DBMS Concepts

DBMS Tutorial Components of DBMS. Applications of DBMS The difference between file system and DBMS. Types of DBMS DBMS Architecture DBMS Schema Three Schema Architecture. DBMS Languages.

DBMS ER Model

ER model: Entity Relationship Diagram (ERD) Components of ER Model. DBMS Generalization, Specialization and Aggregation.

DBMS Relational Model

Codd’s rule of DBMS Relational DBMS concepts Relational Integrity Constraints DBMS keys Convert ER model into Relational model Difference between DBMS and RDBMS Relational Algebra DBMS Joins

DBMS Normalization

Functional Dependency Inference Rules Multivalued Dependency Normalization in DBMS: 1NF, 2NF, 3NF, BCNF and 4NF

DBMS Transaction

What is Transaction? States of transaction ACID Properties in DBMS Concurrent execution and its problems DBMS schedule DBMS Serializability Conflict Serializability View Serializability Deadlock in DBMS Concurrency control Protocols

Difference

Difference between DFD and ERD

Misc

Advantages of DBMS Disadvantages of DBMS Data Models in DBMS Relational Algebra in DBMS Cardinality in DBMS Entity in DBMS Attributes in DBMS Data Independence in DBMS Primary Key in DBMS Foreign Key in DBMS Candidate Key in DBMS Super Key in DBMS Aggregation in DBMS Hashing in DBMS Generalization in DBMS Specialization in DBMS View in DBMS File Organization in DBMS What Is A Cloud Database What Is A Database Levels Of Locking In DBMS What is RDBMS Fragmentation in Distributed DBMS What is Advanced Database Management System Data Abstraction in DBMS Checkpoint In DBMS B Tree in DBMS BCNF in DBMS Advantages of Threaded Binary Tree in DBMS Advantages of Database Management System in DBMS Enforcing Integrity Constraints in DBMS B-Tree Insertion in DBMS B+ Tree in DBMS Advantages of B-Tree in DBMS Types of Data Abstraction in DBMS Levels of Abstraction in DBMS 3- Tier Architecture in DBMS Anomalies in Database Management System Atomicity in Database Management System Characteristics of DBMS DBMS Examples Difference between Relational and Non-Relational Databases Domain Constraints in DBMS Entity and Entity set in DBMS ER Diagram for Banking System in DBMS ER Diagram for Company Database in DBMS ER Diagram for School Management System in DBMS ER Diagram for Student Management System in DBMS ER Diagram for University Database in DBMS ER Diagram of Company Database in DBMS Er Diagram Symbols and Notations in DBMS How to draw ER-Diagram in DBMS Integrity Constraints in DBMS Red-Black Tree Deletion in DBMS Red-Black Tree Properties in DBMS Red-Black Tree Visualization in DBMS Redundancy in Database Management System Secondary Key in DBMS Structure of DBMS 2-Tier Architecture in DBMS Advantages and Disadvantages of Binary Search Tree Closure of Functional Dependency in DBMS Consistency in Database Management System Durability in Database Management System ER Diagram for Bank Management System in DBMS ER Diagram for College Management System in DBMS ER Diagram for Hotel Management System in DBMS ER Diagram for Online Shopping ER Diagram for Railway Reservation System ER Diagram for Student Management System in DBMS Isolation in DBMS Lossless Join and Dependency Preserving Decomposition in DBMS Non-Key Attributes in DBMS Data Security Requirements in DBMS DBMS functions and Components What is Homogeneous Database? DBMS Functions and Components Advantages and Disadvantages of Distributed Database

Redundancy in Database Management System

Redundancy in a database management system (DBMS) refers to the duplication of data within the database. This duplication can occur in multiple ways, such as having multiple copies of the same data in different tables, or having multiple instances of the same data stored in the same table. Redundancy can lead to several issues, such as inconsistencies in the data, difficulties in maintaining the accuracy and consistency of the data, and increased storage requirements.

There are several reasons why redundancy might be introduced into a database:

  • One common reason is to improve performance, by creating denormalized tables that store frequently accessed data in a single location.
  • Another reason is to improve data availability and fault tolerance, by creating redundant copies of the data that can be used in case of system failures or other disruptions.

Overall redundancy can be seen as both advantage as well as a disadvantage for the DBMS, tradeoff must be done between the storage requirement and the performance of DBMS.

