database-design

Normalized database schemas organize data into related tables with primary keys, foreign key constraints, and timestamp patterns to eliminate redundancy. Start by identifying entities (users, posts, comments), define relationships, apply normalization rules to minimize duplication, and enforce referential integrity through constraints.

Indexing strategy balances query performance with write overhead. Analyze your most frequent queries—such as feed retrieval or profile lookups—and create indexes on foreign keys, timestamps, and filter columns. Avoid over-indexing; prioritize columns used in WHERE clauses and joins.

ORM selection depends on your application framework and deployment model. Evaluate ORMs on migration tooling, serverless compatibility, and schema evolution support. Align your choice with your database platform and infrastructure to streamline deployment.

Serverless databases require schemas optimized for connection pooling and stateless queries. Use managed database services with built-in scaling, design schemas that minimize transaction complexity, and avoid long-running migrations during deployment.

Normalization enforces data integrity and reduces storage but increases query complexity through joins. Denormalization speeds up reads but risks data inconsistency and higher maintenance overhead. Balance based on your read-to-write ratio and consistency requirements.

Timestamp patterns—created_at and updated_at columns—enable auditing, sorting, and soft deletes. Include them on tables tracking state changes; omit only on reference or lookup tables where temporal tracking adds no value.