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    davila7

    database-schema-designer

    davila7/database-schema-designer
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    About

    Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization...

    SKILL.md

    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

    Deep Dive: Normalization (SQL)

    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
    );
    
    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
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