Relational Database Design

 

Relational Database Design

Relational database design focuses on structuring data effectively within a relational database system. The primary goal is to create a well-organized and efficient database that minimizes data redundancy, ensures data integrity, and supports efficient data retrieval.

Key Principles:

• Normalization: This is the core of relational database design. It involves organizing data into tables in a way that reduces redundancy and improves data integrity.
• 1NF (First Normal Form):
• Each cell contains only a single value.
• No repeating groups within a row.
• 2NF (Second Normal Form):
• In 1NF and every non-key attribute is fully functionally dependent on the entire primary key.
• 3NF (Third Normal Form):
• In 2NF and no non-key attribute is transitively dependent on the primary key.
• Entity-Relationship (ER) Modeling: A visual technique used to represent entities and their relationships. It helps in understanding the structure of the data and identifying the necessary tables and their relationships.
• Data Integrity Constraints: Rules that ensure the accuracy and consistency of data. These include:
• Primary Keys: Uniquely identify each row within a table.
• Foreign Keys: Establish relationships between tables.
• Unique Constraints: Ensure that a specific column or set of columns has unique values.
• Not Null Constraints: Ensure that a column cannot contain null values.
• Check Constraints: Enforce specific conditions on the data in a column.

Steps in the Design Process:

1. Requirements Gathering: Understand the business needs and the information that needs to be stored and retrieved.
2. Conceptual Design: Create an Entity-Relationship (ER) diagram to model the entities and their relationships.
3. Logical Design: Translate the ER diagram into a set of tables, defining columns, data types, and primary/foreign keys.
4. Normalization: Refine the table structure by applying normalization principles to minimize redundancy.
5. Physical Design: Consider performance factors, such as indexing, partitioning, and storage options, to optimize database performance.
6. Implementation: Create the database schema and populate it with data.
7. Testing and Maintenance: Thoroughly test the database and make necessary adjustments to ensure data integrity and performance.

Benefits of Good Relational Database Design:

• Reduced Data Redundancy: Minimizes storage space and improves data consistency.
• Improved Data Integrity: Ensures data accuracy and reliability.
• Enhanced Query Performance: Enables faster data retrieval and improved system performance.
• Increased Maintainability: Makes it easier to modify and update the database schema as business needs evolve.

By following sound design principles, you can create a well-structured and efficient relational database that effectively supports your organization's data management needs.

Normalization in database design is the process of organizing data within tables in a way that minimizes redundancy and ensures data integrity. It involves breaking down large tables into smaller, more manageable tables, establishing clear relationships between them.

Key Objectives of Normalization:

• Reduce Data Redundancy:
• Minimizes storage space.
• Reduces the risk of inconsistencies when updating data.
• If a piece of data is stored only once, any changes need to be made in only one place.
• Improve Data Integrity:
• Ensures data accuracy and consistency across the database.
• Reduces the likelihood of data anomalies (insertions, deletions, and updates) that can occur when data is redundant.
• Enhance Data Maintainability:
• Makes it easier to modify and update the database schema as business needs evolve.
• Simplifies data maintenance tasks.

Normal Forms:

Normalization is typically achieved through a series of normal forms:

• 1NF (First Normal Form):
• Atomicity: Each cell in the table contains only a single value.
• No Repeating Groups: Avoids storing multiple values within a single cell.
• Example: If a table stores multiple phone numbers for a customer in a single column, it violates 1NF.
• 2NF (Second Normal Form):
• 1NF: Must satisfy 1NF.
• Eliminates Partial Dependency: Every non-key attribute must depend on the entire primary key.
• Example: If a table stores order details and customer information, and the customer address depends only on the customer ID (part of the primary key), it violates 2NF.
• 3NF (Third Normal Form):
• 1NF and 2NF: Must satisfy 1NF and 2NF.
• Eliminates Transitive Dependency: No non-key attribute should depend on another non-key attribute.
• Example: If a table stores order details and the shipping region, and the shipping region depends on the state (another non-key attribute), it violates 3NF.

Benefits of Normalization:

• Reduced Storage Space: Less space is required to store data due to the elimination of redundancy.
• Improved Data Integrity: Ensures data accuracy and consistency.
• Enhanced Data Maintainability: Easier to modify and update the database schema.
• Improved Query Performance: Can lead to faster query execution in some cases.

Note: While normalization is generally beneficial, there can be trade-offs. Over-normalization can sometimes lead to more complex queries and increased overhead due to the need to join multiple tables. Therefore, it's important to strike a balance between normalization and performance requirements.

By following normalization principles, database designers can create well-structured and efficient databases that support the needs of their applications.

ER Modeling in Database Design

What is an ER Model?

• Entity-Relationship (ER) Model: A high-level, conceptual data model used to represent and describe relationships between real-world entities or concepts within a database.
• ER Diagram: A visual representation of the ER Model, utilizing boxes, symbols, and connectors to illustrate the structure.

