Relational Databases

Purpose

This skill guides relational database selection and implementation across multiple languages. Choose the optimal database engine, ORM/query builder, and deployment strategy for transactional systems, CRUD applications, and structured data storage.

When to Use This Skill

Trigger this skill when:

• Building user authentication, content management, e-commerce applications
• Implementing CRUD operations (Create, Read, Update, Delete)
• Designing data models with relationships (users → posts, orders → items)
• Migrating schemas safely in production
• Setting up connection pooling for performance
• Evaluating serverless database options (Neon, PlanetScale, Turso)
• Integrating with frontend skills (forms, tables, dashboards, search-filter)

Skip this skill for:

• Time-series data at scale (use time-series databases)
• Real-time analytics (use columnar databases)
• Document-heavy workloads (use document databases)
• Key-value caching (use Redis, Memcached)

Quick Reference: Database Selection

Database Selection Decision Tree
═══════════════════════════════════════════════════════════

PRIMARY CONCERN?
├─ MAXIMUM FLEXIBILITY & EXTENSIONS (JSON, arrays, vector search)
│  └─ PostgreSQL
│     ├─ Serverless → Neon (scale-to-zero, database branching)
│     └─ Traditional → Self-hosted, AWS RDS, Google Cloud SQL
│
├─ EMBEDDED / EDGE DEPLOYMENT (local-first, global latency)
│  └─ SQLite or Turso
│     ├─ Global distribution → Turso (libSQL, edge replicas)
│     └─ Local-only → SQLite (embedded, zero-config)
│
├─ LEGACY SYSTEM / MYSQL REQUIRED
│  └─ MySQL
│     ├─ Serverless → PlanetScale (non-blocking migrations)
│     └─ Traditional → Self-hosted, AWS RDS, Google Cloud SQL
│
└─ RAPID PROTOTYPING
   ├─ Python → SQLModel (FastAPI) or SQLAlchemy 2.0
   ├─ TypeScript → Prisma (best DX) or Drizzle (performance)
   ├─ Rust → SQLx (compile-time checks)
   └─ Go → sqlc (type-safe code generation)

Quick Reference: ORM vs Query Builder

ORM vs Query Builder Selection
═══════════════════════════════════════════════════════════

TEAM PRIORITIES?
├─ DEVELOPMENT SPEED / DEVELOPER EXPERIENCE
│  └─ ORM (abstracts SQL, handles relations automatically)
│     ├─ Python → SQLAlchemy 2.0, SQLModel
│     ├─ TypeScript → Prisma (migrations, type generation)
│     ├─ Rust → SeaORM (Active Record + Data Mapper)
│     └─ Go → GORM, Ent
│
├─ PERFORMANCE / QUERY CONTROL
│  └─ Query Builder (SQL-like, zero abstraction overhead)
│     ├─ Python → SQLAlchemy Core, asyncpg
│     ├─ TypeScript → Drizzle, Kysely
│     ├─ Rust → SQLx (compile-time query validation!)
│     └─ Go → sqlc (generates types from SQL)
│
├─ TYPE SAFETY / COMPILE-TIME GUARANTEES
│  ├─ Rust → SQLx (queries checked at build time)
│  ├─ Go → sqlc (generates types from SQL)
│  ├─ TypeScript → Prisma or Drizzle
│  └─ Python → SQLModel (Pydantic integration)
│
└─ COMPLEX QUERIES / JOINS
   ├─ SQL-first → Query builders or raw SQL
   └─ ORM-friendly → SeaORM, SQLAlchemy ORM

Multi-Language Implementation

Python: SQLAlchemy 2.0 + SQLModel

Recommended Libraries:

• SQLAlchemy 2.0 (/websites/sqlalchemy_en_21) - ORM + Core, 7,090 snippets
• SQLModel - FastAPI integration, Pydantic validation
• asyncpg - High-performance async PostgreSQL driver

When to Use:

• Production applications requiring flexibility
• FastAPI/Starlette backends
• Async/await workflows

Quick Pattern:

from sqlmodel import SQLModel, Field, Session
class User(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    email: str = Field(unique=True, index=True)

See: references/orms-python.md for complete SQLAlchemy/SQLModel patterns, async workflows, and connection pooling.

TypeScript: Prisma vs Drizzle

Recommended Libraries:

• Prisma 6.x (/prisma/prisma, score: 96.4, 4,281 doc snippets) - Best DX, migrations
• Drizzle ORM (/drizzle-team/drizzle-orm-docs, score: 95.4, 4,037 snippets) - Performance, SQL-like

Quick Comparison:

• Prisma: Best DX, auto-generated types, migrations included
• Drizzle: Best performance, SQL-like syntax, zero overhead

See: references/orms-typescript.md for Prisma vs Drizzle detailed comparison, Kysely, TypeORM patterns.

