Partial Dependency in DBMS – Explained with Examples

Are you facing issues with data redundancy and inconsistency within your database? Do you often feel disturbed by abnormalities in the data and performance-related issues?

Mostly this type of problem arises out of partial dependency within your DBMS. It is a common issue that can lead to data anomalies and performance issues.

Welcome to this blog, where we slowly learn what partial dependency in DBMS is, how it is affecting your database, and, most importantly, ways and means by which you could resolve this through normalisation techniques.

Let’s dive in and ensure the database remains efficient, consistent, and reliable.

Basic Terms and Concepts

Before continuing with the topic, there are a few very basic terms that we should be aware of. Let’s break each of them down:

Database: A structured collection of data in an electronic medium.

Functional Dependency: The relationship between two attributes: one attribute is dependent on another.

Attributes: These are the data or characteristics of the entities in your database. For example, in a student database, student ID, name, and date of birth would all be attributes.

Prime Attribute: An attribute which belongs to a candidate key. This is the primary identifier for the records.

Non-Prime Attribute: It does not form any candidate key. These attributes are important but do not uniquely identify the records.

Candidate Key: The minimal set of attributes which can uniquely identify any record. There can be numerous candidate keys from a table.

Primary Key: The candidate key which is selected to uniquely identify any record of a table.

Partial dependency in DBMS happens when a non-prime attribute depends only on a part of the composite primary key. This can lead to unnecessary redundancy and data anomalies.

Let’s break it down with a clear example.

Imagine we have a table Student_Grades with these attributes:

• StudentID
• CourseID
• CourseName
• Grade

In this table, StudentID and CourseID form the primary key together. But if CourseName depends only on CourseID, not the entire primary key, we have a partial dependency.

This means we don’t need StudentID to determine CourseName.

Here’s a small table to illustrate this:

In the table above, CourseName depends only on CourseID. This partial dependency can cause data redundancy.

If CourseName changes, we need to update it in multiple places, increasing the risk of errors and inconsistencies.

Now, how do we identify this issue?

Methods for Identifying Partial Dependency in Database Tables

Identifying partial dependency in DBMS involves checking if any non-prime attribute relies only on the part of the primary key.

This can be tricky, but with a few steps, we can make it easier.

Steps to Identify Partial Dependency

1. List All Attributes:
   • Write down all the attributes in your table.
   • Identify which ones are prime (part of the primary key) and non-prime.
2. Determine Functional Dependencies:
   • Check the functional dependencies between these attributes.
   • See if a non-prime attribute depends on the entire primary key or just a part of it.
3. Look for Redundancy:
   • If you find repeating groups of data, it’s a sign of partial dependency.
   • Redundancy usually means some data is dependent only on a portion of the primary key.
4. Example Analysis:
   • Let’s consider a table Student_Courses with attributes StudentID, CourseID, InstructorName.
   • Here, StudentID and CourseID form the primary key together.
   • If InstructorName depends only on CourseID, we have a partial dependency.

Let’s consider the same Student_Grades table:

Here, CourseName depends only on CourseID. This dependency shows that CourseName is partially dependent on CourseID.

This partial dependency can lead to data anomalies and redundancy. Identifying such dependencies helps us understand the structure and design flaws in our database.

Consequences of Partial Dependency on Data Integrity and Performance

Partial dependency can cause several issues in our database.

Data Inconsistency:

• Repetitive data can cause mismatches and errors.
• If InstructorName changes, we need to update it in multiple places.

Data Anomalies:

• Update Anomaly: Updating redundant data in multiple places increases the risk of errors.
• Insert Anomaly: Adding a new course without an instructor can be problematic.
• Delete Anomaly: Deleting a student’s record might remove information about the instructor.

Performance Issues:

• Redundant data takes up more space.
• Queries become slower as the database grows.

Steps to Eliminate Partial Dependency in DBMS Through Normalisation

To fix partial dependency, we normalise the database. Normalisation involves organising the data to reduce redundancy and improve integrity.

First Normal Form (1NF)

• Ensure all attributes have atomic values.
• No repeating groups or arrays in the table.

Example:

A table with StudentID and Courses, where Courses is a list of course IDs, is not in 1NF. Split it into separate rows for each course.

Second Normal Form (2NF)

• Move non-prime attributes to separate tables.
• Ensure each non-prime attribute is fully functionally dependent on the entire primary key.

Example:

Original Table: Student_Courses

Normalisation by creating separate tables:

Student_Courses

Courses

By splitting the tables, we eliminate partial dependency, reduce redundancy, and ensure data consistency.

Coding Example:

Let’s see a Python program to demonstrate this process.

# Input and display tables in Python

Output:

Student_Courses Table:

Courses Table:

This program splits the original table into two, showing how we eliminate partial dependency by normalising the data.

Examples of Partial Dependency in Database Design

Why does your database sometimes have redundant data? Why do some updates lead to errors? These are issues caused by partial dependency.

Let’s look at examples to understand this better.

Example 1:

Consider a table Employee_Project with these attributes:

• EmployeeID
• ProjectID
• ProjectName
• Role

Here, EmployeeID and ProjectID form the primary key. If ProjectName depends only on ProjectID, we have a partial dependency.

In this table, ProjectName depends only on ProjectID. This dependency can cause redundancy and inconsistency.

Example 2:

Consider a table Student_Enrollment with attributes:

• StudentID
• CourseID
• CourseName
• Instructor

Here, CourseName and Instructor depend only on CourseID. This shows partial dependency.

Advanced Normalisation Techniques

Partial dependency can be fixed by normalisation. We’ve already talked about 1NF and 2NF. Let’s go further.

Third Normal Form (3NF)

• Remove transitive dependencies.
• Ensure non-prime attributes depend only on the primary key.

Example:

Original Table: Student_Courses

Normalise by creating separate tables:

Student_Courses

Courses

Instructor_Departments

Boyce-Codd Normal Form (BCNF)

• Handle situations where 3NF is not enough.
• Ensure every determinant is a candidate key.

Example:

Original Table: Bookings

If CustomerID and RoomID are candidate keys, but BookingDate depends only on RoomID, we move BookingDate to a new table.

Bookings

Room_Bookings

These steps ensure data integrity and reduce redundancy.

Conclusion

In this blog, we explored partial dependency in DBMS, a key factor in causing data redundancy and inconsistencies.

Through detailed examples, we learned how to identify partial dependencies and the normalisation techniques necessary to eliminate them. We could have a well-structured database with reduced errors and improved performance by applying 2NF, 3NF, and BCNF.

Understanding and dealing with partial dependency is part of managing your database effectively, which will help you in keeping a neat, organised, and efficient database system.