database-design-patterns

Normalization organizes data into 1NF, 2NF, and 3NF to eliminate redundancy and improve data integrity. Start by identifying repeating groups and separating them into distinct tables, then remove partial dependencies by ensuring non-key attributes depend on the entire primary key, and finally eliminate transitive dependencies between non-key attributes.

Denormalize only after measuring performance and identifying read bottlenecks that normalization cannot solve through indexing alone. Strategic denormalization—such as storing computed values or duplicating data across tables—trades write complexity and storage for faster queries; apply it selectively where query load justifies the tradeoff.

Index frequently queried columns, foreign keys, and WHERE clause predicates to accelerate searches. Avoid over-indexing write-heavy tables; use composite indexes for multi-column filters and monitor query execution plans to identify missing or unused indexes on PostgreSQL, MySQL, and other SQL workloads.

Write migrations as paired forward and rollback scripts that preserve data integrity and schema state. Test migrations on production-like data volumes, document schema changes, and ensure each migration is independent and idempotent so you can reverse failed deployments without data loss.

Yes. Analyze your current schema for inefficiencies, apply constraints and foreign keys incrementally, add indexes without downtime, and use migrations to refactor tables. Start with read-heavy optimization through indexing, then introduce denormalization or structural changes only where measurement shows benefit.

Use NOT NULL, UNIQUE, PRIMARY KEY, and FOREIGN KEY constraints to enforce data integrity at the database layer. Choose appropriate data types to match domain requirements and prevent invalid data entry; combine constraints with migrations to retrofit enforcement onto existing schemas progressively.