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
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
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
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
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
);
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
-- 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);
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
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
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
);
-- 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;
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
Database-Specific Patterns: Add MySQL vs PostgreSQL vs SQLite variations
