using-pgdbm

name	using-pgdbm
description	Use when setting up pgdbm database connections, migrations, or multi-service architecture - provides mental model of one pool/many schemas/template syntax and complete API reference without needing to read docs

Using pgdbm: Mental Model and Core Patterns

Overview

Core Principle: One shared connection pool, multiple schema-isolated managers, template-based queries.

pgdbm is designed so code can run standalone (creates own pool) OR embedded (uses shared pool) without changes. This flexibility comes from three key patterns:

1. ONE pool to rule them all - Never create multiple pools to the same database
2. Schema isolation - Each service/module gets its own PostgreSQL schema
3. Template syntax - {{tables.tablename}} works across all deployment modes

Quick Decision Tree

Need database access?
├─ Multiple services/modules in same app?
│  └─ Use SHARED POOL pattern
│     • Create ONE pool with create_shared_pool()
│     • Each service gets AsyncDatabaseManager(pool=shared_pool, schema="service_name")
│     • Each runs own migrations with unique module_name
│
├─ Building reusable library/package?
│  └─ Use DUAL-MODE pattern
│     • Accept EITHER connection_string OR db_manager parameter
│     • Create own pool if standalone, use provided if embedded
│     • ALWAYS run own migrations regardless
│
└─ Simple standalone service?
   └─ Use STANDALONE pattern
      • AsyncDatabaseManager(DatabaseConfig(...))
      • Run migrations with AsyncMigrationManager

Complete API Quick Reference

Creating Connections

# Shared pool (for multi-service apps)
from pgdbm import AsyncDatabaseManager, DatabaseConfig

config = DatabaseConfig(
    connection_string="postgresql://user:pass@host/db",
    min_connections=5,   # Start small, tune based on metrics
    max_connections=20,  # Keep under your DB's max_connections
)
shared_pool = await AsyncDatabaseManager.create_shared_pool(config)

# Schema-isolated managers (share the pool)
users_db = AsyncDatabaseManager(pool=shared_pool, schema="users")
orders_db = AsyncDatabaseManager(pool=shared_pool, schema="orders")

# Standalone (creates own pool)
db = AsyncDatabaseManager(config)
await db.connect()

Running Migrations

from pgdbm import AsyncMigrationManager

migrations = AsyncMigrationManager(
    db,                              # AsyncDatabaseManager instance
    migrations_path="./migrations",  # Directory with SQL files
    module_name="myservice"          # REQUIRED: unique identifier
)
result = await migrations.apply_pending_migrations()

Writing Queries

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

# With transactions
async with db.transaction() as tx:
    user_id = await tx.fetch_value(
        "INSERT INTO {{tables.users}} (email) VALUES ($1) RETURNING id",
        email
    )
    await tx.execute(
        "INSERT INTO {{tables.profiles}} (user_id, bio) VALUES ($1, $2)",
        user_id,
        "Bio text"
    )
    # Auto-commits on success, rolls back on exception

Migration File Format

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

CREATE INDEX IF NOT EXISTS users_email
    ON {{tables.users}} (email);

Why These Patterns Exist

Why ONE Shared Pool?

Problem: PostgreSQL has hard connection limits (typically 100-400). Each connection consumes:

• 5-10MB server memory
• File descriptors
• Authentication overhead

With pool-per-service:

• 5 services × (min=10, max=50) = capacity for 250 connections
• Most sit idle, but you hit database limits anyway
• Wastes resources

With shared pool:

• One pool (min=20, max=100) serves all 5 services
• Connections allocated dynamically based on actual demand
• Never exceeds database limits

Key insight: Services don't hit peak load simultaneously. Dynamic allocation beats static pre-allocation.

Why Schema Isolation?

Three options for multi-tenancy/multi-service:

Approach	Security	Complexity	Use When
Separate DBs	Strongest (PostgreSQL enforced)	High	Regulatory requirements, <1000 tenants
Separate Schemas	Good (application enforced)	Medium	100-10,000 tenants, B2B SaaS
Row-level (tenant_id)	Weakest (code must filter)	Low	Millions of tenants, consumer apps

pgdbm optimizes for schemas because:

• PostgreSQL-native isolation
• Shared connection pool (efficient)
• Can't accidentally query across tenants (different schema = error)
• Simpler than separate databases, safer than row-level

Why Template Syntax?

Problem: Hardcoding table names locks code to ONE deployment mode.

# ❌ WRONG: Hardcoded - only works in "myapp" schema
await db.execute('INSERT INTO "myapp".users (email) VALUES ($1)', email)

# ✅ CORRECT: Template - works everywhere
await db.execute('INSERT INTO {{tables.users}} (email) VALUES ($1)', email)

How it works:

• With schema="myapp": {{tables.users}} → "myapp".users
• Without schema: {{tables.users}} → users
• At query preparation time (not connection init for shared pools)

Benefits:

• Same code works standalone, shared pool, multi-tenant
• Can't forget schema qualification (template does it)
• Migrations portable across deployments
• Libraries can be composed (each uses own schema)

Common Patterns

FastAPI Integration

from contextlib import asynccontextmanager
from fastapi import FastAPI, Request

@asynccontextmanager
async def lifespan(app: FastAPI):
    # Create ONE shared pool
    config = DatabaseConfig(connection_string="postgresql://localhost/myapp")
    shared_pool = await AsyncDatabaseManager.create_shared_pool(config)

    # Schema-isolated managers
    app.state.dbs = {
        'users': AsyncDatabaseManager(pool=shared_pool, schema="users"),
        'orders': AsyncDatabaseManager(pool=shared_pool, schema="orders"),
    }

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

    yield
    await shared_pool.close()

app = FastAPI(lifespan=lifespan)

