pgdbm 0.3.0

Production-ready async PostgreSQL utilities with connection pooling, migrations, and testing support

pgdbm

A PostgreSQL library for Python that provides high-level async database operations with built-in migration management, connection pooling, and testing utilities.

Quick Architecture Decision

Choose your pattern based on your needs:

If you have...	Use this pattern	See example
Single service	Single Database Manager	Quick Start below
Multiple services	Shared Pool Pattern	microservices/
Multi-tenant SaaS	Schema Isolation	saas-app/
Reusable library	Dual-Mode Pattern	Pattern Guide

New to pgdbm? Start with our Production Patterns Guide to avoid common pitfalls and learn best practices from real-world experience.

Key Features

• High Performance - Built on asyncpg, the fastest PostgreSQL driver for Python
• Migration System - Version-controlled schema migrations with automatic ordering
• Testing Support - Fixtures and utilities for database testing
• Module Isolation - Prevent table conflicts when modules share databases
• Monitoring - Track slow queries and connection pool metrics
• Type Safe - Full type hints and Pydantic integration

Design intent: dual ownership

pgdbm is designed so a module can either:

• Own its database when run independently (it creates a pool and runs its own migrations), or
• Use a database owned by another component via a shared pool while still running its own migrations into a schema namespace.

This keeps modules portable: the same code can run as a standalone service or as part of a larger app.

Installation

# Install using uv (recommended)
uv add pgdbm

# Or using pip
pip install pgdbm

# Install with CLI support (optional)
pip install pgdbm[cli]

Claude Code Skills

pgdbm provides expert guidance skills for Claude Code that teach Claude how to work with the library effectively. When you use Claude Code with pgdbm, these skills automatically activate to provide:

• ✅ Production-ready code examples
• ✅ Best practices and patterns
• ✅ Common pitfalls to avoid
• ✅ Architecture guidance
• ✅ Testing strategies

No manual needed - just ask Claude naturally and the right skills load automatically!

Installation

# In Claude Code terminal, add the marketplace
/plugin marketplace add juanre/ai-tools

# Install all pgdbm skills (recommended)
/plugin install pgdbm@juanre-ai-tools

# Or install individual skills
/plugin install pgdbm-shared-pool@juanre-ai-tools
/plugin install pgdbm-testing@juanre-ai-tools

Available Skills

Skill	Description	Install
pgdbm	All pgdbm skills (recommended)	/plugin install pgdbm@juanre-ai-tools
pgdbm-choosing-pattern	Choose the right pattern	/plugin install pgdbm-choosing-pattern@juanre-ai-tools
pgdbm-shared-pool	Production connection pooling	/plugin install pgdbm-shared-pool@juanre-ai-tools
pgdbm-dual-mode	Portable database libraries	/plugin install pgdbm-dual-mode@juanre-ai-tools
pgdbm-standalone	Standalone service pattern	/plugin install pgdbm-standalone@juanre-ai-tools
pgdbm-testing	Database testing patterns	/plugin install pgdbm-testing@juanre-ai-tools
pgdbm-usage	Basic operations	/plugin install pgdbm-usage@juanre-ai-tools
pgdbm-core-api	Complete API reference	/plugin install pgdbm-core-api@juanre-ai-tools
pgdbm-migrations-api	Migrations API reference	/plugin install pgdbm-migrations-api@juanre-ai-tools
pgdbm-common-mistakes	Common pitfalls to avoid	/plugin install pgdbm-common-mistakes@juanre-ai-tools

How It Works

Example: Setting up a FastAPI app

You ask:

"Help me build a FastAPI app with PostgreSQL connection pooling"

What happens:

1. Claude sees "FastAPI", "PostgreSQL", "connection pooling"
2. Automatically loads pgdbm-shared-pool skill
3. Provides expert guidance with production patterns
4. Shows you exactly what you need

Result: Production-ready code following best practices, with proper pool management and schema isolation!

Example: Testing database code

You ask:

"Show me how to test my database code with fixtures"

What happens:

1. Claude sees "test", "database", "fixtures"
2. Loads pgdbm-testing skill
3. Guides you through test setup and fixtures
4. Shows you how to use test_db and test_db_with_schema

Result: Complete test setup with automatic database cleanup and isolation!

