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In this article, we will cover the Top 10 Database Design Mistakes and discuss best practices to avoid them.

1️⃣ Not Normalizing the Database Properly 🏛️

❌ Mistake: Storing Data in a Single Table

Some developers store all data in one table, leading to redundancy, inefficiency, and difficulty in updates.

Bad Example (Denormalized Table): ❌

OrderID	CustomerName	Product	Quantity	Address
1	Rahul	Laptop	1	Delhi, India
2	Rahul	Mouse	2	Delhi, India

✔ Issue: Customer data is repeated, violating 1NF (First Normal Form).

✅ Solution: Normalize the Database (3NF)

✔ Good Example (Normalized Table): ✅

Customers Table:

CustomerID	Name	Address
101	Rahul	Delhi, India

Orders Table:

OrderID	CustomerID	Product	Quantity
1	101	Laptop	1
2	101	Mouse	2

✔ Best Practices:

• Follow 1NF, 2NF, and 3NF for better data integrity.
• Avoid repeating groups and redundant data.

2️⃣ Ignoring Indexing 📌

❌ Mistake: No Indexing on Large Tables

Without indexes, queries run slowly, especially on large datasets.

Bad Example: ❌

SELECT * FROM users WHERE email = 'john@example.com';

✔ Issue: The query scans the entire table, making it slow.

✅ Solution: Use Indexing for Faster Queries

✔ Good Example: ✅

CREATE INDEX idx_email ON users(email);

✔ Best Practices:

• Index frequently searched columns (email, username).
• Avoid too many indexes (affects insert/update speed).
• Use composite indexes where applicable.

3️⃣ Using the Wrong Data Types ⚠️

❌ Mistake: Choosing Inefficient Data Types

Using wrong data types leads to poor performance and storage issues.

Bad Example: ❌

CREATE TABLE users (
    id BIGINT,
    username VARCHAR(255),
    email VARCHAR(255)
);

✔ Issue:

• BIGINT is overkill for id (use INT if < 2 billion rows).
• VARCHAR(255) is unnecessarily large for emails.

✅ Solution: Choose Optimal Data Types

✔ Good Example: ✅

CREATE TABLE users (
    id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,
    username VARCHAR(50),
    email VARCHAR(100)
);

✔ Best Practices:

• Use INT instead of BIGINT when possible.
• Avoid storing dates as VARCHAR (use DATE or TIMESTAMP).
• Use ENUM for fixed values (status ENUM('active', 'inactive')).

4️⃣ Not Using Foreign Keys Properly 🔗

❌ Mistake: No Foreign Key Constraints

Without foreign keys, data integrity issues arise.

Bad Example: ❌

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT
);

✔ Issue: No relationship between orders and customers, leading to orphaned data.

✅ Solution: Use Foreign Keys for Referential Integrity

✔ Good Example: ✅

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT,
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id) ON DELETE CASCADE
);

✔ Best Practices:

• Use FOREIGN KEY constraints to maintain data integrity.
• Use ON DELETE CASCADE to prevent orphaned rows.

5️⃣ Overusing NULL Values ❌

❌ Mistake: Too Many Nullable Columns

Having too many NULL values affects performance and indexing.

Bad Example: ❌

CREATE TABLE users (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    email VARCHAR(100) NULL,
    phone VARCHAR(20) NULL
);

✔ Issue: If email & phone are always required, why allow NULL?

✅ Solution: Avoid NULLs When Possible

✔ Good Example: ✅

CREATE TABLE users (
    id INT PRIMARY KEY,
    name VARCHAR(100) NOT NULL,
    email VARCHAR(100) NOT NULL,
    phone VARCHAR(20) NOT NULL
);

✔ Best Practices:

• Set NOT NULL on mandatory fields.
• Use default values where applicable.

6️⃣ Storing Derived or Redundant Data 📊

❌ Mistake: Storing Computed Values

Storing calculated values wastes storage and increases inconsistency.

Bad Example: ❌

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    quantity INT,
    price DECIMAL(10,2),
    total_price DECIMAL(10,2)  -- Redundant column!
);

✔ Issue: total_price = quantity * price should be calculated, not stored.

✅ Solution: Calculate on the Fly

✔ Good Example: ✅

SELECT order_id, quantity, price, (quantity * price) AS total_price FROM orders;

✔ Best Practices:

• Avoid redundant data storage.
• Use database views for calculated fields.

7️⃣ Using Auto-Incremented IDs for Sensitive Data 🔑

❌ Mistake: Exposing Sequential IDs

Auto-incremented IDs expose internal structure.

Bad Example: ❌

GET /users/100

✔ Issue: Predictable IDs are security risks.

✅ Solution: Use UUIDs for Public APIs

✔ Good Example: ✅

GET /users/550e8400-e29b-41d4-a716-446655440000

✔ Best Practices:

• Use UUIDs for external API references.
• Keep internal IDs auto-incremented.

8️⃣ Not Optimizing Joins & Queries 🏎️

❌ Mistake: Running Expensive Queries

Poor query optimization slows down applications.

Bad Example: ❌

SELECT * FROM orders o JOIN customers c ON o.customer_id = c.customer_id;

✔ Issue: If customer_id is not indexed, query performance drops.

✅ Solution: Optimize Joins & Use Indexes

✔ Good Example: ✅

CREATE INDEX idx_customer_id ON orders(customer_id);

✔ Best Practices:

• Index foreign keys used in joins.
• Use EXPLAIN to analyze query performance.

9️⃣ Hardcoding Database Configurations 🚨

❌ Mistake: Hardcoded Credentials

Storing database credentials inside the code is a security risk.

Bad Example: ❌

String dbUrl = "jdbc:mysql://localhost:3306/mydb";
String dbUser = "root";
String dbPass = "password123";

✔ Issue: Credentials exposed in code.

✅ Solution: Use Environment Variables

✔ Good Example: ✅

export DB_USER=root
export DB_PASS=securepassword

✔ Best Practices:

• Store credentials in environment variables.
• Use secrets management tools (AWS Secrets Manager, Vault).

🔟 Ignoring Database Backups & Disaster Recovery 💾

❌ Mistake: No Backup Strategy

Without backups, data loss is permanent.

Bad Example: ❌

Application crashes → Data lost forever!

✅ Solution: Automate Backups

✔ Best Practices:

• Schedule daily database backups.
• Store backups in different locations.
• Test backup restoration regularly.

🎯 Conclusion

Avoiding these common database design mistakes improves performance, security, and maintainability.

Quick Recap

✔ Normalize the database properly
✔ Use indexing for faster queries
✔ Choose correct data types
✔ Implement foreign keys for integrity
✔ Avoid redundant data storage

🔑 Keywords:

Database design best practices, SQL mistakes, indexing, normalization, database optimization, database security.