@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}

Dual-Mode Library Pattern

from typing import Optional
from pgdbm import AsyncDatabaseManager, DatabaseConfig

class MyLibrary:
    """Library that works standalone OR embedded."""

    def __init__(
        self,
        connection_string: Optional[str] = None,
        db_manager: Optional[AsyncDatabaseManager] = None,
    ):
        if not connection_string and not db_manager:
            raise ValueError("Provide connection_string OR db_manager")

        self._external_db = db_manager is not None
        self.db = db_manager
        self._connection_string = connection_string

    async def initialize(self):
        # Create pool only if not provided
        if not self._external_db:
            config = DatabaseConfig(connection_string=self._connection_string)
            self.db = AsyncDatabaseManager(config)
            await self.db.connect()

        # ALWAYS run own migrations
        migrations = AsyncMigrationManager(
            self.db,
            migrations_path="./migrations",
            module_name="mylib"  # Unique!
        )
        await migrations.apply_pending_migrations()

    async def close(self):
        # Only close if WE created the connection
        if self.db and not self._external_db:
            await self.db.disconnect()

Red Flags - STOP and Reconsider

If you're about to do any of these, you're missing the mental model:

• Creating multiple AsyncDatabaseManager instances with DatabaseConfig to same database
• Hardcoding schema names in SQL: INSERT INTO "myschema".users
• Not using {{tables.}} syntax in queries or migrations
• Passing schema parameter to AsyncMigrationManager (it reads from db)
• Not specifying module_name in migrations (causes conflicts)
• Switching db.schema at runtime (create separate managers instead)
• Using bare table names: INSERT INTO users (not schema-safe)

All of these mean: Review the mental model. You're fighting the library instead of using its design.

Complete API Reference

For COMPLETE AsyncDatabaseManager API: See pgdbm:core-api-reference skill

Includes ALL methods:

• execute, executemany, execute_and_return_id
• fetch_one, fetch_all, fetch_value
• fetch_as_model, fetch_all_as_model (Pydantic)
• copy_records_to_table (bulk operations)
• table_exists, get_pool_stats
• transaction, acquire
• Complete DatabaseConfig parameters (SSL, timeouts, retry, etc.)

For COMPLETE AsyncMigrationManager API: See pgdbm:migrations-api-reference skill

Includes ALL methods:

• apply_pending_migrations, get_applied_migrations
• get_pending_migrations, find_migration_files
• create_migration, rollback_migration
• get_migration_history
• Migration file format and naming conventions
• Checksum validation

Quick reference for most common operations:

# Query methods
count = await db.fetch_value("SELECT COUNT(*) FROM {{tables.users}}")
user = await db.fetch_one("SELECT * FROM {{tables.users}} WHERE id = $1", user_id)
users = await db.fetch_all("SELECT * FROM {{tables.users}}")
await db.execute("DELETE FROM {{tables.users}} WHERE id = $1", user_id)
user_id = await db.execute_and_return_id("INSERT INTO {{tables.users}} ...", ...)

# Transactions
async with db.transaction() as tx:
    await tx.execute(...)
    await tx.fetch_value(...)

# Migrations
migrations = AsyncMigrationManager(db, "migrations", module_name="myapp")
result = await migrations.apply_pending_migrations()

Template Syntax Reference

Available in all SQL queries and migration files:

{{tables.tablename}}  -- Expands to: "schema".tablename (or just tablename if no schema)
{{schema}}            -- Expands to: "schema" (or empty if no schema)

How expansion works:

# With schema="myapp"
"SELECT * FROM {{tables.users}}" → "SELECT * FROM \"myapp\".users"

# Without schema
"SELECT * FROM {{tables.users}}" → "SELECT * FROM users"

Complete Working Example

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

@asynccontextmanager
async def lifespan(app: FastAPI):
    # ONE shared pool
    config = DatabaseConfig(
        connection_string="postgresql://localhost/myapp",
        min_connections=5,
        max_connections=20,
    )
    shared_pool = await AsyncDatabaseManager.create_shared_pool(config)

    # Schema-isolated managers
    app.state.users_db = AsyncDatabaseManager(pool=shared_pool, schema="users")
    app.state.orders_db = AsyncDatabaseManager(pool=shared_pool, schema="orders")

    # Run migrations
    for db, path, name in [
        (app.state.users_db, "migrations/users", "users"),
        (app.state.orders_db, "migrations/orders", "orders"),
    ]:
        migrations = AsyncMigrationManager(db, path, name)
        await migrations.apply_pending_migrations()

    yield
    await shared_pool.close()

app = FastAPI(lifespan=lifespan)

When to Use What

Scenario	Pattern	Key Points
FastAPI app with 3 services	Shared pool	ONE create_shared_pool(), each service gets schema
Building PyPI package	Dual-mode library	Accept db_manager OR connection_string
Simple microservice	Standalone	AsyncDatabaseManager(config)
Multi-tenant SaaS	Shared pool + schemas	Each tenant = separate schema
Testing	Use fixtures	Import from pgdbm.fixtures.conftest

Related Skills

For pattern selection:

• pgdbm:choosing-pattern - Which pattern for your use case

For implementation:

• pgdbm:shared-pool-pattern - Multi-service apps
• pgdbm:dual-mode-library - PyPI packages
• pgdbm:standalone-service - Simple services

For complete API:

• pgdbm:core-api-reference - ALL AsyncDatabaseManager methods
• pgdbm:migrations-api-reference - ALL AsyncMigrationManager methods

For testing and quality:

• pgdbm:testing-database-code - Test fixtures
• pgdbm:common-mistakes - Anti-patterns