judoscale 1.0.0

Official Python adapter for Judoscale — the advanced autoscaler for Heroku

Judoscale

This is the official Python adapter for Judoscale. You can use Judoscale without it, but this gives you request queue time metrics and job queue time (for supported job processors).

It is recommended to install the specific web framework and/or background job library support as "extras" to the judoscale PyPI package. This ensures that checking if the installed web framework and/or background task processing library is supported happens at dependency resolution time.

Supported web frameworks

• Django
• Flask
• FastAPI

Supported job processors

• Celery (with Redis as the broker)
• RQ

Using Judoscale with Django

Install Judoscale for Django with:

$ pip install 'judoscale[django]'

Add Judoscale app to settings.py:

INSTALLED_APPS = [
    "judoscale.django",
    # ... other apps
]

This sets up the Judoscale middleware to capture request queue times.

Optionally, you can customize Judoscale in settings.py:

JUDOSCALE = {
    # LOG_LEVEL defaults to ENV["LOG_LEVEL"] or "INFO".
    "LOG_LEVEL": "DEBUG",

    # API_BASE_URL defaults to ENV["JUDOSCALE_URL"], which is set for you when you install Judoscale.
    # This is only exposed for testing purposes.
    "API_BASE_URL": "https://example.com",

    # REPORT_INTERVAL_SECONDS defaults to 10 seconds.
    "REPORT_INTERVAL_SECONDS": 5,
}

Once deployed, you will see your request queue time metrics available in the Judoscale UI.

Using Judoscale with Flask

Install Judoscale for Flask with:

$ pip install 'judoscale[flask]'

The Flask support for Judoscale is packaged into a Flask extension. Import the extension class and use like you normally would in a Flask application:

# app.py

from judoscale.flask import Judoscale

# If your app is a top-level global

app = Flask("MyFlaskApp")
app.config.from_object('...')  # or however you configure your app
judoscale = Judoscale(app)


# If your app uses the application factory pattern

judoscale = Judoscale()

def create_app():
    app = Flask("MyFlaskApp")
    app.config.from_object('...')  # or however you configure your app
    judoscale.init_app(app)
    return app

This sets up the Judoscale extension to capture request queue times.

Optionally, you can override Judoscale's own configuration via your application's configuration dictionary. The Judoscale Flask extension looks for a top-level "JUDOSCALE" key in app.config, which should be a dictionary, and which the extension uses to configure itself as soon as judoscale.init_app() is called.

JUDOSCALE = {
    # LOG_LEVEL defaults to ENV["LOG_LEVEL"] or "INFO".
    "LOG_LEVEL": "DEBUG",

    # API_BASE_URL defaults to ENV["JUDOSCALE_URL"], which is set for you when you install Judoscale.
    # This is only exposed for testing purposes.
    "API_BASE_URL": "https://example.com",

    # REPORT_INTERVAL_SECONDS defaults to 10 seconds.
    "REPORT_INTERVAL_SECONDS": 5,
}

Note the official recommendations for configuring Flask.

Using Judoscale with Celery and Redis

Install Judoscale for Celery with:

$ pip install 'judoscale[celery-redis]'

NOTE 1: The Judoscale Celery integration currently only works with the Redis broker.

NOTE 2: Using task priorities is currently not supported by judoscale. You can still use task priorities, but judoscale won't see and report metrics on any queues other than the default, unprioritised queue.

Judoscale can automatically scale the number of Celery workers based on the queue latency (the age of the oldest pending task in the queue).

To use the Celery integration, import judoscale_celery and call it with the Celery app instance. judoscale_celery should be called after you have set up and configured the Celery instance.

from celery import Celery
from judoscale.celery import judoscale_celery

broker = Celery("Broker", broker="redis://localhost:6379/0")
# Further setup
# broker.conf.update(...)
# ...

judoscale_celery(broker)

This sets up Judoscale to periodically calculate and report queue latency for each Celery queue.

If you need to change the Judoscale integration configuration, you can pass a dictionary of Judoscale configuration options to judoscale_celery to override the default Judoscale config variables:

judoscale_celery(broker, extra_config={"LOG_LEVEL": "DEBUG"})

Development

This repo includes a sample-apps directory containing apps you can run locally. These apps use the judoscale adapter, but they override API_BASE_URL so they're not connected to the real Judoscale API. Instead, they post API requests to https://requestinspector.com so you can observe the API behavior.

See the README in a sample app for details on how to set it up and run locally.

Contributing

judoscale uses Poetry for managing dependencies and packaging the project. Head over to the installations instructions and install Poetry, if needed.

Clone the repo with

$ git clone [e-mail]:judoscale/judoscale-python.git
$ cd judoscale-python

Verify that you are on a recent version of Poetry:

$ poetry --version
Poetry (version 1.3.1)

Install dependencies with Poetry and activate the virtualenv

$ poetry install --all-extras
$ poetry shell

Run tests with

$ python -m unittest discover -s tests