Last Updated on April 17, 2023 by Prepbytes
DBMS introduced lossless decomposition to eliminate redundancy and improve the efficiency of the database. By breaking down a large table into smaller tables, the amount of data duplication is reduced, which in turn reduces storage space requirements and improves query processing time.
What is Decomposition?
In computer science, decomposition refers to breaking down a complex problem or program into smaller, more manageable parts, which can be solved or implemented separately. This approach is often used in software engineering to create modular, reusable code.
What is Lossless Decomposition in DBMS?
Lossless decomposition in DBMS is a technique used in database management systems to break down a large table into smaller tables while preserving all the information contained in the original table. This means that no data is lost during the decomposition process. The goal of lossless decomposition is to eliminate redundancy and improve the efficiency of the database. It involves identifying and separating out functional dependencies within the table so that each smaller table contains only the relevant information for a particular purpose.
Types of Lossless Decomposition in DBMS
In DBMS, there are two main types of lossless decomposition:
- Dependency preserving decomposition: In this type of decomposition, all the functional dependencies that exist in the original relation are preserved in the smaller relations. This ensures that the smaller relations maintain data integrity and can be used to reconstruct the original relation. Dependency-preserving decomposition can be achieved using the Boyce-Codd Normal Form (BCNF) or the Third Normal Form (3NF).
- Join decomposition: In this type of decomposition, the original relation is decomposed into smaller relations based on the functional dependencies that exist in the original relation. These smaller relations can then be joined back together to reconstruct the original relation without losing any data. Join decomposition can be achieved using the Fourth Normal Form (4NF) or the Fifth Normal Form (5NF).
Conditions Required in Lossless Decomposition in DBMS
To ensure a lossless decomposition, the following conditions must be met:
- Dependency Preservation: The dependencies between the attributes of the original relation must be preserved in the decomposed relations. In other words, all functional dependencies in the original relation must be represented in the decomposed relations.
- Join Preservation: The decomposition should not result in the loss of any information that could be obtained by joining the decomposed relations back together. This means that the original relation should be reconstructed without any loss of data.
- Minimal Redundancy: The decomposed relations should not contain any redundant data. This means that each attribute should be represented in only one relationship and that there should be no unnecessary duplication of information.
- Lossless-join Property: The decomposed relations should have the property that when joined together, they produce the original relation. This means that there should be no loss of information when the relations are joined back together.
Example for Lossless Decomposition in DBMS
Consider the following table:
Employee (EmpID, EmpName, EmpAddress, DeptID, DeptName, Salary)
Functional Dependencies:
EmpID → EmpName, EmpAddress
DeptID → DeptName, SalaryUsing lossless decomposition, we can break down the Employee table into two smaller tables:
Table 1: EmployeeDetails (EmpID, EmpName, EmpAddress, DeptID)
| EmpID | EmpName | EmpAddress | DeptID |
|---|---|---|---|
| 1 | John | 123 Main St | 101 |
| 2 | Jane | 456 Oak St | 102 |
| 3 | Bob | 789 Elm St | 101 |
Table 2: DepartmentDetails (DeptID, DeptName, Salary)
| DeptID | DeptName | Salary |
|---|---|---|
| 101 | Sales | 50000 |
| 102 | Marketing | 60000 |
Explanation:
In the above example, we have an Employee table with six attributes: EmpID, EmpName, EmpAddress, DeptID, DeptName, and Salary. We have identified the functional dependencies in this table and found that EmpID determines EmpName and EmpAddress, while DeptID determines DeptName and Salary. Using lossless decomposition, we have broken down the Employee table into two smaller tables: EmployeeDetails and DepartmentDetails.The EmployeeDetails table contains the attributes EmpID, EmpName, EmpAddress, and DeptID, while the DepartmentDetails table contains the attributes DeptID, DeptName, and Salary. Both tables are now free of any data redundancy and any updates or modifications can be made to the smaller tables rather than the entire original table. We can also combine these tables using the common attribute DeptID, to retrieve the original Employee table. Thus, lossless decomposition has preserved all the information in the original table while improving efficiency and reducing redundancy.
Advantages of Lossless Decomposition in DBMS
Here are some advantages of lossless decomposition in DBMS:
- It eliminates data redundancy
- Improves the efficiency of the database
- Reduces storage space requirements
- Enables effective maintenance of the database
- Preserves all the information from the original table
- Prevents data inconsistencies
Disadvantages of Lossless Dcomposition in DBMS
Here are some disadvantages of lossless decomposition in DBMS:
- Increases complexity of the database
- Not always feasible to achieve
- The retrieving original table can be complex
- May require additional storage space
Conclusion
In conclusion, lossless decomposition in DBMS is important by identifying functional dependencies within the original table and creating smaller tables based on those dependencies, redundancy is eliminated and data consistency is improved. Lossless decomposition in DBMS also enables effective maintenance of the database, resulting in more accurate and reliable data.
Frequently Asked Questions(FAQs)
Q1. What is the difference between lossy and lossless decomposition in DBMS?
Ans: Lossless decomposition ensures that all the information from the original table is preserved after the decomposition, while lossy decomposition may result in the loss of some information during the decomposition process.
Q2. How is lossless decomposition achieved in DBMS?
Ans: Lossless decomposition is achieved by identifying and separating out functional dependencies within the table so that each smaller table contains only the relevant information for a particular purpose.
Q3. What are the benefits of lossless decomposition in DBMS?
Ans: The benefits of lossless decomposition include reducing redundancy and improving the efficiency of the database, reducing storage space requirements, improving query processing time, and enabling effective maintenance of the database.
Q4. What are the limitations of lossless decomposition in DBMS?
Ans: Lossless decomposition can lead to an increase in the number of tables, which can make the database more complex and difficult to manage. Moreover, it may not always be possible to achieve lossless decomposition in certain scenarios.
Q5. Can lossless decomposition result in any data inconsistencies in DBMS?
Ans: No, lossless decomposition does not result in any data inconsistencies as all the information from the original table is preserved during the decomposition process. However, it is important to ensure that the smaller tables are properly joined together to retrieve the original table without any inconsistencies.