Database Design Patterns for Modern Applications

Effective database design is crucial for building performant and maintainable applications. This post covers essential patterns and strategies for modern database architectures.

Fundamental Principles

Normalization vs Denormalization

Normalization reduces data redundancy:

• Eliminates duplicate data
• Ensures data consistency
• Reduces storage requirements

Denormalization optimizes for read performance:

• Reduces complex joins
• Improves query speed
• Increases storage requirements

Common Patterns

Repository Pattern

Encapsulates data access logic:

interface UserRepository {
  findById(id: string): Promise<User>;
  save(user: User): Promise<void>;
  delete(id: string): Promise<void>;
}

Unit of Work

Maintains a list of objects affected by business transactions:

class UnitOfWork {
  private newObjects: Set<Entity> = new Set();
  private dirtyObjects: Set<Entity> = new Set();
  private removedObjects: Set<Entity> = new Set();

  commit(): Promise<void> {
    // Persist all changes in a single transaction
  }
}

Performance Considerations

Indexing Strategy

• Create indexes on frequently queried columns
• Consider composite indexes for multi-column queries
• Monitor index usage and remove unused indexes

Query Optimization

• Use EXPLAIN plans to analyze query performance
• Avoid N+1 query problems
• Implement efficient pagination strategies

Modern Approaches

Event Sourcing

Store events rather than current state:

• Complete audit trail
• Ability to rebuild state at any point
• Support for temporal queries

CQRS (Command Query Responsibility Segregation)

Separate read and write models:

• Optimized read models for queries
• Simplified write models for commands
• Independent scaling of read/write workloads

The choice of patterns depends on your specific requirements around consistency, performance, and scalability.