HuynhSang2005/database-schema-designer

Database Schema Designer

Design production-ready database schemas with best practices built-in.

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Quick Start

Just describe your data model:

design a schema for an e-commerce platform with users, products, orders

You'll get a complete SQL schema like:

CREATE TABLE users (
  id BIGINT AUTO_INCREMENT PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE orders (
  id BIGINT AUTO_INCREMENT PRIMARY KEY,
  user_id BIGINT NOT NULL REFERENCES users(id),
  total DECIMAL(10,2) NOT NULL,
  INDEX idx_orders_user (user_id)
);

What to include in your request:

• Entities (users, products, orders)
• Key relationships (users have orders, orders have items)
• Scale hints (high-traffic, millions of records)
• Database preference (SQL/NoSQL) - defaults to SQL if not specified

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Triggers

| Trigger | Example | |---------|---------| | design schema | "design a schema for user authentication" | | database design | "database design for multi-tenant SaaS" | | create tables | "create tables for a blog system" | | schema for | "schema for inventory management" | | model data | "model data for real-time analytics" | | I need a database | "I need a database for tracking orders" | | design NoSQL | "design NoSQL schema for product catalog" |

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Key Terms

| Term | Definition | |------|------------| | Normalization | Organizing data to reduce redundancy (1NF → 2NF → 3NF) | | 3NF | Third Normal Form - no transitive dependencies between columns | | OLTP | Online Transaction Processing - write-heavy, needs normalization | | OLAP | Online Analytical Processing - read-heavy, benefits from denormalization | | Foreign Key (FK) | Column that references another table's primary key | | Index | Data structure that speeds up queries (at cost of slower writes) | | Access Pattern | How your app reads/writes data (queries, joins, filters) | | Denormalization | Intentionally duplicating data to speed up reads |

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Quick Reference

| Task | Approach | Key Consideration | |------|----------|-------------------| | New schema | Normalize to 3NF first | Domain modeling over UI | | SQL vs NoSQL | Access patterns decide | Read/write ratio matters | | Primary keys | INT or UUID | UUID for distributed systems | | Foreign keys | Always constrain | ON DELETE strategy critical | | Indexes | FKs + WHERE columns | Column order matters | | Migrations | Always reversible | Backward compatible first |

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Process Overview

Your Data Requirements
    |
    v
+-----------------------------------------------------+
| Phase 1: ANALYSIS                                   |
| * Identify entities and relationships               |
| * Determine access patterns (read vs write heavy)   |
| * Choose SQL or NoSQL based on requirements         |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 2: DESIGN                                     |
| * Normalize to 3NF (SQL) or embed/reference (NoSQL) |
| * Define primary keys and foreign keys              |
| * Choose appropriate data types                     |
| * Add constraints (UNIQUE, CHECK, NOT NULL)         |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 3: OPTIMIZE                                   |
| * Plan indexing strategy                            |
| * Consider denormalization for read-heavy queries   |
| * Add timestamps (created_at, updated_at)           |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 4: MIGRATE                                    |
| * Generate migration scripts (up + down)            |
| * Ensure backward compatibility                     |
| * Plan zero-downtime deployment                     |
+-----------------------------------------------------+
    |
    v
Production-Ready Schema

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Commands

| Command | When to Use | Action | |---------|-------------|--------| | design schema for {domain} | Starting fresh | Full schema generation | | normalize {table} | Fixing existing table | Apply normalization rules | | add indexes for {table} | Performance issues | Generate index strategy | | migration for {change} | Schema evolution | Create reversible migration | | review schema | Code review | Audit existing schema |

Workflow: Start with design schema → iterate with normalize → optimize with add indexes → evolve with migration

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Core Principles

| Principle | WHY | Implementation | |-----------|-----|----------------| | Model the Domain | UI changes, domain doesn't | Entity names reflect business concepts | | Data Integrity First | Corruption is costly to fix | Constraints at database level | | Optimize for Access Pattern | Can't optimize for both | OLTP: normalized, OLAP: denormalized | | Plan for Scale | Retrofitting is painful | Index strategy + partitioning plan |

