celery-typed 0.2.0

Type-safe Pydantic serialization for Celery tasks

celery-typed

Type-safe Pydantic serialization for Celery tasks

A low-friction library that enables seamless Pydantic model serialization in Celery tasks using Kombu's preserializer functionality. Pass Pydantic models directly to your Celery tasks without any special decorators or manual serialization.

Features

• ✅ Zero-friction integration - No special task decorators or parameters needed
• ✅ Type-safe serialization - Full Pydantic model support with proper type hints
• ✅ Automatic registration - One-line setup in your Celery configuration
• ✅ Production ready - Based on Kombu's proven serialization system
• ✅ Extensible - Clean protocol-based design for custom preserializers

Installation

pip install celery-typed

Or with uv:

uv add celery-typed

Quick Start

1. Register the preserializer in your Celery app

# core/celery.py
from celery import Celery
from celery_typed import register_pydantic_serializer

app = Celery("myapp")
register_pydantic_serializer()  # One line setup!

# ... rest of your Celery configuration

2. Use Pydantic models directly in tasks

from pydantic import BaseModel
from myapp.celery import app

class UserModel(BaseModel):
    id: int
    name: str
    email: str

class ProcessingResult(BaseModel):
    status: str
    message: str
    user_id: int

@app.task
def process_user(user: UserModel) -> ProcessingResult:
    # Your task logic here
    return ProcessingResult(
        status="success",
        message=f"Processed user {user.name}",
        user_id=user.id
    )

3. Call tasks with Pydantic model instances

# Pass model instances directly
user = UserModel(id=1, name="Alice", email="[e-mail]")
result = process_user.delay(user)

# Get back a proper Pydantic model instance
processing_result: ProcessingResult = result.get()
print(processing_result.status)  # "success"

Comparison with Official Pydantic Support

Before (Official Celery + Pydantic)

@app.task(pydantic=True)  # Must remember this decorator
def process_user(user_data: dict) -> dict:  # Work with dicts
    user = UserModel(**user_data)  # Manual conversion
    # ... process user
    return result.model_dump()  # Manual serialization

# Usage
result = process_user.delay({'id': 1, 'name': 'Alice'})  # Pass dict
result_dict = result.get()  # Get back dict
result_obj = ProcessingResult(**result_dict)  # Manual conversion

After (celery-typed)

@app.task  # No special decorator needed
def process_user(user: UserModel) -> ProcessingResult:  # Real type hints
    # ... process user
    return result  # Return model directly

# Usage  
user = UserModel(id=1, name='Alice', email='[e-mail]')
result = process_user.delay(user)  # Pass model instance
result_obj = result.get()  # Get back model instance

How It Works

celery-typed uses Kombu's register_type functionality to automatically serialize and deserialize Pydantic models. When you pass a Pydantic model to a task:

1. Serialization: The model is packed into a JSON-serializable format containing:

   • Module path and class name for reconstruction
   • The model's data via model_dump()

2. Transmission: Standard Celery message passing (no changes needed)

3. Deserialization: The worker reconstructs the original model instance using the stored class information and data

This approach is based on the excellent implementation described in Dosu's blog post.

API Reference

register_pydantic_serializer()

Convenience function to register Pydantic model serialization for all BaseModel subclasses.

from celery_typed import register_pydantic_serializer

# Call once in your Celery app configuration
register_pydantic_serializer()

register_preserializer(preserializer)

Decorator factory for registering custom preserializers.

from celery_typed import register_preserializer, Preserializer

class MyCustomPreserializer:
    @classmethod
    def compatible_with(cls, type_: type) -> bool:
        return isinstance(type_, MyCustomType)
    
    @classmethod 
    def pack(cls, obj):
        return {"data": obj.serialize()}
        
    @classmethod
    def unpack(cls, data):
        return MyCustomType.deserialize(data["data"])

# Register for your custom type
register_preserializer(MyCustomPreserializer)(MyCustomType)

Preserializer Protocol

Protocol defining the interface for custom preserializers:

class Preserializer(Protocol):
    @classmethod
    def compatible_with(cls, type_: type) -> Literal[True]:
        """Check if type is compatible with this preserializer"""
        
    @classmethod
    def pack(cls, obj: Any) -> Any:
        """Pack object into JSON-serializable format"""
        
    @classmethod
    def unpack(cls, data: Any) -> object:
        """Unpack data back into original object"""

Requirements

• Python 3.10+
• Celery 5.2+
• Kombu 5.2+
• Pydantic 2.0+

Development

This project uses uv for dependency management. To set up a development environment:

# Clone the repository
git clone https://github.com/nwcell/celery-typed.git
cd celery-typed

# Install dependencies
uv sync --dev

# Run tests
uv run pytest

# Run linting
uv run ruff check
uv run ruff format

# Type checking
uv run mypy src/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Make sure to:

• Add tests for any new functionality
• Update documentation as needed
• Follow the existing code style (enforced by Ruff)
• Ensure all tests and type checks pass

License

This project is licensed under the MIT License - see the LICENSE file for details.

Credits

• Inspired by Dosu's blog post on Celery preserializers
• Built on top of Celery and Pydantic
• Uses Kombu's type registration system