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Are you struggling with messy, inconsistent data in your database? Do duplicate entries and anomalies slow down your system?
Decomposition in DBMS is your solution.
In DBMS, decomposition can be defined as an approach of subdividing a large table into even smaller ones so as to solve complex and large problems. This practice works towards the improvement of quality, reduction of time and effort, as well as eradication of multiple entries of similar data.
In this blog, we will look at what decomposition in DBMS is, why it matters, different types, properties, and more advanced concepts in decomposition and look at the strengths and weaknesses as well.
To effectively manage the cleanliness of the database, decomposition plays an important role. The large tables can be divided into smaller ones, which will allow you to avoid any discrepancies in the data.
Here’s why decomposition matters:
Imagine managing a library. You don’t have one enormous list of all books but rather have subdivisions based on the genre: fiction, non-fiction, etc. This makes it easy for youths to locate and organise books. This makes it easier to find and manage books. Similarly, decomposition organises data in a database.
Also Read: Database Languages in DBMS
Understanding the types of decomposition is key to applying the right one for your needs. We can categorise decomposition into two types:
Let’s understand them one by one.
Lossless decomposition means splitting a table without losing any data. When we join the decomposed tables, we get back to the original table.
This is like cutting a cake into slices and then putting it back together perfectly.
Example
Consider a table StudentCourse:
StudentID | StudentName | CourseID | CourseName |
1 | Divya | 101 | Math |
2 | Rahul | 102 | Science |
We decompose it into:
1. Student Table:
StudentID | StudentName | CourseID |
1 | Divya | 101 |
2 | Rahul | 102 |
2. Course Table:
CourseID | CourseName |
101 | Math |
102 | Science |
By joining these on CourseID, we get the original StudentCourse table back.
Here’s how to do it in Python:
Output:
Lossy decomposition might result in losing some data when we split and then rejoin tables. This is like cutting a cake and losing some crumbs.
Example
Consider a table EmployeeProject:
EmpID | EmpName | ProjectID | Hours |
1 | Amit | 201 | 20 |
2 | Anamika | 202 | 30 |
We decompose it into:
1. Employee Table:
EmpID | EmpName |
1 | Amit |
2 | Anamika |
2. Project Table:
ProjectID | Hours |
201 | 20 |
202 | 30 |
Rejoining these might not accurately recreate the original EmployeeProject table.
Here’s how a lossy decomposition might look in Python:
Output:
Here, the original relationships between employees and projects are lost, leading to incorrect data.
When decomposing tables, keeping these properties in mind ensures success.
A lossless join is critical. It means that when we join decomposed tables, we should get back to the original table exactly as it was. Here’s how we can ensure a lossless join:
Dependency preservation ensures that all functional dependencies are maintained in the decomposed tables. This is important for maintaining the logical relationships between data.
Reducing redundancy means eliminating duplicate data entries. This makes the database more efficient and easier to manage.
By decomposing tables, we can improve database performance. Smaller, well-structured tables make queries and updates faster and more efficient.
Decomposition ensures that data remains accurate and consistent. By breaking down tables, we can manage data anomalies better and ensure consistency across the database.
Lossless decomposition ensures that no data is lost when splitting a table. It’s like cutting a cake and being able to piece it back together perfectly. But how do we ensure this?
For a decomposition to be lossless, it must meet these criteria:
These criteria ensure that we can perfectly recreate the original table by joining the decomposed tables.
Imagine a table StudentCourse with columns: StudentID, StudentName, CourseID, and CourseName. Here’s what the table looks like:
StudentID | StudentName | CourseID | CourseName |
1 | Neha | 101 | Math |
2 | Rohan | 102 | Science |
We can decompose it into two tables:
1. Student Table:
StudentID | StudentName | CourseID |
1 | Neha | 101 |
2 | Rohan | 102 |
2. Course Table:
CourseID | CourseName |
101 | Math |
102 | Science |
When we join these tables on CourseID, we can reconstruct the original StudentCourse table perfectly.
Here’s how you can do it with Python:
Output:
Lossy decomposition can lead to data loss. In database terms, this means losing some information when tables are split and rejoined.