Quick Start

Production-Ready Pattern (Recommended)

For production applications, use the shared pool pattern - it's the most efficient and scalable:

# app.py - Complete production setup
from contextlib import asynccontextmanager
from fastapi import FastAPI, Depends
from pgdbm import AsyncDatabaseManager, DatabaseConfig
from pgdbm.migrations import AsyncMigrationManager

@asynccontextmanager
async def lifespan(app: FastAPI):
    # Create ONE shared pool for entire application
    config = DatabaseConfig(
        connection_string="postgresql://localhost/myapp",
        # Pool sizing: start small, then tune using metrics and your DB's max_connections
        min_connections=5,
        max_connections=20,
    )
    shared_pool = await AsyncDatabaseManager.create_shared_pool(config)

    # Create schema-specific managers (share the pool)
    app.state.dbs = {
        'users': AsyncDatabaseManager(pool=shared_pool, schema="users"),
        'orders': AsyncDatabaseManager(pool=shared_pool, schema="orders"),
    }

    # Run migrations for each schema
    for name, db in app.state.dbs.items():
        migrations = AsyncMigrationManager(db, f"migrations/{name}", name)
        await migrations.apply_pending_migrations()

    yield
    await shared_pool.close()

app = FastAPI(lifespan=lifespan)

# Use with dependency injection
@app.post("/users")
async def create_user(email: str, request: Request):
    db = request.app.state.dbs['users']
    user_id = await db.fetch_value(
        "INSERT INTO {{tables.users}} (email) VALUES ($1) RETURNING id",
        email
    )
    return {"id": user_id}

Simple Single-Service Setup

For simple applications with just one service:

from pgdbm import AsyncDatabaseManager, DatabaseConfig, AsyncMigrationManager

# Configure and connect
config = DatabaseConfig(connection_string="postgresql://localhost/myapp")
db = AsyncDatabaseManager(config)
await db.connect()

# Apply migrations
migrations = AsyncMigrationManager(db, migrations_path="./migrations")
await migrations.apply_pending_migrations()

# Use your database
user_id = await db.fetch_value(
    "INSERT INTO {{tables.users}} (email) VALUES ($1) RETURNING id",
    "[e-mail]"
)

# Clean up
await db.disconnect()

Migration Files

-- migrations/001_initial.sql
CREATE TABLE IF NOT EXISTS {{tables.users}} (
    id SERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

💡 Important: Always use {{tables.tablename}} syntax - it automatically handles schema prefixes!

Core Patterns

1. One Pool, Many Schemas

This is the most important pattern in pgdbm:

# ✅ CORRECT: One shared pool for entire application
shared_pool = await AsyncDatabaseManager.create_shared_pool(config)
users_db = AsyncDatabaseManager(pool=shared_pool, schema="users")
orders_db = AsyncDatabaseManager(pool=shared_pool, schema="orders")
billing_db = AsyncDatabaseManager(pool=shared_pool, schema="billing")

# ❌ WRONG: Multiple pools (wastes connections, hits limits)
users_db = AsyncDatabaseManager(DatabaseConfig(...))   # Creates own pool
orders_db = AsyncDatabaseManager(DatabaseConfig(...))  # Another pool - BAD!

Why? PostgreSQL has connection limits. Multiple pools waste connections and can exhaust your database. One shared pool is more efficient and prevents connection errors.

2. Module Isolation with Schemas

When multiple modules share a database, use schemas to prevent table conflicts:

# Each module gets its own schema namespace
user_db = AsyncDatabaseManager(pool=shared_pool, schema="user_module")
blog_db = AsyncDatabaseManager(pool=shared_pool, schema="blog_module")

# Both can have a "users" table without conflict:
# - user_module.users
# - blog_module.users

The {{tables.tablename}} syntax in queries automatically expands to the correct schema-qualified name.

3. Migration Management

# Migrations know their schema from the database manager
migrations = AsyncMigrationManager(
    db,                           # Schema is already set in db
    migrations_path="./migrations",
    module_name="myservice"       # Unique identifier for this service
)
result = await migrations.apply_pending_migrations()

# ⚠️ Common mistake: Don't pass schema to AsyncMigrationManager
# migrations = AsyncMigrationManager(db, path, schema="foo")  # WRONG - no schema param!

