HireFire

This is a Python package for HireFire – The Heroku Process Manager:

HireFire has the ability to automatically scale your web and worker dynos up and down when necessary. When new jobs are queued in to your application’s worker queue [..], HireFire will spin up new worker dynos to process these jobs. When the queue is empty, HireFire will shut down the worker dynos again so you’re not paying for idle workers.

HireFire also has the ability to scale your web dynos. When your web application experiences heavy traffic during certain times of the day, or if you’ve been featured somewhere, chances are your application’s backlog might grow to a point that your web application will run dramatically slow, or even worse, it might result in a timeout. In order to prevent this, HireFire will automatically scale your web dynos up when traffic increases to ensure that your application runs fast at all times. When traffic decreases, HireFire will spin down your web dynos again.

—from the HireFire frontpage

It supports the following Python queuing systems as backends:

Feel free to contribute other backends if you’re using a different queuing system.

Installation

Install the HireFire package with your favorite installer, e.g.:

pip install HireFire

Sign up for HireFire and set the HIREFIRE_TOKEN environment variable with the Heroku CLI as provided on the specific HireFire application page, e.g.:

heroku config:set HIREFIRE_TOKEN=f69f0c0ddebe041248daf187caa6abb3e5d943ca

Now follow the quickstart guide below and don’t forget to tweak the options in the HireFire management system.

For more help see the Hirefire documentation.

Configuration

The hirefire Python package currently supports two frameworks: Django and Tornado. Implementations for other frameworks are planned but haven’t been worked on: Flask, Pyramid (PasteDeploy), WSGI middleware, ..

Feel free to contribute one if you can’t wait.

The following guides imply you have defined at least one hirefire.procs.Proc subclass defined matching one of the processes in your Procfile. For each process you want to monitor you have to have one subclass.

For example here is a Procfile which uses RQ for the “worker” proccess:

web: python manage.py runserver
worker: DJANGO_SETTINGS_MODULE=mysite.settings rqworker high default low

Define a RQProc subclass somewhere in your project, e.g. mysite/procs.py, with the appropriate attributes (name and queues):

from hirefire.procs.rq import RQProc

class WorkerProc(RQProc):
    name = 'worker'
    queues = ['high', 'default', 'low']

See the procs API documentation if you’re using another backend. Now follow the framework specific guidelines below.

Django

Setting up HireFire support for Django is easy:

  1. Add 'hirefire.contrib.django.middleware.HireFireMiddleware' to your MIDDLEWARE setting:

    # Use ``MIDDLEWARE_CLASSES`` prior to Django 1.10
    MIDDLEWARE = [
        'hirefire.contrib.django.middleware.HireFireMiddleware',
        # ...
    ]
    

    Make sure it’s the first item in the list/tuple.

  2. Set the HIREFIRE_PROCS setting to a list of dotted paths to your procs. For the above example proc:

    HIREFIRE_PROCS = ['mysite.procs.WorkerProc']
    
  3. Set the HIREFIRE_TOKEN setting to the token that HireFire shows on the specific application page (optional):

    HIREFIRE_TOKEN = 'f69f0c0ddebe041248daf187caa6abb3e5d943ca'
    

    This is only needed if you haven’t set the HIREFIRE_TOKEN environment variable already (see the installation section how to do that on Heroku).

  4. Check that the middleware has been correctly setup by opening the following URL in a browser:

    http://localhost:8000/hirefire/test
    

    You should see an empty page with ‘HireFire Middleware Found!’.

    You can also have a look at the page that HireFire checks to get the number of current tasks:

    http://localhost:8000/hirefire/<HIREFIRE_TOKEN>/info
    

    where <HIREFIRE_TOKEN> needs to be replaced with your token or – in case you haven’t set the token in your settings or environment – just use development.

Tornado

Setting up HireFire support for Tornado is also easy:

  1. Use hirefire.contrib.tornado.handlers.hirefire_handlers when defining your tornado.web.Application instance:

    import os
    from hirefire.contrib.tornado.handlers import hirefire_handlers
    
    application = tornado.web.Application([
        # .. some patterns and handlers
    ] + hirefire_handlers(os.environ['HIREFIRE_TOKEN'],
                          ['mysite.procs.WorkerProc']))
    

    Make sure to pass a list of dotted paths to the hirefire_handlers function.

  2. Set the HIREFIRE_TOKEN environment variable to the token that HireFire shows on the specific application page (optional):

    export HIREFIRE_TOKEN='f69f0c0ddebe041248daf187caa6abb3e5d943ca'
    

    See the installation section above for how to do that on Heroku.

  3. Check that the handlers have been correctly setup by opening the following URL in a browser:

    http://localhost:8888/hirefire/test
    

    You should see an empty page with ‘HireFire Middleware Found!’.

    You can also have a look at the page that HireFire checks to get the number of current tasks:

    http://localhost:8888/hirefire/<HIREFIRE_TOKEN>/info
    

    where <HIREFIRE_TOKEN> needs to be replaced with your token or – in case you haven’t set the token as an environment variable – just use development.