Signs of lossy decomposition include:
Consider a table EmployeeProject:
EmpID | EmpName | ProjectID | Hours |
1 | Navin | 201 | 20 |
2 | Nancy | 202 | 30 |
If we decompose it into:
1. Employee Table:
EmpID | EmpName |
1 | Navin |
2 | Nancy |
2. Project Table:
ProjectID | Hours |
201 | 20 |
202 | 30 |
We lose the direct relationship between employees and their projects. Here’s a Python example:
Output:
To keep your data clean and efficient, we can use advanced techniques like BCNF, 4NF, 3NF, and 2NF. These methods help ensure our data is well-organised and free from anomalies.
BCNF addresses redundancy and dependency issues more rigorously than 3NF. In BCNF, every determinant must be a candidate key. This means any attribute that determines another attribute must uniquely identify a row.
Example of BCNF
Consider a table InstructorCourse:
InstructorID | CourseID | Room |
1 | 101 | A101 |
1 | 102 | B202 |
2 | 101 | A101 |
To decompose it to BCNF:
1. Instructor Table:
InstructorID | CourseID |
1 | 101 |
1 | 102 |
2 | 101 |
2. Room Table:
CourseID | Room |
101 | A101 |
102 | B202 |
4NF deals with multi-valued dependencies. A table in 4NF has no multi-valued dependencies, ensuring cleaner data.
Example of 4NF
Consider a table EmployeeSkills:
EmpID | Skill | ProjectID |
1 | Python | 301 |
1 | SQL | 302 |
2 | Python | 301 |
To decompose it to 4NF:
1. EmployeeSkills Table:
EmpID | Skill |
1 | Python |
1 | SQL |
2 | Python |
2. EmployeeProjects Table:
EmpID | ProjectID |
1 | 301 |
1 | 302 |
2 | 301 |
3NF ensures that non-key attributes depend only on the primary key. This reduces transitive dependencies, making the data more straightforward.
Example of 3NF
Consider a table EmployeeDetails:
EmpID | EmpName | DeptID | DeptName |
1 | Preksha | 101 | HR |
2 | Murali | 102 | Finance |
To decompose it to 3NF:
1. Employee Table:
EmpID | EmpName | DeptID |
1 | Preksha | 101 |
2 | Murali | 102 |
2. Department Table:
DeptID | DeptName |
101 | HR |
102 | Finance |
2NF eliminates partial dependencies. Every non-key attribute must be fully dependent on the primary key, not just part of it.
Example of 2NF
Consider a table CourseEnrollment:
StudentID | CourseID | Grade |
1 | 101 | A |
2 | 102 | B |
To decompose it to 2NF:
1. Enrollment Table:
StudentID | CourseID |
1 | 101 |
2 | 102 |
2. Grades Table:
CourseID | Grade |
101 | A |
102 | B |
Decomposition breaks down large tables into smaller ones, removing duplicate data entries. This saves storage space and makes data management easier.
Smaller, focused tables help maintain data accuracy. When each table serves a specific purpose, errors become easier to spot and correct.
Decomposition speeds up database queries. When data is organised into smaller, logical tables, the database can find information faster.
Updating data becomes simpler with decomposition. Changes need to be made in only one place, reducing the risk of errors.
Decomposition allows for a more flexible database design. You can add or remove tables without disrupting the entire database.
Decomposed tables can make querying more complex. You might need to join multiple tables to get the information you need.
Improper decomposition can lead to data loss. It’s crucial to ensure that all necessary relationships are preserved.
More tables mean more storage. Even though decomposition reduces redundancy, it can increase the overall number of tables, which requires more storage space.
Maintaining dependencies between tables can be tricky. If not managed well, it can lead to data inconsistencies.
Also Read: Transaction in DBMS
We looked at the vital function of decomposition in DBMS in this blog.
We’ve seen how dividing huge tables into smaller, more manageable ones may assist in improving data integrity, decreasing data redundancy, and increasing query performance.
We examined both lossless and lossy decomposition, understanding their importance and potential pitfalls.
Advanced decomposition techniques, such as BCNF, 4NF, 3NF, and 2NF, ensure our databases remain efficient and free from anomalies.
By carefully implementing decomposition, we can maintain a clean, efficient, and flexible database system. The key is to balance the benefits while addressing the challenges to achieve optimal database management.
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