Redundancy can be reduced by using a technique called normalization, which involves organizing the data in the database into separate tables according to their logical relationships, and removing duplicate data. This can improve the efficiency and flexibility of the database, but it also increases the complexity of the system and can negatively impact performance.

Normalization is a technique used in database design to eliminate redundancy and improve the efficiency, flexibility, and accuracy of the data. It involves organizing the data in a database into separate tables according to their logical relationships, and removing duplicate data. Normalization is typically done using a set of rules called normal forms, which specify the conditions that a table must meet to be considered "normalized". By implementing normalization, the DBMS can help increase the data integrity and consistency, making the data more flexible and accurate, and also help in easier maintenance of data. However, it also increases the complexity of the system and can negatively impact performance, as it requires more joins to retrieve the data.

 The most common normal forms are as:

  • 1st Normal Form (1NF): A table is in 1NF if it contains no repeating groups. This means that each row in the table represents a single, unique entity, and that each column in the table contains only atomic (indivisible) values.
  • 2nd Normal Form (2NF): A table is in 2NF if it is already in 1NF and all non-key columns are functionally dependent on the primary key. This means that the values in any non-key column are dependent on the values in the primary key, and cannot be determined by any other columns in the table.
  • 3rd Normal Form (3NF): A table is in 3NF if it is already in 2NF and all non-key columns are not transitively dependent on the primary key. This means that the values in any non-key column do not depend on any non-key columns in the table, only on the primary key.

Therefore, it is important to find a balance between the storage requirement and the performance of DBMS. Normalization technique should be used after taking into account the trade-offs and performance requirements of the system and the DBMS.

Advantage of Normalization in Redundancy in DBMS

Normalization is a technique that is used to eliminate redundancy in a database management system (DBMS) and improve the efficiency, flexibility, and accuracy of the data. Other advantages of normalization in redundancy in DBMS include as:

  1. Data Integrity: Normalization helps to ensure data integrity by eliminating duplicate data and ensuring that data is stored in a consistent and accurate manner.
  2. Data Consistency: Normalization helps to ensure data consistency by enforcing relationships between tables and preventing data from being stored in multiple, inconsistent forms.
  3. Ease of Data Modification: Normalization simplifies the process of making changes to the data.

Disadvantage of Normalization in Redundancy in DBMS   

One disadvantage of normalization in a database management system (DBMS) is that it can lead to increased redundancy. This occurs because normalization often involves breaking up data into multiple, separate tables in order to eliminate data duplication and redundancy. However, this can also lead to a higher number of table joins being required to retrieve all of the necessary data for a given query, which can slow down query performance. Additionally, since the same data is now stored in multiple places, updating the data can be more complex and time-consuming.

Features of Redundancy in Database Management System

In a database management system (DBMS), redundancy refers to the duplication of data in multiple places. This can occur for a number of reasons, such as for data integrity, for performance reasons, or to support data security. Some examples of redundancy features in a DBMS include as:

1. Data Replication: This is the process of copying data from one database to another, either within the same DBMS or across different DBMSs. This can be used to improve performance by distributing data across multiple servers, or to ensure that data is available in the event of a failure. Data replication also refers to the process of creating and maintaining multiple copies of a database on different servers or devices. This is done for the purpose of providing redundancy, which means that if one copy of the database becomes unavailable, another copy can be used to ensure that the system continues to operate. There are different types of data replication, including synchronous and asynchronous replication.
In synchronous replication, changes made to one copy of the database are immediately propagated to all other copies. In asynchronous replication, changes are propagated at a later time.

Data replication can also be done in a variety of ways, including master-slave replication, peer-to-peer replication, and multi-master replication.

  • In master-slave replication, one server is designated as the master and all other servers are designated as slaves. The master server is responsible for propagating changes to the slaves.
  • In peer-to-peer replication, all servers are equal and changes can be made to any server.
  • Multi-master replication is a combination of both, where all servers can be a master and all servers are aware of each other and changes made on any server will be replicated to all other servers.

Data replication can be useful for many reasons, such as improving the performance of the system by distributing the load across multiple servers, increasing the availability of the system by providing redundancy, and providing disaster recovery capabilities by allowing the system to continue operating even if one or more servers fail.

