database-schema-designer
БесплатноБез исполняемых скриптовНе проверенDesign robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint des
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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 |
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FAQ
Что делает скилл database-schema-designer?
Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models.
Как установить скилл database-schema-designer?
Скопируй папку скилла в ~/.claude/skills (вкладка Claude Code выше делает это одной командой), либо поставь как плагин.
Скилл database-schema-designer запускает скрипты?
Нет, скилл состоит только из инструкций (SKILL.md), без исполняемых скриптов.
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