4. Transactions with Template Substitution

Transactions automatically handle {{tables.}} template substitution:

async with db.transaction() as tx:
    # Create a user
    user_id = await tx.fetch_value(
        "INSERT INTO {{tables.users}} (email) VALUES ($1) RETURNING id",
        "[e-mail]"
    )

    # Create user profile in same transaction
    await tx.execute(
        "INSERT INTO {{tables.profiles}} (user_id, bio) VALUES ($1, $2)",
        user_id,
        "Software developer"
    )
    # Automatically committed on success, rolled back on exception

All transaction methods return dictionaries for consistency:

• tx.execute() - Execute without results
• tx.fetch_one() - Get single row as dict
• tx.fetch_all() - Get all rows as list of dicts
• tx.fetch_value() - Get single value

5. Testing Support

Built-in fixtures for database tests:

# conftest.py
from pgdbm.fixtures.conftest import *

# test_users.py
async def test_create_user(test_db):
    # Automatic test database with cleanup
    await test_db.execute("""
        CREATE TABLE users (id SERIAL PRIMARY KEY, email TEXT)
    """)

    user_id = await test_db.execute_and_return_id(
        "INSERT INTO users (email) VALUES ($1)",
        "[e-mail]"
    )
    assert user_id == 1

6. Monitoring

Track database performance:

from pgdbm import MonitoredAsyncDatabaseManager

db = MonitoredAsyncDatabaseManager(
    config=config,
    slow_query_threshold_ms=100  # Log queries over 100ms
)

# Get metrics
metrics = await db.get_metrics()
slow_queries = await db.get_slow_queries(limit=10)

7. Production TLS and Timeouts

Enable TLS with certificate verification and enforce server-side timeouts:

from pgdbm import AsyncDatabaseManager, DatabaseConfig

config = DatabaseConfig(
    connection_string="postgresql://db.example.com/app",
    ssl_enabled=True,
    ssl_mode="verify-full",           # 'require' | 'verify-ca' | 'verify-full'
    ssl_ca_file="/etc/ssl/certs/ca.pem",
    # Optional mutual TLS:
    # ssl_cert_file="/etc/ssl/certs/client.crt",
    # ssl_key_file="/etc/ssl/private/client.key",

    # Sensible timeouts (ms)
    statement_timeout_ms=60_000,
    idle_in_transaction_session_timeout_ms=60_000,
    lock_timeout_ms=5_000,
)

db = AsyncDatabaseManager(config)
await db.connect()

Notes:

• Use verify-full for strict hostname and certificate validation in production.
• Timeouts are applied via server_settings; you can override or disable by passing None.

Examples

The examples/ directory contains applications:

• todo-app/ - REST API with migrations, testing, and error handling
• saas-app/ - Multi-tenant SaaS application
• microservices/ - Multiple services sharing a connection pool

Documentation

• Production Patterns Guide - START HERE! Real-world patterns and best practices
• 📋 Quick Reference - Cheatsheet for common patterns and commands
• Quickstart Guide - Step-by-step getting started
• CLI Reference - Command-line interface documentation
• Patterns Guide - Deployment patterns, dual-mode libraries, and framework integration
• Migration Guide - Schema versioning and {{tables.}} syntax
• API Reference - Complete API documentation
• Testing Guide - Testing best practices

Contributing

Short version:

• Requirements: Python 3.9+, PostgreSQL 12+, uv (or pip)
• Setup:
  • uv: uv sync
  • pip: pip install -e ".[dev]"
  • hooks: pre-commit install
• Run tests: pytest
• Lint/type-check: pre-commit run --all-files

Notes:

• Integration tests use ephemeral databases; you can override with env vars like TEST_DB_HOST, TEST_DB_PORT, TEST_DB_USER, TEST_DB_PASSWORD.
• Keep PRs small and focused, include tests/docs for user-visible changes.
• Style is enforced via Black/Isort/Ruff/Mypy; run pre-commit locally before pushing.
• New contributors should read the Repository Guidelines for a concise overview of project structure, tooling, and expectations.

Testing & Modular Usage at a Glance

• Import pgdbm.fixtures.conftest in tests/conftest.py to unlock fixtures like test_db, test_db_with_schema, and test_db_with_tables; each test gets a fresh database, while test_db_isolated offers a faster transaction/savepoint path when you only need rollback semantics.
• Use markers (slow, integration, unit) and skip long runs locally with pytest -m "not slow"; monitor coverage via pytest --cov src/pgdbm --cov-report=term-missing.
• Libraries and services should accept either a connection string or an injected AsyncDatabaseManager, always run their own migrations (module_name scoped) and use {{tables.*}} templating so the same code works standalone or inside a shared pool; in pooled mode the schema prefix is inserted during query preparation, not via search_path, so every statement must use the template helpers.
• Parent apps create a single shared pool and hand schema-bound managers to each module (AsyncDatabaseManager(pool=shared_pool, schema="module"))—library composition is cooperative, meaning the host is responsible for wiring every participating module before they can reference each other.
• See docs/testing.md and docs/production-patterns.md for deeper walkthroughs and code samples.

License

MIT License - see LICENSE for details.