2. Backup and Recovery: A DBMS will often include mechanisms for creating backups of the database, which can be used to restore the database in the event of a failure. This can include both full backups, which copy all of the data in the database, and incremental backups, which copy only changes made since the last backup. Backup refers to the process of copying and saving the database to a separate location, such as an external hard drive or cloud storage, so that it can be restored in case of a failure. This is typically done on a regular schedule, such as daily or weekly. Recovery refers to the process of restoring the database to a previous state using the backup. This can be done in the event of a system failure, data loss, or other disaster. There are different types of backup and recovery methods such as:

  • Full Back up: This method backs up the entire database.
  • Incremental Backup: This method backs up only the changes made to the database since the last backup.
  • Differential Backup: This method backs up all changes made to the database since the last full backup.

In summary, backup and recovery in redundancy in DBMS is the process of copying and saving the database to a separate location, and restoring it to a previous state in case of a system failure or data loss. This is done to provide a backup and ensure data availability.

3. Data encryption: To secure the data in the database, the DBMS may include features to encrypt the data. This can include both at-rest encryption, which encrypts data when it is stored on disk, and in-transit encryption, which encrypts data as it is transmitted over a network. Data encryption in a database management system (DBMS) refers to the process of converting plaintext data into an unreadable format, called ciphertext, using a mathematical algorithm, called a cipher. The process of converting ciphertext back to its original plaintext form is called decryption. When data encryption and redundancy are used together, it provides an added layer of security to the database. For example, if an attacker were to gain access to one of the redundant copies of the data, they would only be able to access the encrypted data, which would be unreadable without the decryption key. Additionally, even if an attacker were to corrupt one of the redundant copies of the data, the other copies would still be available for use, and the corrupted data could be replaced with the uncorrupted copies.

In the DBMS, Data encryption in redundancy is used in various applications such as Banking, Healthcare, Online Transactions, and in Government agencies where security of data is a major concern.

4. Data validation and integrity constraints: A DBMS will often include mechanisms for validating data as it is entered into the database, such as checking that values fall within a certain range or that they match a specific format. Additionally, integrity constraints can be used to ensure that relationships between data in the database are maintained, such as ensuring that a foreign key value exists in a related table. Data validation in a database management system (DBMS) is the process of ensuring that the data stored in the database adheres to a set of rules or constraints. This can include things like ensuring that a date is in the correct format, a value falls within a certain range, or a foreign key references a valid primary key in another table.

One example of how data validation and redundancy can be used together in a DBMS is in the case of a banking application. In this application, data validation would be used to ensure that all transactions adhere to regulations, such as preventing transactions that exceed a certain amount or that are from a blacklisted account. Redundancy could be used to store a copy of all transactions in multiple locations to ensure that the data is available even in the event of a failure of one of the database servers.

Disadvantages of Redundancy in Database Management System

The several disadvantages of redundancy in database management system (DBMS) are as below:

  • Increased complexity: Having multiple copies of the same data can make it more difficult to maintain consistency and integrity in the database. This makes the finding up of data in the database more typical and difficult. This increases the complex situation to search particular data items.
  • Complex database schema: As the number of tables, relationships, and constraints in a database increases, the schema can become increasingly complex, making it difficult to understand and maintain. The factors responsible for the complexity in database as:
    • Complex queries: As the number of queries and the complexity of those queries increases, the DBMS may struggle to execute them efficiently.
    • Complex data: As the volume and variety of data stored in the database increases, it can become more difficult to manage, query, and analyze that data.
    • Complex security: As the number of users and access levels increases, the security requirements of the DBMS can become more complex.
    • Complex data integration: As the number of data sources and types increases, it can become more difficult to integrate that data into the DBMS.
    • Complex scalability: As the number of users and the amount of data grows, it can become more difficult to scale the DBMS to meet the increased demand.
    • Complex data management: As the data becomes more complex and diverse, it can become more difficult to manage it, such as maintaining data integrity, enforcing data constraints, and optimizing data performance. It is important to consider the complexity of a DBMS in its design and implementation, and to have a plan in place for managing and mitigating complexity as it increases over time."
  • Reduced performance: Querying and updating redundant data can take longer than querying and updating non-redundant data, which can lead to slower performance overall. There are several potential causes of reduced performance in a database management system (DBMS), including:
    • If the DBMS is running on hardware that is not powerful enough to handle the workload, performance will likely suffer.
    • Poorly designed database schema: If the database schema is not optimized for the queries that will be run against it, performance will suffer.
    • Indexing and caching: if the indexes and caching is not implemented properly, it could cause slow performance.
    • Poorly written SQL queries: If the SQL queries being run against the database are not written efficiently, performance will suffer.

