Choosing the Right pgdbm Pattern

Overview

Core Principle: Choose pattern based on deployment context and reusability needs.

pgdbm supports three main patterns. This skill helps you choose the right one in <30 seconds without reading multiple docs.

Quick Decision Tree

What are you building?
│
├─ Reusable library/package for PyPI?
│  └─ → DUAL-MODE LIBRARY pattern
│     • Accept connection_string OR db_manager
│     • Works standalone AND embedded
│
├─ Single application with multiple services/modules?
│  └─ → SHARED POOL pattern
│     • ONE pool, many schema-isolated managers
│     • Most efficient for production
│
└─ Simple standalone service/microservice?
   └─ → STANDALONE pattern
      • AsyncDatabaseManager(DatabaseConfig(...))
      • Simplest setup

Pattern Selection Table

If you have...	Use this pattern	Key indicator
Library published to PyPI	Dual-Mode	Code needs to work in someone else's app
FastAPI monolith with routers	Shared Pool	Multiple services, same process
Multiple services, same app	Shared Pool	Need connection efficiency
Background worker (separate process)	Standalone	Different OS process
Simple microservice	Standalone	One service, own database
Multi-tenant SaaS	Shared Pool	Many tenants, schema isolation

Detailed Decision Criteria

Use DUAL-MODE LIBRARY When:

Triggers:

• You're publishing to PyPI
• You're building internal shared library
• Code will be used by other developers
• Library might be used alongside other pgdbm libraries

Key characteristics:

• Unknown deployment context
• Must work standalone OR embedded
• Always runs own migrations
• Schema-agnostic via

  {{tables.}}

Red flags you need this:

• You're writing

  import mylib

  in your README
• Someone else's app will import your code
• You don't control the database configuration

Example minimal setup:

class MyLibrary:
    def __init__(
        self,
        connection_string: Optional[str] = None,
        db_manager: Optional[AsyncDatabaseManager] = None,
    ):
        if not connection_string and not db_manager:
            raise ValueError("Provide one or the other")
        self._external_db = db_manager is not None
        self.db = db_manager
        self._connection_string = connection_string

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

        # ALWAYS run migrations
        migrations = AsyncMigrationManager(
            self.db, "migrations", module_name="mylib"
        )
        await migrations.apply_pending_migrations()

For complete implementation: See

pgdbm:dual-mode-library

skill

Use SHARED POOL When:

Triggers:

• Multiple services in same Python process
• FastAPI app with multiple routers
• Monolith with logical service separation
• Multi-tenant SaaS application

Key characteristics:

• Services share database connection pool
• Each service gets own schema
• Connection efficiency critical
• All services in same application

Red flags you need this:

• You're creating multiple

  AsyncDatabaseManager(DatabaseConfig(...))

  in same app
• You're hitting PostgreSQL connection limits
• You have >2 routers/services needing database

Example minimal setup:

# In lifespan
config = DatabaseConfig(connection_string="postgresql://...")
shared_pool = await AsyncDatabaseManager.create_shared_pool(config)

# Each service gets schema-isolated manager
users_db = AsyncDatabaseManager(pool=shared_pool, schema="users")
orders_db = AsyncDatabaseManager(pool=shared_pool, schema="orders")

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

For complete implementation: See

pgdbm:shared-pool-pattern

skill

Use STANDALONE When:

Triggers:

• Single service, dedicated database
• Background worker (separate process)
• Simple microservice
• Development/testing
• Service can't share connections

Key characteristics:

• Creates own connection pool
• Controls full database lifecycle
• Simplest pattern
• Most straightforward

Red flags you need this:

• Separate OS process (can't share pool anyway)
• Only one logical service
• Development environment
• Learning pgdbm

Example minimal setup:

config = DatabaseConfig(connection_string="postgresql://...")
db = AsyncDatabaseManager(config)
await db.connect()

migrations = AsyncMigrationManager(db, "migrations", module_name="myservice")
await migrations.apply_pending_migrations()

# Use db
user_id = await db.fetch_value(
    "INSERT INTO {{tables.users}} (email) VALUES ($1) RETURNING id",
    email
)

await db.disconnect()

For complete implementation: See

pgdbm:standalone-service

skill

Wrong Pattern Red Flags

🚫 You Chose WRONG Pattern If:

Using Standalone but should use Shared Pool:

• You create multiple

  AsyncDatabaseManager(DatabaseConfig(...))

  to same database
• You see warning: "Creating another connection pool to..."
• You're hitting PostgreSQL

  max_connections

  limit
• All services run in same Python process

Using Shared Pool but should use Dual-Mode:

• Your code will be imported by other apps
• You're publishing to PyPI
• Users need to provide their own database

Using Dual-Mode but should use Standalone:

• You control entire deployment
• Code never used as library
• Adding unnecessary complexity

Common Ambiguous Cases

"Background worker, same database as main app"

Question to ask: Same process or different process?

• Same process (threads/asyncio tasks): Use Shared Pool
• Different process (separate Python process): Use Standalone
  • Each process creates own pool (can't share across processes)
  • Use schema isolation to prevent table conflicts
  • Worker uses schema="worker", main uses schema="main"

"Multiple microservices, containerized"

Answer: Each container = Standalone pattern

• Containers are separate processes
• Can't share pools across containers
• Use schema isolation if sharing same database
• Each service:

  AsyncDatabaseManager(DatabaseConfig(...))

"Library used internally, not published"

Answer: Still use Dual-Mode if used by multiple apps

• "Internal" doesn't mean "standalone"
• If imported by different projects, use Dual-Mode
• If only used in one app, can use Shared Pool

Pattern Comparison

Aspect	Standalone	Dual-Mode	Shared Pool
Complexity	Low	Medium	Medium
Flexibility	Low	High	Medium
Connection Efficiency	Low	Varies	High
Use Case	Simple services	Reusable libraries	Multi-service apps
Pool Creation	Creates own	Conditional	Uses provided
Migration Management	Owns	Always runs own	Each service runs own
Best For	Microservices, workers	PyPI packages	Monoliths, multi-tenant

Decision Process Example

Scenario: "I'm building a FastAPI app with user authentication, blog posts, and comments"

Decision process:

1. Multiple services? Yes (auth, blog, comments)
2. Same Python process? Yes (all in FastAPI app)
3. Will be reused as library? No (application code)

Answer: Shared Pool Pattern

Why:

• Multiple services = need isolation
• Same process = can share pool
• Not reusable = don't need dual-mode flexibility

Quick Self-Check

Before implementing, ask:

1. Who creates the database manager?

   • Me = Standalone or Shared Pool
   • Could be me or someone else = Dual-Mode

2. How many services need database access?

   • 1 = Standalone (probably)
   • 2+ in same process = Shared Pool
   • 2+ in different processes = Standalone each

3. Will my code be imported by other projects?

   • Yes = Dual-Mode
   • No = Standalone or Shared Pool

Next Steps

Once you've chosen:

• Dual-Mode: See

  pgdbm:dual-mode-library

  for full implementation
• Shared Pool: See

  pgdbm:shared-pool-pattern

  for full implementation
• Standalone: See

  pgdbm:standalone-service

  for full implementation

All patterns use:

• {{tables.}}

  syntax (mandatory)
• Unique

  module_name

  in migrations (mandatory)
• Schema isolation for multi-service (recommended)

The Iron Rule

Whatever pattern you choose, NEVER:

• Create multiple pools to same database in same process
• Hardcode schema names in SQL
• Skip

  module_name

  in AsyncMigrationManager
• Switch

  db.schema

  at runtime

These violations break pgdbm's core assumptions.