Good and Bad Decomposition in DBMS

What is Decomposition in DBMS?

Decomposition in database management systems (DBMS) is dividing a large table into smaller tables to increase data storage and retrieval efficacy and efficiency. This procedure entails identifying the functional dependencies inside the table and breaking it into smaller tables to reduce redundancy and guarantee data integrity.

Definition of Good Decomposition

A huge table is broken down into smaller tables to ensure data integrity, minimize redundancy, and enable effective data administration. This process is known as “good decomposition” in relational database management systems (DBMS).

Characteristics of Good Decomposition

• Minimal redundancy: A proper decomposition should eliminate unnecessary information and ensure that each table contains data on just one item.
• Preservation of dependencies: A successful decomposition should ensure that each table has all the essential data for each object it represents, as well as that the functional dependencies of the original table are preserved.
• Simplicity: A proper decomposition should have a clear relationship between the various tables and be simple and easy to grasp.
• Consistency: A successful decomposition should ensure that the relationships between tables are specified and consistent with the entire database design.

Benefits of Good Decomposition

Following are some advantages of effective decomposition in DBMS:

• Better data management: Effective decomposition simplifies data and enables quick retrieval and manipulation. In the long run, this makes database maintenance simpler.
• Better scalability: A good decomposition makes adding or removing tables simple without affecting the database's general structure. This makes scaling the database as necessary simpler.
• Improved data integrity: Data is stored in tables corresponding to their appropriate entities, and effective decomposition eliminates redundancy. Inconsistencies in data are reduced, and data integrity is enhanced as a result.
• Improved query performance: Faster query response times result from effective decomposition, making it more efficient to query databases.
• Easier maintenance: A good decomposition makes it simpler to maintain the database over time because individual table updates may be done without altering the database's general structure.

Example of good decomposition in a real-world scenario

Let's take an example of a company that manages customer orders and inventory. In the original table, all the information relating to orders and inventories is kept in one single table. This table is challenging to manage and query because it has redundant data and a messy structure.

This table should be divided into smaller tables that include data on particular entities as part of a good decomposition.

The table can be divided, for instance, into the following tables:

• Customer table: This table contains customer data, including names, addresses, and phone numbers.
• Order table: Customer order details, including the order ID, date, and customer ID, are listed.
• Order information table: The product ID, quantity, and price of each item in each order are all listed in this table.
• Inventory table: This table includes details on the products currently in stock, including the product ID, name, quantity, and price.

We have removed duplication by splitting the original table into smaller ones and ensuring that each table contains data about a particular object. Additionally, the tables are well-defined and related, making the database easier to manage and query.

For instance, we can link the order table with the order details table to retrieve details about a particular order. Similarly, we may query the inventory table to get details about a product in stock, enabling quicker response times and more effective database queries.

Best practices to achieve good decomposition in DBMS

The following best practices should be followed to obtain good decomposition in DBMS:

• Determine functional dependencies: It's crucial to determine the functional relationships between the attributes of a huge table before breaking it down. This makes it easier to choose which attributes to group into smaller tables.
• Normalize the tables: To eliminate duplicate information and guarantee data integrity, the tables should be normalized. To do this, the tables must be divided into smaller tables that each store data about a single object.
• Use correct primary and foreign keys: Each table needs a primary key to identify each record uniquely, and foreign keys are used to connect tables together.
• Maintain consistency: The deconstructed tables should be consistent with maintaining consistency with the overall database architecture and ensuring that the relationships between tables are clearly specified.
• Optimise for query performance: The deconstructed tables should be configured with the proper partitions and indexes to maximize query performance. This aids in enhancing database performance in general and query response times specifically.
• Document the design: The table structures and relationships in the database architecture should be explained in detail. It facilitates developer cooperation and aids in database maintenance over time.
• Test the design: To make sure the database design satisfies the specifications and operates as anticipated, it should be thoroughly tested. It entails evaluating the design using test data and confirming the validity of the findings.

Definition of Bad Decomposition

In DBMS, the process of dividing a large table into smaller tables in a way that results in data redundancy, inconsistency, and complexity are referred to as bad decomposition.

Risks and consequences of bad decomposition are as follows:

• Data inconsistency: When data is inconsistently decomposed, it may result in the same data being stored in different tables. Decision-making and data analysis errors may result from this.
• Slow query performance: Inefficient data management due to poor decomposition may result in data being stored in tables that are challenging to query. It may result in sluggish query execution times and subpar database performance.
• Poor data quality: Data in tables that do not match their corresponding entities due to inadequate decomposition may result in poor data quality. Inaccuracies in reporting and analysis may result from this.
• Difficulty in database management: It may be challenging to administer and maintain the database if there are complicated relationships between tables due to poor decomposition. Costs for development and maintenance may go up as a result.
• Challenges to security: Insecure table storage of sensitive data due to poor decomposition may provide security issues. Data breaches and other security incidents may result from this.

Example of bad decomposition in a real-world scenario

A client order table breakdown on an e-commerce website might be an example of poor decomposition in the real world.

Suppose the original table had the following characteristics:

Order ID
Customer Name
Customer Address
Customer Phone
Product ID
Product Name
Quantity
Order Date

Splitting this table into two tables, one carrying customer information and the other containing order information, without clearly demonstrating the relationship between them, would be a poor decomposition. For instance:

• Customer table: Customer ID, Customer Name, Customer Address, Customer Phone
• Order table: Order ID, Customer ID, Product ID, Quantity, Order Date

The following problems have arisen as a result of the poor decomposition in this instance:

• The customer table and the order table include copies of the customer information.
• The customer table and the order table need to be updated when a customer's information changes. Inconsistencies in the data may result if this is not done correctly.
• Because of the intricate interaction between the customer and order tables, it takes several joins to get the details of a given order.
• Repeated client information in the order table might lead to ineffective data management and more storage space being needed.
• Quickly getting information may be challenging due to the slow query performance caused by the tables' complicated relationship.

Common mistakes to avoid in the decomposition process

• Over-decomposition: Over-decomposition is one of the most frequent errors made during the decomposition process. It entails splitting up a table into an excessive number of smaller tables, which can result in inconsistent data, complexity, and inefficiency.
• Poor entity and relationship identification: Improper entity and relationship identification can lead to improper decomposition, which causes inconsistencies in the data and inefficiencies.
• Incomplete decomposition: Failure to divide a huge table into smaller tables results in incomplete decomposition, which can cause redundancy and inefficiency.
• Improperly established relationships: Inaccurately establishing links between tables can lead to discrepancies in data and inefficiency.
• Insufficient normalization: Data redundancies and inefficiencies result from inadequate normalization, which entails breaking normalization standards.
• Inadequate performance consideration: Inadequate performance consideration can lead to slow query performance and database inefficiencies.

Final observations and prospects for DBMS optimization by decomposition

To ensure that the database management system is effective, dependable, and simple to maintain, decomposition is a crucial component of database design and optimization. The significance of efficient decomposition techniques in DBMS optimization cannot be emphasized as data complexity rises and organizations produce larger volumes of data.

Applying artificial intelligence and machine learning in database optimization is one future trend that will likely emerge. The deconstruction process can be automated using these technologies, which can also be used to pinpoint problem areas and speed up database optimization.

Additionally, as more businesses switch to cloud-based solutions, new methods for database optimization in distributed systems will probably emerge. Data partitioning and distributed query processing may be used to ensure that the data is stored and retrieved effectively across several nodes.