Key Components of an ER Diagram:

1. Entities:
• Represent real-world objects or concepts (e.g., Customers, Products, Orders).
• Depicted as rectangles.
2. Attributes:
• Properties or characteristics of an entity (e.g., CustomerName, ProductPrice).
• Represented within the entity rectangle.
3. Relationships:
• Associations between entities (e.g., a Customer can place many Orders).
• Represented by lines or diamonds connecting entities.
• Cardinality: Defines the number of instances of one entity that can be associated with another.
• One-to-One: One instance of entity A is associated with one instance of entity B.
• One-to-Many: One instance of entity A is associated with ¹ multiple instances of entity B.  
• Many-to-One: Multiple instances of entity A are associated with one instance of entity B.
• Many-to-Many: Multiple instances of entity A are associated with multiple instances of entity B.

Example:

Consider a simple e-commerce scenario:

• Entities: Customers, Products, Orders
• Relationships:
• A Customer can place many Orders (One-to-Many).
• An Order can include many Products (Many-to-Many).

1. One-to-One Relationship

• Definition: For each record in one table, there is exactly one corresponding record in another table, and vice-versa.
• Example:
• Scenario: A company may have a table for employees and a separate table for their lockers. Each employee is assigned exactly one locker, and each locker is assigned to exactly one employee.
• Tables:
• Employees: EmployeeID (PK), EmployeeName, Department
• Lockers: LockerID (PK), EmployeeID (FK), Location
• Implementation: The EmployeeID in the Lockers table would be a foreign key referencing the EmployeeID in the Employees table.

2. One-to-Many Relationship

• Definition: In a one-to-many relationship, one record in the "one" table can be associated with multiple records in the "many" table, but each record in the "many" table is associated with only one record in the "one" table.

Example: Customers and Orders

• Scenario: A customer can place multiple orders, but each order belongs to only one customer.
• Tables:
• Customers:
• CustomerID (Primary Key)
• CustomerName
• Address
• Orders:
• OrderID (Primary Key)
• CustomerID (Foreign Key referencing Customers table)
• OrderDate
• TotalAmount
• Explanation:
• The Customers table represents individual customers.
• The Orders table represents orders placed by customers.
• The CustomerID in the Orders table is a foreign key that references the CustomerID in the Customers table.
• This foreign key establishes the one-to-many relationship:
• One customer can have many orders.
• Each order belongs to only one customer.

Visual Representation:

Customers (1) -----> Orders (Many)

• The line with the crow's foot symbol on the Orders side indicates the "many" side of the relationship.

Key Points:

• Foreign Key: The foreign key in the "many" table plays a crucial role in linking the two tables and enforcing the one-to-many relationship.
• Data Integrity: The foreign key constraint ensures that every order is associated with a valid customer.

3.Many-to-One Relationship

• Definition: In a many-to-one relationship, multiple records in one table can be associated with a single record in another table.
• Example: Employees and Departments
• Scenario: Multiple employees can belong to the same department, but each employee belongs to only one department.
• Tables:
• Employees:
• EmployeeID (Primary Key)
• EmployeeName
• DepartmentID (Foreign Key referencing Departments table)
• Departments:
• DepartmentID (Primary Key)
• DepartmentName
• Explanation:
• The Employees table represents individual employees.
• The Departments table represents different departments within the company.
• The DepartmentID in the Employees table is a foreign key that references the DepartmentID in the Departments table.
• This foreign key establishes the many-to-one relationship:
• Many employees can belong to the same department.
• Each employee belongs to only one department.

Visual Representation:

Employees (Many) -----> Departments (One)

• The line with the crow's foot symbol on the Employees side indicates the "many" side of the relationship.

Key Points:

• Foreign Key: The foreign key in the "many" table (Employees) plays a crucial role in linking the two tables and enforcing the many-to-one relationship.

 

4. Many-to-Many Relationship

• Definition: One record in one table can be associated with multiple records in another table, and vice-versa.
• Example:
• Scenario: Students can enroll in multiple courses, and each course can have multiple students enrolled.
• Tables:
• Students: StudentID (PK), StudentName
• Courses: CourseID (PK), CourseName
• StudentCourses: StudentID (FK referencing Students), CourseID (FK referencing Courses) (This is the junction table)
• Implementation: A third table, StudentCourses, is created to link the Students and Courses tables. This table typically has a composite primary key consisting of StudentID and CourseID.

 

Benefits of ER Modeling:

• Clearer Understanding: Provides a visual representation of the data structure, making it easier to understand relationships.
• Improved Design: Helps identify potential issues and inconsistencies early in the design process.
• Better Communication: Facilitates communication between database designers, developers, and business stakeholders.
• Foundation for Database Implementation: Serves as a blueprint for creating the actual database schema.