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Anti-Patterns

| Avoid | Why | Instead | |-------|-----|---------| | VARCHAR(255) everywhere | Wastes storage, hides intent | Size appropriately per field | | FLOAT for money | Rounding errors | DECIMAL(10,2) | | Missing FK constraints | Orphaned data | Always define foreign keys | | No indexes on FKs | Slow JOINs | Index every foreign key | | Storing dates as strings | Can't compare/sort | DATE, TIMESTAMP types | | SELECT * in queries | Fetches unnecessary data | Explicit column lists | | Non-reversible migrations | Can't rollback | Always write DOWN migration | | Adding NOT NULL without default | Breaks existing rows | Add nullable, backfill, then constrain |

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Verification Checklist

After designing a schema:

• [ ] Every table has a primary key
• [ ] All relationships have foreign key constraints
• [ ] ON DELETE strategy defined for each FK
• [ ] Indexes exist on all foreign keys
• [ ] Indexes exist on frequently queried columns
• [ ] Appropriate data types (DECIMAL for money, etc.)
• [ ] NOT NULL on required fields
• [ ] UNIQUE constraints where needed
• [ ] CHECK constraints for validation
• [ ] created_at and updated_at timestamps
• [ ] Migration scripts are reversible
• [ ] Tested on staging with production data

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<details> <summary><strong>Deep Dive: Normalization (SQL)</strong></summary>

Normal Forms

| Form | Rule | Violation Example | |------|------|-------------------| | 1NF | Atomic values, no repeating groups | product_ids = '1,2,3' | | 2NF | 1NF + no partial dependencies | customer_name in order_items | | 3NF | 2NF + no transitive dependencies | country derived from postal_code |

1st Normal Form (1NF)

-- BAD: Multiple values in column
CREATE TABLE orders (
  id INT PRIMARY KEY,
  product_ids VARCHAR(255)  -- '101,102,103'
);

-- GOOD: Separate table for items
CREATE TABLE orders (
  id INT PRIMARY KEY,
  customer_id INT
);

CREATE TABLE order_items (
  id INT PRIMARY KEY,
  order_id INT REFERENCES orders(id),
  product_id INT
);

2nd Normal Form (2NF)

-- BAD: customer_name depends only on customer_id
CREATE TABLE order_items (
  order_id INT,
  product_id INT,
  customer_name VARCHAR(100),  -- Partial dependency!
  PRIMARY KEY (order_id, product_id)
);

-- GOOD: Customer data in separate table
CREATE TABLE customers (
  id INT PRIMARY KEY,
  name VARCHAR(100)
);

3rd Normal Form (3NF)

-- BAD: country depends on postal_code
CREATE TABLE customers (
  id INT PRIMARY KEY,
  postal_code VARCHAR(10),
  country VARCHAR(50)  -- Transitive dependency!
);

-- GOOD: Separate postal_codes table
CREATE TABLE postal_codes (
  code VARCHAR(10) PRIMARY KEY,
  country VARCHAR(50)
);

When to Denormalize

| Scenario | Denormalization Strategy | |----------|-------------------------| | Read-heavy reporting | Pre-calculated aggregates | | Expensive JOINs | Cached derived columns | | Analytics dashboards | Materialized views |

-- Denormalized for performance
CREATE TABLE orders (
  id INT PRIMARY KEY,
  customer_id INT,
  total_amount DECIMAL(10,2),  -- Calculated
  item_count INT               -- Calculated
);

</details> <details> <summary><strong>Deep Dive: Data Types</strong></summary>

String Types

| Type | Use Case | Example | |------|----------|---------| | CHAR(n) | Fixed length | State codes, ISO dates | | VARCHAR(n) | Variable length | Names, emails | | TEXT | Long content | Articles, descriptions |

-- Good sizing
email VARCHAR(255)
phone VARCHAR(20)
country_code CHAR(2)

Numeric Types

| Type | Range | Use Case | |------|-------|----------| | TINYINT | -128 to 127 | Age, status codes | | SMALLINT | -32K to 32K | Quantities | | INT | -2.1B to 2.1B | IDs, counts | | BIGINT | Very large | Large IDs, timestamps | | DECIMAL(p,s) | Exact precision | Money | | FLOAT/DOUBLE | Approximate | Scientific data |

-- ALWAYS use DECIMAL for money
price DECIMAL(10, 2)  -- $99,999,999.99

-- NEVER use FLOAT for money
price FLOAT  -- Rounding errors!

