django-melnottam 0.1.1

A Python package for managing Django application workers using InterpreterPoolExecutor

Django Melnottam

⚠️ EXPERIMENTAL - This library is still in early development. APIs are subject to change without notice. Use at your own risk and with caution in production environments.

A Python package for managing Django application workers using InterpreterPoolExecutor from Python 3.14+. This package provides a clean abstraction for running web and task workers in isolated Python interpreters.

Intention

This library simplifies Django deployment by running web and task workers in a single process Rather than managing separate services, you get a unified, straightforward deployment experience. We've selected sensible defaults—including django-tasks-db for task queuing and django-prodserver for the production server—to create a plug-and-play system that integrates seamlessly with your existing Django applications.

Overview

django-melnottam manages multiple worker processes using Python 3.14's concurrent.futures.InterpreterPoolExecutor. It provides:

• Web Workers: ASGI/WSGI application servers using Hypercorn
• Task Workers: Django task queue workers using django-tasks-db
• Isolated Execution: Each worker runs in its own Python interpreter, avoiding GIL contention
• Graceful Shutdown: Coordinated shutdown with timeout handling
• Signal Management: Proper SIGINT handling and cleanup

Requirements

• Python 3.14+
• Django 6.0+
• django-tasks-db >= 0.12.0
• django-prodserver>=2.4.0
• hypercorn >= 0.18.0

Installation

From Pip

pip install django-melnottam

Preferred Deployment

This library includes a built-in django-prodserver backend that provides the recommended way to deploy your Django application with web and task workers.

Deployment Command

To use the preferred deployment method with django-prodserver, run the following command with environment variable substitution:

BIND_ADDRESS="0.0.0.0:$PORT" WORKERS="$WORKERS" TASK_WORKERS="$INSTANCE_TASK_WORKERS" python manage.py prodserver web-with-task-worker

Configuration in Django Settings

Ensure your settings.py includes the production process configuration:

from django_melnottam.config import PositiveIntegerValue, Value

INSTALLED_APPS = [
    # ...
    "django_tasks_db",
    "django_prodserver",
    # ...
]

TASKS = {
    "default": {
        "BACKEND": "django_tasks_db.DatabaseBackend",
        "QUEUES": ["default"],
    }
}

# Production process configuration with command-line parameter support

PRODUCTION_PROCESSES = {
    "web-with-task-worker": {
        "BACKEND": "django_melnottam.prodserver_backends.subinterpreter.SubinterpreterWorkerServer",
        "ARGS": {
            "app_path": "myapp.wsgi:application",
            "django_settings_module": "myapp.settings",
            "workers": WORKERS,
            "task_workers": TASK_WORKERS,
            "bind": BIND_ADDRESS,
        },
    },
}

Environment Variables

• PORT: The port to bind to (e.g., 8000)
• WORKERS: Number of web workers (e.g., 4)
• INSTANCE_TASK_WORKERS: Number of task workers (e.g., 2)

Usage

Command Line Interface

django-melnottam-cli <application> [options]

Arguments

• application: Path to your WSGI application (e.g., myapp.wsgi:application)

Options

• -w, --workers: Number of web workers (default: 2)
• -t, --task-workers: Number of task workers (default: 1)
• -b, --bind: Bind address for web server (default: 0.0.0.0:9001)
• -s, --settings: Django settings module (default: from DJANGO_SETTINGS_MODULE env var)
• -v, --verbose: Enable debug logging

Example

django-melnottam-cli myapp.wsgi:application \
  --workers 4 \
  --task-workers 2 \
  --bind 0.0.0.0:8000 \
  --settings myapp.settings.production

Programmatic Usage

from django_melnottam.manager import run_application

run_application(
    app_path="myapp.wsgi:application",
    workers=4,
    task_workers=2,
    bind="0.0.0.0:8000",
    django_settings_module="myapp.settings"
)