesirable, and therefore it is eliminated from the second normal form during the normalization process.

Partial Dependency in DBMS Examples

To understand the partial dependency in DBMS better, we will see an example and learn how we can detect a partial dependency given a set of functional dependencies for a relation.

Example: Consider a relation R(ABC) with a set of functional dependencies AB → C, C → A, and find whether there exists any functional dependency or not?

Solution: First, we find the candidate keys for the relation R to find the partial dependency.

The candidate keys are AB and BC as AB+ ={ABC}, and BC+ ={ABC}.

Now we proceed to find the partial dependency in the FD set as we know that AB is the candidate key. Hence, AB → C is not the case of partial dependency, while in the FD C → A, attribute C is a proper subset of the candidate key BC. Therefore, it forms the partial dependency.

Practice Questions on Partial Dependency in DBMS

Few practice problems will boast the concept of understanding the partial dependency in DBMS. Let us see a few practice problems.

Example 1: X → Z is said to be partial dependency if:

  1. X is a proper subset of candidate key
  2. X is a prime attribute
  3. Both A and B
  4. None of the above

Answer: Option C

Example 2: Consider the relation R(ABCDEFGH) with FDs as:

{
AB → CEFGH
A→D
F → G
FB → H
HBC → ADEFG
FBC → ADE
}

Find the partial dependency present in R.

Answer: A→D

Important GATE Topics

Types Of Op AmpTellegens Theorem
What Is GearDegree Of Static Indeterminacy
Difference Between Linker And LoaderMillmans Theorem
OP AmpSimple Op Amp Circuits
Maxwells Reciprocal TheoremDifference Between Abstract Class And Interface

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FAQs on Partial Dependency in DBMS

  • A partial dependency in DBMS occurs between a prime attribute and a non-prime attribute. A partial dependency exists if a non-prime attribute is dependent on a proper subset of a candidate key. Example A→B, where B is some non-prime attribute, and A is the proper subset of a candidate key, say X.

  • A partial dependency occurs between a prime attribute or the proper subset of the candidate key and the non-prime attribute. At the same time, a transitive dependency is an indirect relation between functional dependencies. Let's say A→B, and B→C, then A→C, here A→C forms a transitive dependency.

  • No, partial dependency is not allowed in 2NF. It forms redundancy. Therefore, it is eliminated from the second normal form. A partial dependency is of the form: P→Q, where Q is some non-prime attribute, and P is the proper subset of a candidate key, say R.

  • A database's entire functional dependency corresponds to the Second Normal Form normalization standard of normalization (2NF). This entails that all non-key qualities are completely functionally dependent on the primary key and that it satisfies the conditions of 1NF.

  • The organization of data in a database is done by normalization. This comprises creating tables and linking those tables together in accordance with principles designed to protect the data and increase the database's adaptability by removing duplication and inconsistent reliance.

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