In database design, decomposition is the process of breaking down a complex database into smaller, simpler components. Decomposition can be either lossless or lossy, depending on whether or not data is lost during the decomposition process.
Lossless decomposition is the process of breaking down a database into smaller components in a way that allows the original database to be reconstructed without losing any data. This is accomplished by using a process called “lossless join decomposition”, which involves creating tables that can be joined together to recreate the original database without losing any data.
Lossless decomposition is important because it ensures that the data in the decomposed tables is not lost during the decomposition process. It also ensures that the integrity of the data is preserved, as all the data in the decomposed tables is still dependent on the primary key and can be used to enforce the integrity of the data.
On the other hand, lossy decomposition is the process of breaking down a database in a way that results in the loss of some data. This can occur when attributes are split into separate tables and some of the data is not included in the decomposed tables.
Lossy decomposition is generally less desirable than lossless decomposition because it can result in the loss of important data. However, it may be necessary in some cases in order to eliminate redundancy and improve the efficiency of the database.
To determine whether a decomposition is lossless or lossy, you can use the concept of functional dependencies. A functional dependency is a relationship between two attributes in a database where the value of one attribute (the determinant) determines the value of another attribute (the dependent).
For example, consider a database with the following attributes:
- Employee ID (primary key)
- Employee name (determinant)
- Employee salary (dependent)
- Employee department (dependent)
In this database, the following functional dependencies exist:
- Employee ID -> Employee name
- Employee name -> Employee salary
- Employee name -> Employee department
If we decompose this database into the following two tables:
Employee:
- Employee ID (primary key)
- Employee name (determinant)
- Employee salary (dependent)
Department:
- Employee name (determinant)
- Employee department (dependent)
This decomposition would be lossless because all the data in the original database can be reconstructed by joining the two decomposed tables. However, if we decompose the database into the following two tables:
Employee:
- Employee ID (primary key)
- Employee name (determinant)
Department:
- Employee name (determinant)
- Employee salary (dependent)
- Employee department (dependent)
This decomposition would be lossy because the value of the Employee salary attribute is not included in either of the decomposed tables and would be lost during the decomposition process.
In general, it is important to use lossless decomposition whenever possible to ensure that the data in the decomposed tables is not lost during the decomposition process. However, in some cases, lossy decomposition may be necessary in order to eliminate redundancy and improve the efficiency of the database.