Relational decomposition is a process in database design that involves breaking down a single relation or table into multiple smaller relations or tables. The purpose of decomposition is to eliminate redundancy, improve data integrity, and ensure efficient data management.
There are different types of decomposition techniques, but the most commonly used one is called normalization. Normalization follows a set of rules known as normal forms, which guide the decomposition process. The most widely known normal forms are:
- First Normal Form (1NF): It requires that each attribute in a relation contains only atomic values, meaning that it cannot be further divided. It also states that each attribute should have a unique name, and there should be a primary key that uniquely identifies each tuple (row) in the relation.
- Second Normal Form (2NF): It builds upon 1NF and requires that every non-key attribute in a relation is fully functionally dependent on the entire primary key. In other words, if a relation has a composite primary key, each non-key attribute must depend on the entire composite key, not just a part of it.
- Third Normal Form (3NF): It extends the requirements of 2NF and states that no non-key attribute should be transitively dependent on the primary key. Transitive dependency occurs when an attribute depends on another attribute that is not part of the primary key. To eliminate transitive dependencies, the relation needs to be decomposed further.
Other normal forms, such as Boyce-Codd Normal Form (BCNF) and Fourth Normal Form (4NF), provide more advanced guidelines for decomposition, addressing more complex dependency scenarios.
The decomposition process involves analyzing the functional dependencies within a relation and identifying potential anomalies or redundancies. By decomposing the relation into smaller relations that adhere to the desired normal form, we can ensure data integrity, minimize redundancy, and optimize database operations.
It’s important to note that decomposition is not a one-size-fits-all solution, and different decomposition strategies may result in different sets of smaller relations. The choice of decomposition technique depends on the specific requirements and characteristics of the data being modeled.
Types of Decomposition:
In the context of database design, there are primarily two types of decomposition: functional decomposition and normalization. Let’s explore each type in more detail:
- Functional Decomposition: Functional decomposition involves breaking down a complex system or process into smaller, more manageable sub-functions or modules. This type of decomposition is often used in system analysis and design, where the focus is on understanding the functionality and behavior of the system.
Functional decomposition helps in simplifying complex systems by dividing them into smaller parts, which are easier to understand, develop, and maintain. Each sub-function or module performs a specific task and can be independently analyzed and designed.
- Normalization: Normalization is a technique used in database design to decompose a relation or table into smaller relations, eliminating redundancy and improving data integrity. It follows a set of rules called normal forms, which guide the decomposition process.
The common normal forms used in database normalization are:
- First Normal Form (1NF): Ensures that each attribute in a relation contains only atomic values and there is a unique identifier for each tuple.
- Second Normal Form (2NF): Ensures that non-key attributes are fully functionally dependent on the entire primary key.
- Third Normal Form (3NF): Ensures that no non-key attribute is transitively dependent on the primary key.
- Boyce-Codd Normal Form (BCNF): A stronger form of 3NF that deals with more complex dependency scenarios.
- Fourth Normal Form (4NF) and higher: Address more specific and advanced dependency scenarios.
Normalization aims to reduce data redundancy, eliminate anomalies (such as insertion, update, and deletion anomalies), and ensure data integrity by structuring the database schema in a logical and efficient manner.
It’s important to note that functional decomposition and normalization are not mutually exclusive. In fact, functional decomposition is often used as a preliminary step in database design to identify the functional dependencies and guide the normalization process. The ultimate goal of both types of decomposition is to create a well-structured, efficient, and maintainable system or database.
Lossless decomposition, also known as lossless join decomposition, is a property of decomposition in database design. A decomposition is considered lossless when the original information can be reconstructed exactly by joining the smaller relations resulting from the decomposition. In other words, no information is lost during the decomposition process.
To achieve lossless decomposition, the decomposition must satisfy the lossless join property. The lossless join property ensures that when the smaller relations resulting from the decomposition are joined back together using a common attribute, the resulting relation will be equivalent to the original relation.
There are different techniques to achieve lossless decomposition, such as:
- Dependency preservation: If a decomposition preserves all functional dependencies of the original relation, it guarantees lossless join. This means that if the dependencies are preserved in the smaller relations, the original relation can be reconstructed by joining them.
- Boyce-Codd Normal Form (BCNF) decomposition: BCNF decomposition inherently guarantees lossless join. If a relation is decomposed into smaller relations that are in BCNF, then joining these smaller relations using the common attribute(s) will yield the original relation without any loss of information.
- Fourth Normal Form (4NF) decomposition: Similar to BCNF, a decomposition into 4NF relations ensures lossless join. If a relation is decomposed into smaller relations that are in 4NF, the original relation can be obtained by joining these smaller relations using the common attribute(s).
Lossless decomposition is an important property to ensure data integrity and consistency in a database. By preserving the original information during decomposition and enabling the reconstruction of the original relation, it avoids data loss or inconsistencies that could occur when manipulating the data across multiple relations.
Dependency preservation is a property of decomposition in database design, which ensures that all the functional dependencies present in the original relation are preserved in the resulting smaller relations after decomposition. In other words, the decomposition does not introduce any new functional dependencies or remove any existing ones.
Preserving dependencies is crucial to maintain data integrity and consistency in the database. It ensures that the information contained in the original relation can still be inferred from the smaller relations obtained through decomposition. By preserving dependencies, we can avoid data anomalies and inconsistencies when performing operations such as insertion, deletion, and modification.
To achieve dependency preservation during decomposition, various techniques can be employed, including:
- Dependency analysis: Before decomposing the original relation, a thorough analysis of its functional dependencies should be performed. This analysis helps in identifying the dependencies and understanding their nature (e.g., partial dependencies, transitive dependencies). By having a clear understanding of the dependencies, we can ensure their preservation during decomposition.
- Normalization: Normalization is a widely used technique to decompose a relation into smaller relations while preserving dependencies. By following the rules of normalization and progressively applying the normal forms (e.g., First Normal Form, Second Normal Form, Third Normal Form, etc.), we can ensure the preservation of functional dependencies.
- Functional dependency propagation: During decomposition, it is essential to propagate the functional dependencies from the original relation to the resulting smaller relations. This propagation involves assigning the appropriate dependencies to the smaller relations to maintain their integrity and ensure that all the original dependencies are preserved.
It’s important to note that while dependency preservation is a desirable property during decomposition, it is not always possible to achieve it fully. In some cases, decomposition may lead to loss of certain dependencies or the introduction of new dependencies. In such situations, trade-offs need to be made to balance between decomposition goals (such as normalization and performance optimization) and dependency preservation.