High concurrency: If there are too many users trying to access the database at the same time, performance will suffer.

Lack of proper maintenance: If the database is not properly maintained, such as running regular backups and optimizing the database, performance will suffer over time.

Network latency: If the database server is located far away from the application server and there is high network latency, it could cause performance issues

Blocking and Deadlock: If there are blocking and deadlock conditions in the database, it could cause performance issues.

Insufficient memory: If the database server does not have enough memory to handle the workload, it could cause performance issues. It is important to identify the specific issue causing the reduced performance and address it accordingly.

  • Increased storage space requirements: Storing redundant data takes up more space on the database server, which can become a problem as the database grows in size. This increases the complex storage space for the data storing in the database system.
  • Increased risk of data inconsistencies: With multiple copies of the same data, it can be harder to ensure that all copies are kept up-to-date and consistent with each other.
  • Increased cost: Storing and maintaining redundant data can increase the overall cost of the database system. Increased cost in a DBMS can be caused by a variety of factors, such as an increase in the amount of data being stored, more complex queries being run, or a lack of proper indexing and optimization. Additionally, the cost of hardware and maintenance for the DBMS may also contribute to increased costs. To mitigate these costs, it may be necessary to scale up hardware resources, optimize database design and queries, or consider using a more cost-effective DBMS solution.

Advantages of Redundancy in Database Management System

The Redundancy in a database management system (DBMS) can have several advantages, some including as:

  • Improved data availability: Redundant copies of data can be used to ensure that the data is still accessible even if one copy is lost or becomes unavailable. Improved data availability in a DBMS refers to the ability of the system to ensure that data is accessible and usable even in the event of failures or other disruptions. This can be achieved through various techniques such as replication, backup and recovery, and failover mechanisms. Replication involves creating multiple copies of the data and distributing them across different servers or locations. This ensures that if one copy becomes unavailable, the others can be used to provide continued access to the data.
    Backup and recovery involve creating regular copies of the data and storing them in a separate location. This allows the system to restore the data in the event of a failure or other disruption. Failover mechanisms involve automatically switching to a secondary system or server in the event of a failure or disruption. This ensures that the system can continue to provide access to the data even if the primary system is unavailable. Overall, improved data availability in a DBMS is critical for ensuring the reliability and uptime of the system, and for meeting the needs of organizations that rely on the data stored in the system for their operations.
  • Improved data integrity: Redundant copies of data can be used to detect and correct errors in the data.
  • Improved performance: Redundant copies of data can be used to distribute the load of accessing the data across multiple servers, improving overall performance. Improved performance in a DBMS refers to the ability of the system to handle large amounts of data and perform complex operations quickly and efficiently. This can be achieved through various techniques such as indexing, caching, and optimization of query execution. Indexing involves creating a separate data structure that maps the data in the database to specific locations in memory or on disk. This allows the system to quickly locate and retrieve specific records without having to search through the entire database.

Caching involves storing recently accessed data in memory so that it can be quickly retrieved again without having to go back to disk. This can significantly improve performance for frequently accessed data. Overall, improved performance in a DBMS is critical for meeting the needs of organizations that rely on the data stored in the system for their operations. It also allows the system to handle large amounts of data, complex queries, and support multiple concurrent users with minimal latency.

  • Improved security: Redundant copies of data can be used to ensure that the data is still accessible even if one copy is lost due to security breaches. Authentication involves verifying the identity of users who are trying to access the system. This can be done through methods such as usernames and passwords, or more advanced methods like biometric identification or multi-factor authentication. Authorization involves controlling what actions a user can perform on the data stored in the system. This can be done through role-based access control, which assigns specific roles and permissions to users based on their job functions or responsibilities. Encryption involves converting data into a coded format that can only be decrypted by authorized users. This can be used to protect sensitive data, such as credit card numbers or personal information, from unauthorized access or disclosure.
  • Improved Disaster recovery: Redundant copies of data can be used to recover data in case of natural disasters or other unexpected events.