Date/Time Types

DATE        -- 2025-10-31
TIME        -- 14:30:00
DATETIME    -- 2025-10-31 14:30:00
TIMESTAMP   -- Auto timezone conversion

-- Always store in UTC
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP

Boolean

-- PostgreSQL
is_active BOOLEAN DEFAULT TRUE

-- MySQL
is_active TINYINT(1) DEFAULT 1

</details> <details> <summary><strong>Deep Dive: Indexing Strategy</strong></summary>

When to Create Indexes

| Always Index | Reason | |--------------|--------| | Foreign keys | Speed up JOINs | | WHERE clause columns | Speed up filtering | | ORDER BY columns | Speed up sorting | | Unique constraints | Enforced uniqueness |

-- Foreign key index
CREATE INDEX idx_orders_customer ON orders(customer_id);

-- Query pattern index
CREATE INDEX idx_orders_status_date ON orders(status, created_at);

Index Types

| Type | Best For | Example | |------|----------|---------| | B-Tree | Ranges, equality | price > 100 | | Hash | Exact matches only | email = '[email protected]' | | Full-text | Text search | MATCH AGAINST | | Partial | Subset of rows | WHERE is_active = true |

Composite Index Order

CREATE INDEX idx_customer_status ON orders(customer_id, status);

-- Uses index (customer_id first)
SELECT * FROM orders WHERE customer_id = 123;
SELECT * FROM orders WHERE customer_id = 123 AND status = 'pending';

-- Does NOT use index (status alone)
SELECT * FROM orders WHERE status = 'pending';

Rule: Most selective column first, or column most queried alone.

Index Pitfalls

| Pitfall | Problem | Solution | |---------|---------|----------| | Over-indexing | Slow writes | Only index what's queried | | Wrong column order | Unused index | Match query patterns | | Missing FK indexes | Slow JOINs | Always index FKs |

</details> <details> <summary><strong>Deep Dive: Constraints</strong></summary>

Primary Keys

-- Auto-increment (simple)
id INT AUTO_INCREMENT PRIMARY KEY

-- UUID (distributed systems)
id CHAR(36) PRIMARY KEY DEFAULT (UUID())

-- Composite (junction tables)
PRIMARY KEY (student_id, course_id)

Foreign Keys

FOREIGN KEY (customer_id) REFERENCES customers(id)
  ON DELETE CASCADE     -- Delete children with parent
  ON DELETE RESTRICT    -- Prevent deletion if referenced
  ON DELETE SET NULL    -- Set to NULL when parent deleted
  ON UPDATE CASCADE     -- Update children when parent changes

| Strategy | Use When | |----------|----------| | CASCADE | Dependent data (order_items) | | RESTRICT | Important references (prevent accidents) | | SET NULL | Optional relationships |

Other Constraints

-- Unique
email VARCHAR(255) UNIQUE NOT NULL

-- Composite unique
UNIQUE (student_id, course_id)

-- Check
price DECIMAL(10,2) CHECK (price >= 0)
discount INT CHECK (discount BETWEEN 0 AND 100)

-- Not null
name VARCHAR(100) NOT NULL

</details> <details> <summary><strong>Deep Dive: Relationship Patterns</strong></summary>

One-to-Many

CREATE TABLE orders (
  id INT PRIMARY KEY,
  customer_id INT NOT NULL REFERENCES customers(id)
);

CREATE TABLE order_items (
  id INT PRIMARY KEY,
  order_id INT NOT NULL REFERENCES orders(id) ON DELETE CASCADE,
  product_id INT NOT NULL,
  quantity INT NOT NULL
);

Many-to-Many

-- Junction table
CREATE TABLE enrollments (
  student_id INT REFERENCES students(id) ON DELETE CASCADE,
  course_id INT REFERENCES courses(id) ON DELETE CASCADE,
  enrolled_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  PRIMARY KEY (student_id, course_id)
);

Self-Referencing

CREATE TABLE employees (
  id INT PRIMARY KEY,
  name VARCHAR(100) NOT NULL,
  manager_id INT REFERENCES employees(id)
);

Polymorphic

-- Approach 1: Separate FKs (stronger integrity)
CREATE TABLE comments (
  id INT PRIMARY KEY,
  content TEXT NOT NULL,
  post_id INT REFERENCES posts(id),
  photo_id INT REFERENCES photos(id),
  CHECK (
    (post_id IS NOT NULL AND photo_id IS NULL) OR
    (post_id IS NULL AND photo_id IS NOT NULL)
  )
);

