Database Schema Design Workflow
Design a SQL schema, visualize entity relationships, and generate type-safe ORM models.
Overview
Good database design requires iterating between SQL schema definitions, entity relationship diagrams, and ORM model generation. This workflow lets you do all three in one sitting without leaving your browser.
Step-by-Step Implementation
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Quick Summary
Designing a schema lives best in three loops: SQL DDL → visual ERD (catches missing relationships) → ORM model (catches naming/typing inconsistencies). Iterate between them, a relationship that's awkward in the ORM is usually a sign the SQL was wrong.
Key Takeaways
- Normalize to 3NF first, then strategically denormalize hot paths after profiling.
- Always include `created_at` and `updated_at` timestamps, you'll regret omitting them.
- Foreign keys are non-negotiable for relational integrity; the small write cost is worth it.
- Use UUIDs (v7 if available, for time-ordering) over auto-increment for distributed systems.
- Index foreign keys explicitly, most databases don't auto-create indexes for them (MySQL does, Postgres doesn't).
When to use it
- Greenfield schema design before writing the first migration.
- Reverse-engineering an existing database into an ERD for documentation.
- Generating Prisma/Drizzle/SQLAlchemy models from a legacy SQL schema.
- Reviewing schema changes during code review, visual diffs are easier than DDL diffs.
Common Mistakes
- Storing dates as VARCHAR, kills query performance and timezone correctness.
- Using NULL when an empty string or default would work, three-valued logic surprises you in WHERE clauses.
- Auto-incrementing PKs that leak business volume (`/orders/12345` reveals total order count).
- Skipping indexes 'until needed', adding them later requires either downtime or careful CONCURRENTLY in Postgres.
Database Schema Design Workflow, Frequently Asked
UUID vs auto-increment for primary keys?
UUID v7 (time-sortable) for new schemas, works in distributed systems, no enumeration leaks. Auto-increment is fine for single-DB monoliths with no exposure of IDs to external systems.
Should I use an ORM or raw SQL?
ORM (Prisma, Drizzle, SQLAlchemy) for CRUD and type safety. Raw SQL for reports, analytics queries, and anything performance-critical. Most teams need both.
How do I version schema changes?
Use a migration tool (Prisma Migrate, Flyway, Liquibase, Atlas). Migrations should be additive where possible, drop columns/tables only after the application no longer references them.