10 Common Database Design Mistakes to Avoid

Designing a database is both an art and a science. While a well-designed database ensures optimal performance and scalability, common mistakes can lead to inefficiencies and maintenance nightmares. Here, we explore 10 frequent database design errors and how to avoid them.

1. Ignoring Normalization

Failing to normalize your database can result in redundant data, making updates cumbersome and error-prone.


      -- Example of normalization
      CREATE TABLE Employees (
          EmployeeID INT PRIMARY KEY,
          FirstName VARCHAR(50),
          LastName VARCHAR(50),
          DepartmentID INT,
          FOREIGN KEY (DepartmentID) REFERENCES Departments(DepartmentID)
      );

2. Over-Normalization

Excessive normalization can lead to overly complex queries and reduced performance. Strike a balance based on your use case.


      -- Over-normalized schema can result in complex joins
      SELECT e.FirstName, d.DepartmentName
      FROM Employees e
      JOIN Departments d ON e.DepartmentID = d.DepartmentID;

3. Poor Indexing Strategy

Without proper indexing, your queries may run significantly slower, especially as the database grows.


      -- Adding an index to improve performance
      CREATE INDEX idx_department_id ON Employees (DepartmentID);

4. Using Generic Data Types

Choosing overly generic data types can lead to wasted storage and less efficient queries.

Column	Generic Data Type	Optimized Data Type
Age	VARCHAR(255)	TINYINT
Price	VARCHAR(255)	DECIMAL(10,2)

5. Neglecting Foreign Keys

Failing to define foreign key relationships compromises data integrity and makes debugging harder.


      -- Example of defining a foreign key
      ALTER TABLE Orders
      ADD CONSTRAINT fk_customer
      FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID);

6. Not Planning for Scalability

Designing without considering future growth can result in costly redesigns and performance issues.


      -- Partitioning for scalability
      CREATE TABLE Sales (
          SaleID INT NOT NULL,
          SaleDate DATE NOT NULL,
          RegionID INT NOT NULL,
          Amount DECIMAL(10,2),
          PRIMARY KEY (SaleID, RegionID)
      ) PARTITION BY RANGE (SaleDate) (
          PARTITION p1 VALUES LESS THAN ('2025-01-01'),
          PARTITION p2 VALUES LESS THAN ('2030-01-01')
      );

7. Overuse of NULL Values

Excessive use of NULLs can complicate query logic and lead to unexpected results in aggregations.


      -- Avoiding NULLs by using default values
      ALTER TABLE Products
      MODIFY COLUMN Stock INT DEFAULT 0 NOT NULL;

8. Hardcoding Configuration Values

Storing configuration values directly in code rather than in the database makes updates cumbersome and error-prone.


      -- Storing configuration in a table
      CREATE TABLE Configurations (
          ConfigID INT PRIMARY KEY,
          ConfigKey VARCHAR(50) NOT NULL,
          ConfigValue VARCHAR(255) NOT NULL
      );

9. Ignoring Data Security

Failing to implement encryption, user permissions, and security protocols can leave your data vulnerable to breaches.


      -- Example of user permissions
      GRANT SELECT, INSERT, UPDATE ON Employees TO 'app_user';

10. Lack of Documentation

Without proper documentation, onboarding new developers or debugging issues becomes a daunting task.


      -- Example of a schema comment
      COMMENT ON TABLE Employees IS 'Table to store employee information';

Conclusion

Avoiding these common mistakes will not only improve your database’s performance but also reduce technical debt and maintenance overhead. Which of these mistakes have you encountered? Share your experience in the comments below!