Tools for Creating ER Diagrams:

• Diagramming Software: Tools like Lucidchart, Draw.io, and Microsoft Visio can be used to create professional-looking ER diagrams.
• Database Design Tools: Some database management systems (DBMS) provide built-in tools for creating and visualizing ER diagrams.

By effectively utilizing ER modeling, database designers can create well-structured and efficient databases that accurately reflect the business requirements.

 Data Integrity: The foreign key ensures that each employee is associated with a valid department.

SQL Syntax for Creating Database and its objects

SQL (Structured Query Language) has a well-defined syntax for interacting with relational databases. Here's a breakdown of key elements:

Syntaxes are little different depending on type of software using for database design. Most commons are Oracle, SQL Server, MySql, Postgress, etc.

The syntax for creating a database in SQL generally follows this structure:

CREATE DATABASE database_name;

Example:

SQL

CREATE DATABASE my_new_database;

This statement will create a new database named "my_new_database".

Optional Clauses (may vary depending on the specific database system):

• IF NOT EXISTS: This clause prevents an error if a database with the same name already exists.

SQL

CREATE DATABASE IF NOT EXISTS my_new_database;

• Character Set and Collation: You can specify the character set and collation for the database.

SQL

CREATE DATABASE my_new_database

    CHARACTER SET utf8mb4

    COLLATE utf8mb4_unicode_ci;

Important Notes:

• Database Names: Database names usually have restrictions on characters (e.g., no spaces, special characters). Refer to the specific documentation for your database system for naming conventions.
• Permissions: You typically need appropriate privileges (e.g., the CREATE DATABASE privilege) to create new databases.

1. Data Definition Language (DDL)

• CREATE TABLE:
• Creates a new table in the database.
• Syntax:

SQL

CREATE TABLE table_name (

    column1 data_type [constraints],

    column2 data_type [constraints],

    ...

    [primary key (column1, column2, ...)],

    [foreign key (column1) references other_table(column1)]

);

• Example:

SQL

CREATE TABLE Customers (

    CustomerID INT PRIMARY KEY,

    CustomerName VARCHAR(255),

    City VARCHAR(50)

);

• ALTER TABLE:
• Modifies the structure of an existing table.
• Syntax:

SQL

ALTER TABLE table_name

    ADD column_name data_type;

ALTER TABLE table_name

    DROP COLUMN column_name;

ALTER TABLE table_name

    MODIFY column_name data_type;

• DROP TABLE:
• Deletes an existing table and all its data.
• Syntax:

SQL

DROP TABLE table_name;

2. Data Manipulation Language (DML)

• SELECT:
• Retrieves data from one or more tables.
• Syntax:

SQL

SELECT column1, column2, ...

FROM table1

[WHERE condition]

[ORDER BY column1, column2, ...]

[LIMIT number];

• Example:

SQL

SELECT CustomerName, City

FROM Customers

WHERE City = 'New York';

• INSERT INTO:
• Inserts new rows into a table.
• Syntax:

SQL

INSERT INTO table_name (column1, column2, ...)

VALUES (value1, value2, ...);

• UPDATE:
• Modifies existing data in a table.
• Syntax:

SQL

UPDATE table_name

SET column1 = value1, column2 = value2, ...

WHERE condition;

• DELETE FROM:
• Deletes rows from a table.
• Syntax:

SQL

DELETE FROM table_name

WHERE condition;

3. Data Control Language (DCL)

• GRANT:
• Grants privileges to users or roles.
• REVOKE:
• Revokes privileges from users or roles.

Key Concepts:

• Keywords: Reserved words with specific meanings (e.g., SELECT, FROM, WHERE, CREATE, TABLE).
• Data Types: Specify the type of data that can be stored in a column (e.g., INT, VARCHAR, DATE).
• Operators: Used to compare values (e.g., =, <, >, !=), perform arithmetic operations, and combine conditions (e.g., AND, OR, NOT).
• Constraints: Rules that enforce data integrity (e.g., PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL).

This is a simplified overview of SQL syntax. It's essential to refer to the specific documentation of your database system for detailed information and any variations in syntax.

 Database Objects:

In a relational database, database objects are the fundamental components that store and manage data. Here's a breakdown of some common database objects:

1. Tables:

• Core Structure: The primary building block of a relational database.
• Organization: Data is organized in rows and columns.
• Purpose: Store and represent specific entities or subjects (e.g., "Customers," "Products," "Orders").