Rust: SQLx (Compile-Time Checked)

Recommended Libraries:

• SQLx 0.8 - Compile-time query validation, async
• SeaORM 1.x - Full ORM with Active Record pattern
• Diesel 2.3 - Mature, stable (sync/async)

Quick Pattern:

use sqlx::FromRow;
#[derive(FromRow)]
struct User { id: i32, email: String, name: String }
// Compile-time checked queries (verified at build time!)
let user = sqlx::query_as::<_, User>("SELECT * FROM users WHERE email = $1")
    .bind("test@example.com").fetch_one(&pool).await?;

See: references/orms-rust.md for SQLx macros, SeaORM, Diesel patterns, and compile-time guarantees.

Go: sqlc (Type-Safe Code Generation)

Recommended Libraries:

• sqlc - Generates Go code from SQL queries
• GORM v2 - Full ORM with associations, hooks
• Ent - Graph-based ORM, schema as code
• pgx - High-performance PostgreSQL driver

Quick Pattern:

-- queries.sql: SQL annotations generate type-safe Go code
-- name: CreateUser :one
INSERT INTO users (email, name) VALUES ($1, $2) RETURNING *;

user, err := queries.CreateUser(ctx, db.CreateUserParams{Email: "test@example.com"})

See: references/orms-go.md for sqlc setup, GORM, Ent, and pgx patterns.

Connection Pooling

Recommended Pool Sizes:

• Web API (single instance): 10-20 connections
• Serverless (per function): 1-2 connections + pgBouncer
• Background workers: 5-10 connections

See: references/connection-pooling.md for configuration examples, sizing formulas, and monitoring strategies.

Migrations

Critical Principles:

1. Use multi-phase deployment for column drops (never drop directly in production)
2. Use CREATE INDEX CONCURRENTLY (PostgreSQL) to avoid blocking writes
3. Test migrations in staging with production-like data volume

Tools: Alembic (Python), Prisma Migrate (TypeScript), SQLx migrations (Rust), golang-migrate (Go)

See: references/migrations-guide.md for safe migration patterns, multi-phase deployments, and rollback strategies.

Serverless Databases

Database	Type	Key Feature	Best For
Neon	PostgreSQL	Database branching, scale-to-zero	Development workflows, preview environments
PlanetScale	MySQL (Vitess)	Non-blocking schema changes	MySQL apps, zero-downtime migrations
Turso	SQLite (libSQL)	Edge deployment, low latency	Edge functions, global distribution

See: references/serverless-databases.md for setup examples, branching workflows, and cost comparisons.

Frontend Integration

Common Integration Patterns:

• Forms skill: Form submission → API validation → Database CRUD (INSERT/UPDATE)
• Tables skill: Paginated queries → API → Table display with sorting/filtering
• Dashboards skill: Aggregation queries (COUNT, SUM) → API → KPI cards
• Search-filter skill: Full-text search (PostgreSQL tsvector) → Ranked results

See working examples in: examples/python-sqlalchemy/, examples/typescript-drizzle/, examples/rust-sqlx/

Bundled Resources

Reference Documentation

• references/postgresql-guide.md - PostgreSQL features (pgvector, PostGIS, TimescaleDB)
• references/mysql-guide.md - MySQL-specific patterns, PlanetScale integration
• references/sqlite-guide.md - SQLite patterns, Turso edge deployment
• references/orms-python.md - SQLAlchemy 2.0, SQLModel, asyncpg
• references/orms-typescript.md - Prisma, Drizzle, Kysely comparisons
• references/orms-rust.md - SQLx, SeaORM, Diesel
• references/orms-go.md - GORM, sqlc, Ent, pgx
• references/migrations-guide.md - Safe schema evolution patterns
• references/connection-pooling.md - Pool sizing and monitoring
• references/serverless-databases.md - Neon, PlanetScale, Turso deployment

Working Examples

• examples/python-sqlalchemy/ - SQLAlchemy 2.0 + FastAPI with pooling, migrations
• examples/typescript-prisma/ - Prisma + Next.js with schema, migrations
• examples/typescript-drizzle/ - Drizzle + Hono with type-safe queries
• examples/rust-sqlx/ - SQLx + Axum with compile-time checks
• examples/go-sqlc/ - sqlc + Gin with generated type-safe code

Utility Scripts

• scripts/validate_schema.py - Validate database schema structure, constraints
• scripts/generate_migration.py - Generate migration templates for common operations

Best Practices

Security:

• Always use parameterized queries (prevents SQL injection)
• Hash passwords with Argon2/bcrypt
• Use environment variables for connection strings
• Enable SSL/TLS in production

Performance:

• Use connection pooling (10-20 for web APIs)
• Create indexes on filtered/sorted columns
• Implement pagination for large result sets
• Use EXPLAIN ANALYZE for slow queries

Reliability:

• Test migrations in staging first
• Use transactions for multi-statement operations
• Monitor connection pool exhaustion
• Set up and test database backups

Development:

• Version control schema and migrations
• Use database branching (Neon) for features
• Write integration tests against real databases