database-schema-design
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SKILL.md
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Database Schema Design
Core Principles
- Normalize first, denormalize for performance
- Use appropriate data types - smallest type that fits
- Index strategically - based on query patterns
- Plan for growth - consider partitioning early
Naming Conventions
-- Tables: plural, snake_case
users, order_items, user_addresses
-- Columns: snake_case
first_name, created_at, is_active
-- Primary keys: id
id SERIAL PRIMARY KEY
-- Foreign keys: singular_table_id
user_id REFERENCES users(id)
-- Indexes: idx_table_column(s)
CREATE INDEX idx_users_email ON users(email);
-- Constraints: chk_/uq_/fk_ prefix
CONSTRAINT uq_users_email UNIQUE (email)
CONSTRAINT chk_orders_amount CHECK (amount > 0)
Common Patterns
Users Table
CREATE TABLE users (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
email VARCHAR(255) NOT NULL UNIQUE,
password_hash VARCHAR(255) NOT NULL,
name VARCHAR(100) NOT NULL,
role VARCHAR(20) DEFAULT 'user' CHECK (role IN ('user', 'admin', 'moderator')),
is_active BOOLEAN DEFAULT true,
email_verified_at TIMESTAMPTZ,
created_at TIMESTAMPTZ DEFAULT NOW(),
updated_at TIMESTAMPTZ DEFAULT NOW()
);
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_role ON users(role) WHERE is_active = true;
One-to-Many Relationship
CREATE TABLE posts (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE,
title VARCHAR(255) NOT NULL,
content TEXT,
status VARCHAR(20) DEFAULT 'draft',
published_at TIMESTAMPTZ,
created_at TIMESTAMPTZ DEFAULT NOW(),
updated_at TIMESTAMPTZ DEFAULT NOW()
);
CREATE INDEX idx_posts_user_id ON posts(user_id);
CREATE INDEX idx_posts_status_published ON posts(status, published_at DESC)
WHERE status = 'published';
Many-to-Many Relationship
CREATE TABLE tags (
id SERIAL PRIMARY KEY,
name VARCHAR(50) NOT NULL UNIQUE,
slug VARCHAR(50) NOT NULL UNIQUE
);
CREATE TABLE post_tags (
post_id UUID REFERENCES posts(id) ON DELETE CASCADE,
tag_id INTEGER REFERENCES tags(id) ON DELETE CASCADE,
PRIMARY KEY (post_id, tag_id)
);
CREATE INDEX idx_post_tags_tag_id ON post_tags(tag_id);
Polymorphic Associations
-- Using separate tables (preferred)
CREATE TABLE post_comments (
id UUID PRIMARY KEY,
post_id UUID REFERENCES posts(id),
content TEXT NOT NULL,
user_id UUID REFERENCES users(id)
);
CREATE TABLE image_comments (
id UUID PRIMARY KEY,
image_id UUID REFERENCES images(id),
content TEXT NOT NULL,
user_id UUID REFERENCES users(id)
);
-- Alternative: Single table with type column
CREATE TABLE comments (
id UUID PRIMARY KEY,
commentable_type VARCHAR(50) NOT NULL,
commentable_id UUID NOT NULL,
content TEXT NOT NULL,
user_id UUID REFERENCES users(id),
CONSTRAINT uq_comments_target UNIQUE (commentable_type, commentable_id, id)
);
Drizzle ORM Schema
import { pgTable, uuid, varchar, text, timestamp, boolean, index } from 'drizzle-orm/pg-core';
import { relations } from 'drizzle-orm';
export const users = pgTable('users', {
id: uuid('id').primaryKey().defaultRandom(),
email: varchar('email', { length: 255 }).notNull().unique(),
name: varchar('name', { length: 100 }).notNull(),
passwordHash: varchar('password_hash', { length: 255 }).notNull(),
isActive: boolean('is_active').default(true),
createdAt: timestamp('created_at', { withTimezone: true }).defaultNow(),
updatedAt: timestamp('updated_at', { withTimezone: true }).defaultNow(),
}, (table) => ({
emailIdx: index('idx_users_email').on(table.email),
}));
export const posts = pgTable('posts', {
id: uuid('id').primaryKey().defaultRandom(),
userId: uuid('user_id').notNull().references(() => users.id, { onDelete: 'cascade' }),
title: varchar('title', { length: 255 }).notNull(),
content: text('content'),
status: varchar('status', { length: 20 }).default('draft'),
publishedAt: timestamp('published_at', { withTimezone: true }),
createdAt: timestamp('created_at', { withTimezone: true }).defaultNow(),
});
export const usersRelations = relations(users, ({ many }) => ({
posts: many(posts),
}));
export const postsRelations = relations(posts, ({ one }) => ({
author: one(users, {
fields: [posts.userId],
references: [users.id],
}),
}));
Indexing Strategies
-- Single column index
CREATE INDEX idx_users_email ON users(email);
-- Composite index (order matters!)