2. Views:

• Virtual Tables: Derived from one or more underlying tables. They don't store data directly but rather provide a dynamic window into the data.
• Purpose:
• Simplify complex queries.
• Present a specific subset of data.
• Provide a layer of abstraction over the underlying tables.
• Improve data security by restricting access to specific columns or rows.
• Types of Views 

• 1. Simple Views:

  • Definition: Derived from a single table, often selecting specific columns or rows based on conditions.
  • Example:
    SQL

    CREATE VIEW CustomerNames AS
    SELECT CustomerName 
    FROM Customers;
    

  2. Complex Views:

  • Definition: Involve multiple tables, joins, aggregate functions (like SUM, AVG, COUNT), and other complex operations.
  • Example:
    SQL

    CREATE VIEW OrderTotals AS
    SELECT CustomerName, SUM(OrderAmount) AS TotalAmount
    FROM Customers c
    JOIN Orders o ON c.CustomerID = o.CustomerID
    GROUP BY CustomerName;
    

  3. Materialized Views:

  • Definition: Store the result set of a query physically, improving performance for frequently accessed complex queries.
  • Key Characteristics:
    • Pre-calculated: Data is stored and updated periodically, reducing the need to re-execute the complex query each time.
    • Faster Queries: Access to materialized views is generally faster than executing the underlying complex query.
    • Maintenance: Requires regular updates to keep the materialized view synchronized with the base tables.

  4. Inline Views (Subquery Views):

  • Definition: A view defined within a subquery in the FROM clause of another query.
  • Example:
    SQL

    SELECT *
    FROM (SELECT CustomerName, SUM(OrderAmount) AS TotalAmount
           FROM Customers c
           JOIN Orders o ON c.CustomerID = o.CustomerID
           GROUP BY CustomerName) AS OrderTotals
    WHERE TotalAmount > 1000;

3. Indexes:

• Data Structures: Created on specific columns to speed up data retrieval.
• Purpose:
• Allow the database system to quickly locate specific rows without scanning the entire table. 
• Improve the performance of queries that use WHERE clauses, ORDER BY clauses, and JOIN operations.  Helps the database system choose the most efficient execution plan for complex queries.

• Types of Indexes

1. Unique Index: Ensures that each value in the indexed column is unique.
2. Non-Unique Index: Allows duplicate values in the indexed column.
3. Clustered Index: Determines the physical order of data in the table. Only one clustered index can exist per table.
4. Non-Clustered Index: Creates a separate data structure that points to the actual data rows.

• Drawbacks of Using Indexes

• Increased Storage Space: Indexes require additional storage space to maintain the index data structure.
• Slower Data Modifications: Insert, update, and delete operations on indexed columns can be slightly slower due to the need to maintain the index.

• When to Use Indexes

  • Frequently Queried Columns: Create indexes on columns that are frequently used in WHERE, JOIN, ORDER BY, and GROUP BY clauses.
  • Large Tables: Indexes are especially beneficial for large tables where full table scans can be time-consuming.
  • Unique Constraints: If you need to enforce uniqueness on a column, create a unique index on it.

4. Stored Procedures:

• Pre-compiled SQL Code: Reusable blocks of SQL code that perform specific tasks.
• Purpose:
• Encapsulate complex business logic.
• Improve performance by reducing network traffic.
• Enhance security by centralizing access control.

5. Functions:

• Similar to Stored Procedures: But typically return a single value.
• Purpose:
• Perform calculations or data transformations.
• Used within SQL statements.

6. Triggers:

• Automated Actions: Code blocks that are automatically executed in response to specific events (e.g., insert, update, delete).
• Purpose:
• Enforce business rules.
• Maintain data integrity.
• Implement audit trails.

• Types of Triggers

• 1. Data Manipulation Language (DML) Triggers:

  • Triggered by: INSERT, UPDATE, or DELETE statements on a table.
  • Types:
    • AFTER Triggers: Execute after the DML operation is successfully completed.
    • INSTEAD OF Triggers: Replace the default DML operation with custom logic. Often used with views or tables without underlying data.

  2. Data Definition Language (DDL) Triggers:

  • Triggered by: DDL statements like CREATE TABLE, ALTER TABLE, DROP TABLE, etc.
  • Purpose:
    • Audit database schema changes.
    • Enforce constraints on database structure.
    • Trigger other actions based on schema modifications.

  3. Logon Triggers:

  • Triggered by: User login events to the database server.
  • Purpose:
    • Audit user login attempts.
    • Enforce security policies (e.g., password complexity checks).
    • Execute specific tasks upon user login (e.g., setting session variables).

7. Constraints:

• Rules: Enforce data integrity and consistency within tables.
• Examples:
• Primary Key: Uniquely identifies each row in a table.
• Foreign Key: Establishes relationships between tables.
• Unique: Ensures that a specific column or set of columns has unique values.
• Not Null: Prevents null values in a column.
• Check: Enforces specific conditions on the data in a column.

These are some of the most common database objects. The specific types and features of database objects may vary depending on the database system (e.g., MySQL, PostgreSQL, SQL Server).

By understanding these objects and how they interact, you can effectively design, implement, and maintain efficient and robust database systems.