Database Schema Designer Design production-ready database schemas with best practices built-in. 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 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" 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 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 Boolean -- PostgreSQL is_active BOOLEAN DEFAULT TRUE -- MySQL is_active TINYINT ( 1 ) DEFAULT 1 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 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 ) ; 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" } ) ; 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 ; 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 Advanced Patterns: Time-series, event sourcing, CQRS, multi-tenancy ORM Integration: TypeORM, Prisma, SQLAlchemy patterns Monitoring: Query performance tracking, slow query alerts