castor-io 0.4.1

Minimalistic, embedded task manager.

Castor

Castor is a minimalistic, embedded task manager built on BeaverDB. It's designed to run background tasks (both process and thread-based) in applications where a complicated, distributed broker like Redis is overkill.

It embraces the minimalistic philosophy of BeaverDB while still being feature-full for the use cases where it makes sense.

Core Philosophy

• Explicit Over Implicit: The library avoids global state. Configuration is handled through explicit Manager objects, making applications more robust and testable.
• Single Responsibility: castor is a task queueing and execution library. Nothing else, nothing more.
• Developer Experience: The API is designed to be intuitive, requiring minimal boilerplate to turn a function into a background task.
• Decoupled Architecture: The application that enqueues a task is fully separate from the worker process that executes it. They communicate only through the shared database file.
• Targeted Concurrency: Provides clear, mandatory choices for both I/O-bound (thread) and CPU-bound (process) concurrency models on a per-task basis.

Installation

pip install castor-io

Quickstart

1. Create your application file

# main.py
import time
from beaver import BeaverDB
from castor import Manager

# 1. Setup the manager
db = BeaverDB("tasks.db")
manager = Manager(db)

# 2. Define a background task
@manager.task(mode='thread')
def send_email(recipient: str):
    """Simulates a background I/O task."""
    print(f"-> Sending email to {recipient}...")
    time.sleep(2)
    print(f"<- Email sent to {recipient}.")
    return {"recipient": recipient, "status": "sent"}

# 3. Dispatch the task (if running this file directly)
if __name__ == "__main__":
    print("--- Dispatching background task ---")
    email_task = send_email.delay("[e-mail]")
    print(f"Dispatched email task with ID: {email_task.id}")

    print("\n--- Waiting for result ---")
    result = email_task.join(timeout=5)
    print(f"Result from email task: {result}")
    print("\n--- Example finished ---")

2. Run the worker from your terminal

The worker needs to know where your Manager instance is. You provide this as an import path.

castor main:manager

You will see the worker start and process the task.

Starting server... Ctrl+C to stop.

Alternatively, run in interactive mode to see a rich dashboard with logs and statistics.

castor main:manager -i

3. Run your application

python main.py

You will see tasks being processed in the worker log.

Features

• Task Decorator: A simple @manager.task decorator to turn any function into a background task.
• Execution Modes: Explicitly define tasks as thread (for I/O-bound work) or process (for CPU-bound work).
• Task Handle: Calling .delay() on a task returns a TaskHandle object, allowing you to check the .status() or wait for the result.
• Synchronous and Asynchronous Results: Block for a result with .join() or wait for it asynchronously with .resolve().
• Reliable Backend: Uses beaver-db for a simple and reliable file-based persistence layer.
• CLI Worker: A built-in command-line interface to run the worker server.

Roadmap

This is a work in progress. The immediate roadmap includes:

• Process-based tasks: While the mode='process' is available in the API, the underlying process pool execution is not yet fully implemented. This is the highest priority.
• Monitoring UI: A more advanced terminal-based monitoring dashboard for the worker.
• Retries and error handling: More robust mechanisms for automatic retries and dead-letter queues.