Cardinality in DBMS (Mapping Constraints)

Cardinality in DBMS

Introduction to DBMS

A software program called a Database Management System (DBMS) is made to effectively manage databases. It enables structured data creation, updating, deletion, and querying. Most modern DBMSs represent data using tables composed of rows and columns, simplifying data handling and retrieval. Structured Query Language (SQL) is the standard tool for interacting with such systems, making it easy to access and manipulate the data.

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What is Cardinality in DBMS?

In the context of DBMS, Cardinality refers to the number of associations between one entity and another in a relationship set. It describes how many instances of one entity are related to instances of another entity.

In a broader sense, a table’s cardinality also describes how many rows (or tuples) it has. However, in entity-relationship modeling, it specifically addresses the nature of relationships between two entities.

Understanding Cardinality Ratio (Mapping Constraints)

The Cardinality Ratio, also known as Mapping Cardinality, defines how many entities of one set can be associated with entities of another set in a binary relationship. This concept is crucial when designing relational models for real-world applications like customer databases, inventory systems, or educational platforms.

There are four types of cardinality mapping in DBMS:

1. One to One (1:1)
2. One to Many (1:M)
3. Many to One (M:1)
4. Many to Many (M:N)

Let’s explore each of these in detail:

1. One to One (1:1) Mapping

One entity in set A is linked to just one entity in set B in a one-to-one cardinality, and vice versa.

Example:
A student has exactly one student ID, and that student ID belongs to only one student. Similarly, There can only be one principal per school, and that principal can only oversee one school.

Note: A 1:1 relationship is not required for all of the entities in a set to participate. Depending on the situation, participation may be optional for certain entities.

2. Many to One (M:1) Mapping

A single entity from set B may be associated to several entities from set A in a many-to-one mapping.

Example:
Multiple patients in a hospital can be treated by a single doctor. Thus, the relationship from patients to doctors is Many to One.

This is a superset of One to One mapping, as the 1:1 scenario is a special case where the “many” happens to be one.

3. One to Many (1:M) Mapping

In One to Many relationships, a single entity from set A can be associated with multiple entities from set B.

Example:
A hospital may have several compounders. So, one hospital is related to many compounders, indicating a One to Many relationship.

This is also considered a superset of One to One mapping.

4. Many to Many (M:N) Mapping

In Many to Many cardinality, multiple entities from set A can be related to multiple entities from set B.

Example:
In a college, multiple students can be working on a single project, and one student can be working on multiple projects. This bidirectional relationship illustrates the Many to Many mapping.

This is the broadest form of mapping, encompassing all other cardinalities within it as subsets.

Choosing the Appropriate Mapping Cardinality

Selecting the correct cardinality for a relationship depends entirely on the real-world scenario being modeled. Here are some general guidelines:

• One to One (with total participation): The two entities can be merged into a single table.
• One to Many or Many to One: Can be implemented by foreign keys referencing the “one” side.
• Many to Many: Requires a separate relationship table with foreign keys referencing both participating entity tables.

Tip: Understanding the nature of relationships in your system helps in designing efficient, scalable, and normalized databases.

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Final Thoughts

Cardinality plays a critical role in the structure and integrity of relational databases. By properly defining mapping constraints between entities, database designers can create models that accurately reflect real-world business logic and support efficient data operations.

For more insights on DBMS concepts and database design strategies, keep learning with UpdateGadh.

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