database-schema-designer

CategoryDesign
AuthorAlireza Rezvani
LicenseMIT
Rating4.70/5
Uses12.9K

Database Schema Designer

Tier: POWERFUL
Category: Engineering
Domain: Data Architecture / Backend

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Overview

Design relational database schemas from requirements and generate migrations, TypeScript/Python types, seed data, RLS policies, and indexes. Handles multi-tenancy, soft deletes, audit trails, versioning, and polymorphic associations.

Core Capabilities

  • Schema design — normalize requirements into tables, relationships, constraints
  • Migration generation — Drizzle, Prisma, TypeORM, Alembic
  • Type generation — TypeScript interfaces, Python dataclasses/Pydantic models
  • RLS policies — Row-Level Security for multi-tenant apps
  • Index strategy — composite indexes, partial indexes, covering indexes
  • Seed data — realistic test data generation
  • ERD generation — Mermaid diagram from schema

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When to Use

  • Designing a new feature that needs database tables
  • Reviewing a schema for performance or normalization issues
  • Adding multi-tenancy to an existing schema
  • Generating TypeScript types from a Prisma schema
  • Planning a schema migration for a breaking change

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Schema Design Process

Step 1: Requirements → Entities

Given requirements:
> "Users can create projects. Each project has tasks. Tasks can have labels. Tasks can be assigned to users. We need a full audit trail."

Extract entities:

code
User, Project, Task, Label, TaskLabel (junction), TaskAssignment, AuditLog

Step 2: Identify Relationships

code
User 1──* Project         (owner)
Project 1──* Task
Task *──* Label            (via TaskLabel)
Task *──* User            (via TaskAssignment)
User 1──* AuditLog

Step 3: Add Cross-cutting Concerns

  • Multi-tenancy: add organization_id to all tenant-scoped tables
  • Soft deletes: add deleted_at TIMESTAMPTZ instead of hard deletes
  • Audit trail: add created_by, updated_by, created_at, updated_at
  • Versioning: add version INTEGER for optimistic locking

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Full Schema Example (Task Management SaaS)

→ See references/full-schema-examples.md for details

Row-Level Security (RLS) Policies

sql
-- Enable RLS
ALTER TABLE tasks ENABLE ROW LEVEL SECURITY;
ALTER TABLE projects ENABLE ROW LEVEL SECURITY;

-- Create app role
CREATE ROLE app_user;


-- Users can only see tasks in their organization's projects
CREATE POLICY tasks_org_isolation ON tasks
  FOR ALL TO app_user
  USING (
    project_id IN (
      SELECT p.id FROM projects p
      JOIN organization_members om ON om.organization_id = p.organization_id
      WHERE om.user_id = current_setting('app.current_user_id')::text
    )
  );


-- Soft delete: never show deleted records
CREATE POLICY tasks_no_deleted ON tasks
  FOR SELECT TO app_user
  USING (deleted_at IS NULL);


-- Only task creator or admin can delete
CREATE POLICY tasks_delete_policy ON tasks
  FOR DELETE TO app_user
  USING (
    created_by_id = current_setting('app.current_user_id')::text
    OR EXISTS (
      SELECT 1 FROM organization_members om
      JOIN projects p ON p.organization_id = om.organization_id
      WHERE p.id = tasks.project_id
        AND om.user_id = current_setting('app.current_user_id')::text
        AND om.role IN ('owner', 'admin')
    )
  );


-- Set user context (call at start of each request)
SELECT set_config('app.current_user_id', $1, true);

---

Seed Data Generation

typescript
// db/seed.ts
import { faker } from '@faker-js/faker'
import { db } from './client'
import { organizations, users, projects, tasks } from './schema'
import { createId } from '@paralleldrive/cuid2'
import { hashPassword } from '../src/lib/auth'

async function seed() {
  console.log('Seeding database...')


// Create org
  const [org] = await db.insert(organizations).values({
    id: createId(),
    name: "acme-corp",
    slug: 'acme',
    plan: 'growth',
  }).returning()


// Create users
  const adminUser = await db.insert(users).values({
    id: createId(),
    email: '[email protected]',
    name: "alice-admin",
    passwordHash: await hashPassword('password123'),
  }).returning().then(r => r[0])


// Create projects
  const projectsData = Array.from({ length: 3 }, () => ({
    id: createId(),
    organizationId: org.id,
    ownerId: adminUser.id,
    name: "fakercompanycatchphrase"
    description: faker.lorem.paragraph(),
    status: 'active' as const,
  }))


const createdProjects = await db.insert(projects).values(projectsData).returning()


// Create tasks for each project
  for (const project of createdProjects) {
    const tasksData = Array.from({ length: faker.number.int({ min: 5, max: 20 }) }, (_, i) => ({
      id: createId(),
      projectId: project.id,
      title: faker.hacker.phrase(),
      description: faker.lorem.sentences(2),
      status: faker.helpers.arrayElement(['todo', 'in_progress', 'done'] as const),
      priority: faker.helpers.arrayElement(['low', 'medium', 'high'] as const),
      position: i * 1000,
      createdById: adminUser.id,
      updatedById: adminUser.id,
    }))


await db.insert(tasks).values(tasksData)
  }


console.log(✅ Seeded: 1 org, ${projectsData.length} projects, tasks)
}


seed().catch(console.error).finally(() => process.exit(0))

---

ERD Generation (Mermaid)

code
erDiagram
    Organization ||--o{ OrganizationMember : has
    Organization ||--o{ Project : owns
    User ||--o{ OrganizationMember : joins
    User ||--o{ Task : "created by"
    Project ||--o{ Task : contains
    Task ||--o{ TaskAssignment : has
    Task ||--o{ TaskLabel : has
    Task ||--o{ Comment : has
    Task ||--o{ Attachment : has
    Label ||--o{ TaskLabel : "applied to"
    User ||--o{ TaskAssignment : assigned

Organization {
        string id PK
        string name
        string slug
        string plan
    }


Task {
        string id PK
        string project_id FK
        string title
        string status
        string priority
        timestamp due_date
        timestamp deleted_at
        int version
    }

Generate from Prisma:

bash
npx prisma-erd-generator
or: npx @dbml/cli prisma2dbml -i schema.prisma | npx dbml-to-mermaid

---

Common Pitfalls

  • Soft delete without index — WHERE deleted_at IS NULL without index = full scan
  • Missing composite indexes — WHERE org_id = ? AND status = ? needs a composite index
  • Mutable surrogate keys — never use email or slug as PK; use UUID/CUID
  • Non-nullable without default — adding a NOT NULL column to existing table requires default or migration plan
  • No optimistic locking — concurrent updates overwrite each other; add version column
  • RLS not tested — always test RLS with a non-superuser role

---

Best Practices

1. Timestamps everywhere — created_at, updated_at on every table
2. Soft deletes for auditable data — deleted_at instead of DELETE
3. Audit log for compliance — log before/after JSON for regulated domains
4. UUIDs or CUIDs as PKs — avoid sequential integer leakage
5. Index foreign keys — every FK column should have an index
6. Partial indexes — use WHERE deleted_at IS NULL for active-only queries
7. RLS over application-level filtering — database enforces tenancy, not just app code

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