Production-Ready Schema

---

## 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`

---

## 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 |

---

## 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 |

---

## 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

---

<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)

```sql
-- 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
);

Deep Dive: Data Types

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

Deep Dive: Indexing Strategy

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 = 'x@y.com'
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

Deep Dive: Constraints

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

Deep Dive: Relationship Patterns

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
);

Deep Dive: NoSQL Design (MongoDB)

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": "jane@example.com"
  },
  "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" });

Deep Dive: Migrations

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;

Deep Dive: Performance Optimization

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

---

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