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adding task queue resources
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feeds/all.atom.xml

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<?xml version="1.0" encoding="utf-8"?>
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<feed xmlns="http://www.w3.org/2005/Atom"><title>Matt Makai</title><link href="http://www.fullstackpython.com/" rel="alternate"></link><link href="http://www.fullstackpython.com/feeds/all.atom.xml" rel="self"></link><id>http://www.fullstackpython.com/</id><updated>2014-03-23T18:45:18Z</updated></feed>
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<feed xmlns="http://www.w3.org/2005/Atom"><title>Matt Makai</title><link href="http://www.fullstackpython.com/" rel="alternate"></link><link href="http://www.fullstackpython.com/feeds/all.atom.xml" rel="self"></link><id>http://www.fullstackpython.com/</id><updated>2014-03-24T09:21:31Z</updated></feed>

source/content/pages/15-task-queues/1501-task-queues.markdown

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15 minutes
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## Task Queue resources
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The [Celery](http://www.celeryproject.org/) distributed task queue is the
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most commonly used Python library for handling asynchronous tasks and
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scheduling.
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[Queues.io](http://queues.io/) is a collection of task queue systems with
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short summaries for each one. The task queues are not all compatible with
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Python but ones that work with it are tagged with the "Python" keyword.
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The [RQ (Redis Queue)](http://python-rq.org/) is a simple Python
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library for queueing jobs and processing them in the background with workers.
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It is backed by Redis and it is designed to have a low barrier to entry.
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It should be integrated in your web stack easily.
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## Task queue projects
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* The [Celery](http://www.celeryproject.org/) distributed task queue is the
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most commonly used Python library for handling asynchronous tasks and
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scheduling.
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* The [RQ (Redis Queue)](http://python-rq.org/) is a simple Python
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library for queueing jobs and processing them in the background with workers.
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It is backed by Redis and it is designed to have a low barrier to entry.
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It should be integrated in your web stack easily.
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* [Taskmaster](https://github.com/dcramer/taskmaster) is a lightweight simple
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distributed queue for handling large volumes of one-off tasks.
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## Task queue resources
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* [Distributing work without Celery](http://justcramer.com/2012/05/04/distributing-work-without-celery/)
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provides a scenario in which Celery and RabbitMQ are not the right tool
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for scheduling asynchronous jobs.
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* [Queues.io](http://queues.io/) is a collection of task queue systems with
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short summaries for each one. The task queues are not all compatible with
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Python but ones that work with it are tagged with the "Python" keyword.
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* [Why Task Queues](http://www.slideshare.net/bryanhelmig/task-queues-comorichweb-12962619)
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is a presentation for what task queues are and why they are needed.
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* [How to use Celery with RabbitMQ](https://www.digitalocean.com/community/articles/how-to-use-celery-with-rabbitmq-to-queue-tasks-on-an-ubuntu-vps)
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is a detail walkthrough for using these tools on an Ubuntu VPS.
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* Heroku has a clear walkthrough for using
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[RQ for background tasks](https://devcenter.heroku.com/articles/python-rq).
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task-queues.html

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15 minutes</p>
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</li>
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</ul>
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<h2>Task Queue resources</h2>
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<h2>Task queue projects</h2>
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<ul>
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<li>
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<p>The <a href="http://www.celeryproject.org/">Celery</a> distributed task queue is the
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most commonly used Python library for handling asynchronous tasks and
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scheduling.</p>
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<p><a href="http://queues.io/">Queues.io</a> is a collection of task queue systems with
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short summaries for each one. The task queues are not all compatible with
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Python but ones that work with it are tagged with the "Python" keyword.</p>
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most commonly used Python library for handling asynchronous tasks and
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scheduling.</p>
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</li>
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<li>
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<p>The <a href="http://python-rq.org/">RQ (Redis Queue)</a> is a simple Python
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library for queueing jobs and processing them in the background with workers.
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It is backed by Redis and it is designed to have a low barrier to entry.
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It should be integrated in your web stack easily.</p>
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library for queueing jobs and processing them in the background with workers.
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It is backed by Redis and it is designed to have a low barrier to entry.
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It should be integrated in your web stack easily.</p>
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</li>
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<li>
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<p><a href="https://github.com/dcramer/taskmaster">Taskmaster</a> is a lightweight simple
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distributed queue for handling large volumes of one-off tasks. </p>
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</li>
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</ul>
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<h2>Task queue resources</h2>
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<ul>
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<li>
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<p><a href="http://justcramer.com/2012/05/04/distributing-work-without-celery/">Distributing work without Celery</a>
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provides a scenario in which Celery and RabbitMQ are not the right tool
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for scheduling asynchronous jobs.</p>
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</li>
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<li>
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<p><a href="http://queues.io/">Queues.io</a> is a collection of task queue systems with
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short summaries for each one. The task queues are not all compatible with
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Python but ones that work with it are tagged with the "Python" keyword.</p>
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</li>
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<li>
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<p><a href="http://www.slideshare.net/bryanhelmig/task-queues-comorichweb-12962619">Why Task Queues</a>
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is a presentation for what task queues are and why they are needed. </p>
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</li>
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<li>
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<p><a href="https://www.digitalocean.com/community/articles/how-to-use-celery-with-rabbitmq-to-queue-tasks-on-an-ubuntu-vps">How to use Celery with RabbitMQ</a>
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is a detail walkthrough for using these tools on an Ubuntu VPS.</p>
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</li>
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<li>
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<p>Heroku has a clear walkthrough for using
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<a href="https://devcenter.heroku.com/articles/python-rq">RQ for background tasks</a>.</p>
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</li>
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</ul>
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<br/>
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Next read the
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<a href="/api-integration.html">api integration</a> section.

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