-- Approach 2: Type + ID (flexible, weaker integrity)
CREATE TABLE comments (
  id INT PRIMARY KEY,
  content TEXT NOT NULL,
  commentable_type VARCHAR(50) NOT NULL,
  commentable_id INT NOT NULL
);

</details> <details> <summary><strong>Deep Dive: NoSQL Design (MongoDB)</strong></summary>

Embedding vs Referencing

| Factor | Embed | Reference | |--------|-------|-----------| | Access pattern | Read together | Read separately | | Relationship | 1:few | 1:many | | Document size | Small | Approaching 16MB | | Update frequency | Rarely | Frequently |

Embedded Document

{
  "_id": "order_123",
  "customer": {
    "id": "cust_456",
    "name": "Jane Smith",
    "email": "[email protected]"
  },
  "items": [
    { "product_id": "prod_789", "quantity": 2, "price": 29.99 }
  ],
  "total": 109.97
}

Referenced Document

{
  "_id": "order_123",
  "customer_id": "cust_456",
  "item_ids": ["item_1", "item_2"],
  "total": 109.97
}

MongoDB Indexes

// Single field
db.users.createIndex({ email: 1 }, { unique: true });

// Composite
db.orders.createIndex({ customer_id: 1, created_at: -1 });

// Text search
db.articles.createIndex({ title: "text", content: "text" });

// Geospatial
db.stores.createIndex({ location: "2dsphere" });

</details> <details> <summary><strong>Deep Dive: Migrations</strong></summary>

Migration Best Practices

| Practice | WHY | |----------|-----| | Always reversible | Need to rollback | | Backward compatible | Zero-downtime deploys | | Schema before data | Separate concerns | | Test on staging | Catch issues early |

Adding a Column (Zero-Downtime)

-- Step 1: Add nullable column
ALTER TABLE users ADD COLUMN phone VARCHAR(20);

-- Step 2: Deploy code that writes to new column

-- Step 3: Backfill existing rows
UPDATE users SET phone = '' WHERE phone IS NULL;

-- Step 4: Make required (if needed)
ALTER TABLE users MODIFY phone VARCHAR(20) NOT NULL;

Renaming a Column (Zero-Downtime)

-- Step 1: Add new column
ALTER TABLE users ADD COLUMN email_address VARCHAR(255);

-- Step 2: Copy data
UPDATE users SET email_address = email;

-- Step 3: Deploy code reading from new column
-- Step 4: Deploy code writing to new column

-- Step 5: Drop old column
ALTER TABLE users DROP COLUMN email;

Migration Template

-- Migration: YYYYMMDDHHMMSS_description.sql

-- UP
BEGIN;
ALTER TABLE users ADD COLUMN phone VARCHAR(20);
CREATE INDEX idx_users_phone ON users(phone);
COMMIT;

-- DOWN
BEGIN;
DROP INDEX idx_users_phone ON users;
ALTER TABLE users DROP COLUMN phone;
COMMIT;

</details> <details> <summary><strong>Deep Dive: Performance Optimization</strong></summary>

Query Analysis

EXPLAIN SELECT * FROM orders
WHERE customer_id = 123 AND status = 'pending';

| Look For | Meaning | |----------|---------| | type: ALL | Full table scan (bad) | | type: ref | Index used (good) | | key: NULL | No index used | | rows: high | Many rows scanned |

N+1 Query Problem

# BAD: N+1 queries
orders = db.query("SELECT * FROM orders")
for order in orders:
    customer = db.query(f"SELECT * FROM customers WHERE id = {order.customer_id}")

# GOOD: Single JOIN
results = db.query("""
    SELECT orders.*, customers.name
    FROM orders
    JOIN customers ON orders.customer_id = customers.id
""")

Optimization Techniques

| Technique | When to Use | |-----------|-------------| | Add indexes | Slow WHERE/ORDER BY | | Denormalize | Expensive JOINs | | Pagination | Large result sets | | Caching | Repeated queries | | Read replicas | Read-heavy load | | Partitioning | Very large tables |

</details>

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Extension Points

1. Database-Specific Patterns: Add MySQL vs PostgreSQL vs SQLite variations
2. Advanced Patterns: Time-series, event sourcing, CQRS, multi-tenancy
3. ORM Integration: TypeORM, Prisma, SQLAlchemy patterns
4. Monitoring: Query performance tracking, slow query alerts