State difference between relational and hierarchical model.
1 Answer
Of course. Here is a clear, point-by-point comparison of the Relational Model and the Hierarchical Model, two fundamentally different ways of structuring a database.
Introduction
The Hierarchical Model is one of the oldest database models, organizing data in a rigid, top-down tree structure. The Relational Model, developed by E.F. Codd, is the most widely used model today, organizing data into simple, flexible tables (relations).
The key difference lies in how they represent relationships between data: the hierarchical model uses a strict parent-child structure, while the relational model uses values in columns to link tables.
Key Differences: Relational vs. Hierarchical Model
1. Data Structure
- Hierarchical Model: Organizes data into a tree-like structure. Data is stored as records that are connected to one another through links. Each record is a collection of fields.
- Relational Model: Organizes data into a collection of tables (also called relations). Each table consists of rows (tuples) and columns (attributes).
Analogy:
Hierarchical: An organization chart or a file system with folders and subfolders.
Relational: A collection of linked spreadsheets.
2. Relationship Representation
- Hierarchical Model: Represents relationships in a strict parent-child format. A child record can have only one parent record. This directly models a one-to-many relationship.
- Relational Model: Represents relationships using foreign keys. A common column with a matching value is used to link rows from different tables. This model can easily represent one-to-one, one-to-many, and many-to-many relationships.
3. Handling Complex Relationships (Many-to-Many)
- Hierarchical Model: Cannot directly represent many-to-many relationships. To model this, you must duplicate data, which leads to significant data redundancy. For example, if a student can take multiple courses and a course can have multiple students, you would have to store the course information under each student, or vice versa, causing repetition.
- Relational Model: Handles many-to-many relationships efficiently by using a third table, known as a junction or linking table. This table contains foreign keys that reference the primary keys of the two tables being linked, completely eliminating data redundancy.
4. Data Access and Flexibility
- Hierarchical Model: Data access is navigational and rigid. You must start at the root of the tree and traverse down a predefined path to reach the desired data. This is efficient for planned queries but very inflexible for ad-hoc or unexpected queries.
- Relational Model: Data access is declarative and highly flexible. You simply declare what data you want using a query language like SQL. The DBMS figures out the best way to get it. You can join any tables with a common attribute, allowing for an almost infinite variety of queries.
5. Data Integrity and Independence
- Hierarchical Model: Has low data independence. If you change the structure of the database (e.g., move a record type), you often have to change the application programs that access it because the navigation path has changed.
- Relational Model: Has high data independence. The logical structure of the data (tables) is separate from its physical storage. You can add new columns, change the order of columns, or add new tables without breaking most existing application code.
6. Complexity
- Hierarchical Model: The concept of a tree is simple to understand, but the implementation and data manipulation are complex for programmers, as they need to manage the pointers and navigation paths.
- Relational Model: The concept of tables is intuitive and easy to understand. The use of SQL makes data manipulation much simpler for both developers and end-users.
Summary Table
| Feature | Hierarchical Model | Relational Model |
| :--- | :--- | :--- |
| Basic Structure | Tree (Parent-Child nodes) | Tables (Rows and Columns) |
| Relationships | Strict One-to-Many (Parent-to-Child) | Flexible (One-to-One, One-to-Many, Many-to-Many) |
| Many-to-Many | Handled poorly, requires data duplication. | Handled efficiently with a linking table. |
| Data Redundancy | High, due to its rigid structure. | Low, due to normalization. |
| Data Access | Navigational (predefined paths). Inflexible. | Declarative (using SQL). Highly flexible. |
| Data Independence| Low. Changes to structure affect applications. | High. Structure can change without affecting applications. |
| Query Language | No standard, complex procedural access. | Standardized, powerful query language (SQL). |
| Example System | IBM's IMS | MySQL, PostgreSQL, Oracle, SQL Server |
Conclusion
The Relational Model largely superseded the Hierarchical Model because it is far more flexible, less redundant, and easier for both users and programmers to work with. While the hierarchical structure is efficient for very specific, well-defined data hierarchies, the relational model's ability to handle diverse and evolving data relationships makes it the standard for almost all modern database applications.