In the realm of data modeling, many-to-many relationships are often considered an "odd duck." Unlike one-to-one or one-to-many relationships, which can be directly implemented in physical database schemas, many-to-many relationships must remain in the abstract, the conceptual, the supernatural, and never the physical. This insubstantial nature makes them troublesome for greener data modelers.
Many-to-many relationships occur when multiple records in one entity/table are associated with multiple records in another table. This type of relationship is fundamental in modeling complex real-world scenarios. For example, consider the relationship between students and courses in a university database. A single student can enroll in multiple courses, and a single course can have multiple students enrolled. This forms a many-to-many relationship between the students and courses.
When it comes to physical implementation, many-to-many relationships cannot be directly expressed in a typical relational database. Other relationships (one-to-many, one-to-one) are expressed within the data structures by means of a foreign key. One takes the primary key from the one-side and places it as column(s) on the many-side. Obviously, the trouble we have with a many-to-many relationship is that there is no “one-side” from which to pull a primary key. Instead, a change must be made.
To implement a many-to-many relationship, the relationship itself is upgraded into a table. A table coming into existence in this fashion is referred to as an “associative” table, as it associates the correct combinations of many’s from each side of the relationship into single-valued pairings of keys. Continuing with the university example, the "Enrollments" table would be an associative table having foreign keys that reference the "Students" and "Courses" tables. And there are now relationships between the pre-existing tables and the new associative table. These are one-to-many relationships with the new association on the many-side of each.
This relationship upgrade allows for coherent SQL statements to retrieve valid information about the relationship between the two “many” tables. Querying data in many-to-many relationships requires joining the associative table with the original “many” tables. For example, to find all courses a student is enrolled in, a query would join the "Students" table with the "Enrollments" table, and then the "Enrollments" table with the "Courses" table filtering for the student in focus. The possibly complex, has been made simple and direct. An additional advantage in having an associative entity/table is that now should a data item rise that naturally is a descriptor of the relationship, it has a place to exist. For our University example, if we have a grade to assign to a specific student’s individual grade for an enrolled course, it would naturally be placed on our new Enrollment table. Similarly, if one has a circumstance wherein the relationships between tables change over time, start and stop dates may easily be added onto the associative table to track exactly when the relationship is, or is not, valid.
One might argue that the data model has now been made more complex, by having more tables and more relationships. However, most should find that this “complexity” allows for the data model to be more expressive and clearer, which might simplify the model for reviewers. Many-to-many relationships are quite common. Beyond the university example mentioned above, consider an ecommerce platform where products can belong to multiple categories, and categories can contain multiple products. Another example might be a social media platform where users can follow multiple other users, and each user can be followed by multiple others. These scenarios highlight the versatility and importance of many-to-many relationships in capturing the intricacy of real-world data. By using associative tables, we can effectively manage these relationships within relational databases. Understanding and mastering this concept is crucial for database designers and developers, as it enables the accurate modeling of complex interactions found in real-world data.