Flask

Setting up HireFire support for Flask is (again!) also easy:

  1. The module hirefire.contrib.flask.blueprint provides a build_hirefire_blueprint factory function that should be called with HireFire token and procs as arguments. The result is a blueprint providing the hirefire routes and which should be registered inside your app:

    import os
    from flask import Flask
    from hirefire.contrib.flask.blueprint import build_hirefire_blueprint
    
    app = Flask(__name__)
    bp = build_hirefire_blueprint(os.environ['HIREFIRE_TOKEN'],
                                  ['mysite.procs.WorkerProc'])
    app.register_blueprint(bp)
    

    Make sure to pass a list of dotted paths to the build_hirefire_blueprint function.

  2. Set the HIREFIRE_TOKEN environment variable to the token that HireFire shows on the specific application page (optional):

    export HIREFIRE_TOKEN='f69f0c0ddebe041248daf187caa6abb3e5d943ca'
    

    See the installation section above for how to do that on Heroku.

  3. Check that the handlers have been correctly setup by opening the following URL in a browser:

    http://localhost:8080/hirefire/test
    

    You should see an empty page with ‘HireFire Middleware Found!’.

    You can also have a look at the page that HireFire checks to get the number of current tasks:

    http://localhost:8080/hirefire/<HIREFIRE_TOKEN>/info
    

    where <HIREFIRE_TOKEN> needs to be replaced with your token or – in case you haven’t set the token as an environment variable – just use development.

Proc backends

Two base classes are includes that you can use to implement custom backends. All the other contributed backends use those base classes, too.

hirefire.procs.Proc

class hirefire.procs.Proc(name=None, queues=None)[source]

The base proc class. Use this to implement custom queues or other behaviours, e.g.:

import mysite.sekrit
from hirefire import procs

class MyCustomProc(procs.Proc):
    name = 'worker'
    queues = ['default']

    def quantity(self):
        return sum([mysite.sekrit.count(queue)
                    for queue in self.queues])
Parameters:
  • name (str) – the name of the proc (required)
  • queues (str or list of str) – list of queue names to check (required)
name = None

The name of the proc

quantity(**kwargs)[source]

Returns the aggregated number of tasks of the proc queues.

Needs to be implemented in a subclass.

kwargs must be captured even when not used, to allow for future extensions.

The only kwarg currently implemented is cache, which is a dictionary made available for cross-proc caching. It is empty when the first proc is processed.

queues = []

The list of queues to check

hirefire.procs.ClientProc

class hirefire.procs.ClientProc(*args, **kwargs)[source]

A special subclass of the Proc class that instantiates a list of clients for each given queue, e.g.:

import mysite.sekrit
from hirefire import procs

class MyCustomProc(procs.ClientProc):
    name = 'worker'
    queues = ['default']

    def client(self, queue):
        return mysite.sekrit.Client(queue)

    def quantity(self):
        return sum([client.count(queue)
                    for client in self.clients])

See the implementation of the RQProc class for an example.

client(queue, *args, **kwargs)[source]

Returns a client instance for the given queue to be used in the quantity method.

Needs to be implemented in a subclass.

quantity(**kwargs)

Returns the aggregated number of tasks of the proc queues.

Needs to be implemented in a subclass.

kwargs must be captured even when not used, to allow for future extensions.

The only kwarg currently implemented is cache, which is a dictionary made available for cross-proc caching. It is empty when the first proc is processed.

Contributed backends

See the following API overview of the other supported queuing backends.

Issues & Feedback

For bug reports, feature requests and general feedback, please use the Github issue tracker.

Thanks

Many thanks to the folks at Hirefire for building a great tool for the Heroku ecosystem.

Authors

  • Emmanuel Leblond
  • Jannis Leidel
  • Marc Tamlyn
  • Ryan Hiebert
  • Ryan West
  • Shravan Reddy

Changes

0.5 (2017-01-20)

  • Add simple_queues feature to Celery Proc, to enable optionally skipping one inspect call. (#23)
  • Make the default quantity 0 by the recommendation of the HireFire team.

0.4 (2016-06-04)

  • Consider all Celery tasks including the ones in the active, reserved and scheduled queues. This fixes a long standing issue where tasks in those queues could have been dropped if HireFire were to scale down the workers. Many thanks to Ryan Hiebert for working on this.
  • Removed django-pq backend since the library is unmaintained.

0.3 (2015-05-05)

  • Added Flask blueprint.
  • Fixed Celery queue length measurement for AMQP backends.

0.2.2 (2014-11-27)

  • Fixed a regression in 0.2.1 fix. Thanks to Ryan West.

0.2.1 (2014-05-27)

  • Fix the RQ Proc implementation to take the number of task into account that are currently being processed by the workers to prevent accidental shutdown mid-processing. Thanks to Jason Lantz for the report and initial patch.

0.2 (2014-04-20)

  • Got rid of d2to1 dependency.
  • Added django-pq backend.
  • Ported to Python 3.
  • Added Tornado contrib handlers.

0.1 (2013-02-17)