Demystifying Database Design: The Key Differences Between Identifying and Non-Identifying Relationships

database design data modeling

Here's a breakdown of the key points:

Identifying Relationships:

  • The child table's primary key includes the parent table's primary key as part of it. In simpler terms, the child record needs the parent record's ID to be uniquely identified.
  • Example: Imagine tables for Orders (child) and Customers (parent). An order wouldn't exist without a customer, so the Orders table might have a CustomerID as part of its primary key, along with another identifier for the order itself.

Non-Identifying Relationships:

  • The child table has a foreign key referencing the parent table's primary key, but this foreign key is not a part of the child's primary key. The child record can be identified on its own.
  • Example: Consider tables for Books (child) and Genres (parent). A book can belong to a genre, but it can also exist without one. The Books table might have a GenreID as a separate attribute, but the primary key of the Books table wouldn't include it.

In essence:

  • Identifying relationships show a stronger dependency - the child entity cannot exist without the parent.
  • Non-identifying relationships indicate a looser dependency - the child entity can exist on its own, but it might have a category or classification based on the parent.

Understanding these relationships helps designers create efficient database structures that maintain data integrity and simplify queries.

Identifying Relationship:

  CustomerID INT NOT NULL,
  FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID)

CREATE TABLE Customers (
  CustomerName VARCHAR(255)

In this example:

  • Orders table has an OrderID as its primary key, but it also has a CustomerID which is a foreign key referencing the Customers table's primary key.
  • Since CustomerID is part of the Orders table's primary key, an Order record cannot exist without a valid CustomerID, representing an identifying relationship.

Non-Identifying Relationship:

  Title VARCHAR(255),
  GenreID INT,

  GenreName VARCHAR(255)


  • Books table has a BookID as its primary key. It also has a GenreID as a foreign key referencing Genres.
  • However, GenreID is not part of the Books table's primary key. A book can exist without a genre, demonstrating a non-identifying relationship.

Remember, this is a simplified example. These tables would likely have additional columns depending on the specific data being stored.

  1. Composite Primary Key (For Identifying Relationships):
  • Instead of including the entire parent table's primary key in the child table, you can create a composite primary key in the child table that combines the child's unique identifier with another attribute that, along with the parent's key, uniquely identifies a child record.


Imagine an OrderItems table (child) that stores details about items within an Order (parent). Traditionally, you might have OrderID (from Orders) as a foreign key and part of the primary key in OrderItems.

An alternative approach would be to have a composite primary key in OrderItems consisting of OrderID (foreign key) and another unique identifier like ItemSequence within that specific order. This maintains the identifying relationship but offers more flexibility.

  1. Separate Link Table (For Many-to-Many Relationships):
  • This method is particularly useful for many-to-many relationships, where a record in one table can be associated with multiple records in another, and vice versa. Instead of foreign keys in both tables, a separate link table is created to establish the connections.


Consider tables for Students and Courses. A student can take many courses, and a course can have many students. Traditionally, you might include foreign keys in both tables.

An alternative is to create a separate StudentCourses table with foreign keys to both Students and Courses. This table would have its own primary key, and the relationships between students and courses would be managed through this link table.

These are just a few examples, and the best method depends on the specific data model and relationships you're trying to represent. Remember, the goal is to create an efficient and normalized database structure that accurately reflects the real-world relationships between your data entities.

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