CREATE INDEX idx_posts_user_status ON posts(user_id, status);
-- Partial index (smaller, faster)
CREATE INDEX idx_posts_published ON posts(published_at DESC)
WHERE status = 'published';
-- Expression index
CREATE INDEX idx_users_email_lower ON users(LOWER(email));
-- JSONB index
CREATE INDEX idx_users_metadata ON users USING GIN(metadata);
Soft Deletes
CREATE TABLE posts (
id UUID PRIMARY KEY,
-- other columns...
deleted_at TIMESTAMPTZ,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Query active records
SELECT * FROM posts WHERE deleted_at IS NULL;
-- Partial index for performance
CREATE INDEX idx_posts_active ON posts(created_at DESC)
WHERE deleted_at IS NULL;
Audit Trail
CREATE TABLE audit_logs (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
table_name VARCHAR(100) NOT NULL,
record_id UUID NOT NULL,
action VARCHAR(20) NOT NULL, -- INSERT, UPDATE, DELETE
old_data JSONB,
new_data JSONB,
user_id UUID REFERENCES users(id),
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Trigger function
CREATE OR REPLACE FUNCTION audit_trigger_func()
RETURNS TRIGGER AS $$
BEGIN
INSERT INTO audit_logs (table_name, record_id, action, old_data, new_data, user_id)
VALUES (
TG_TABLE_NAME,
COALESCE(NEW.id, OLD.id),
TG_OP,
CASE WHEN TG_OP != 'INSERT' THEN row_to_json(OLD) END,
CASE WHEN TG_OP != 'DELETE' THEN row_to_json(NEW) END,
current_setting('app.current_user_id', true)::uuid
);
RETURN COALESCE(NEW, OLD);
END;
$$ LANGUAGE plpgsql;
Best Practices
- Always use UUIDs for public-facing IDs
- Add timestamps (created_at, updated_at) to all tables
- Use foreign key constraints for referential integrity
- Create indexes based on queries not assumptions
- Use ENUM types sparingly - prefer check constraints
- Plan for soft deletes if business requires audit trail
- Use transactions for multi-table operations
- Partition large tables by time or category
Source
git clone https://github.com/autohandai/community-skills/blob/main/database-schema-design/SKILL.mdView on GitHub Overview
This skill teaches structured schema design using core principles like normalization, appropriate data types, and strategic indexing. It covers naming conventions, common patterns (users, one-to-many, many-to-many, polymorphic associations), and practical Drizzle ORM examples to guide implementation.
How This Skill Works
It starts with normalization and careful data typing, then enforces constraints and builds indexes aligned with real query workloads. Patterns are demonstrated in SQL and mapped to Drizzle ORM schemas for PostgreSQL, Prisma, and related stacks.
When to Use It
- Building a user-centric app with roles and auth
- Modeling posts and authors (one-to-many)
- Implementing tags with post_tags (many-to-many)
- Adding polymorphic comments across entities
- Planning for growth with indexing and partitioning
Quick Start
- Step 1: Define core tables (users, posts) with primary keys and naming conventions
- Step 2: Add FKs, indexes, and constraints to enforce relationships
- Step 3: Implement using Drizzle ORM or Prisma and validate queries
Best Practices
- Normalize data first; denormalize only for read performance when needed
- Choose the smallest data type that fits each column
- Index based on real query patterns; avoid over-indexing
- Name tables in plural snake_case and suffix keys clearly (id, user_id)
- Define clear constraints (PK, FK, UNIQUE, CHECK) and plan for growth
Example Use Cases
- Users table with unique email and role field
- Posts table linked to users via user_id with published_at
- Tags and post_tags implementing a many-to-many relationship
- Polymorphic comments using dedicated comment tables
- Drizzle ORM schema mapping for PostgreSQL (drizzle-orm/pg-core)
Frequently Asked Questions
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