.. image:: http://media.charlesleifer.com/blog/photos/huey2-logo.png
*a lightweight alternative*.
huey is:
* a task queue (**2019-04-01**: `version 2.0 released <https://huey.readthedocs.io/en/latest/changes.html>`_)
* written in python (2.7+, 3.4+)
* clean and simple API
* redis, sqlite, file-system, or in-memory storage
* `example code <https://github.com/coleifer/huey/tree/master/examples/>`_.
* `read the documentation <https://huey.readthedocs.io/>`_.
huey supports:
* multi-process, multi-thread or greenlet task execution models
* schedule tasks to execute at a given time, or after a given delay
* schedule recurring tasks, like a crontab
* automatically retry tasks that fail
* task prioritization
* task result storage
* task expiration
* task locking
* task pipelines and chains
.. image:: http://i.imgur.com/2EpRs.jpg
.. image:: https://api.travis-ci.org/coleifer/huey.svg?branch=master
At a glance
-----------
.. code-block:: python
from huey import RedisHuey, crontab
huey = RedisHuey('my-app', host='redis.myapp.com')
@huey.task()
def add_numbers(a, b):
return a + b
@huey.task(retries=2, retry_delay=60)
def flaky_task(url):
# This task might fail, in which case it will be retried up to 2 times
# with a delay of 60s between retries.
return this_might_fail(url)
@huey.periodic_task(crontab(minute='0', hour='3'))
def nightly_backup():
sync_all_data()
Calling a ``task``-decorated function will enqueue the function call for
execution by the consumer. A special result handle is returned immediately,
which can be used to fetch the result once the task is finished:
.. code-block:: pycon
>>> from demo import add_numbers
>>> res = add_numbers(1, 2)
>>> res
<Result: task 6b6f36fc-da0d-4069-b46c-c0d4ccff1df6>
>>> res()
3
Tasks can be scheduled to run in the future:
.. code-block:: pycon
>>> res = add_numbers.schedule((2, 3), delay=10) # Will be run in ~10s.
>>> res(blocking=True) # Will block until task finishes, in ~10s.
5
For much more, check out the `guide <https://huey.readthedocs.io/en/latest/guide.html>`_
or take a look at the `example code <https://github.com/coleifer/huey/tree/master/examples/>`_.
Running the consumer
^^^^^^^^^^^^^^^^^^^^
Run the consumer with four worker processes:
.. code-block:: console
$ huey_consumer.py my_app.huey -k process -w 4
To run the consumer with a single worker thread (default):
.. code-block:: console
$ huey_consumer.py my_app.huey
If your work-loads are mostly IO-bound, you can run the consumer with threads
or greenlets instead. Because greenlets are so lightweight, you can run quite a
few of them efficiently:
.. code-block:: console
$ huey_consumer.py my_app.huey -k greenlet -w 32
Storage
-------
Huey's design and feature-set were informed by the capabilities of the
`Redis <https://redis.io>`_ database. Redis is a fantastic fit for a
lightweight task queueing library like Huey: it's self-contained, versatile,
and can be a multi-purpose solution for other web-application tasks like
caching, event publishing, analytics, rate-limiting, and more.
Although Huey was designed with Redis in mind, the storage system implements a
simple API and many other tools could be used instead of Redis if that's your
preference.
Huey comes with builtin support for Redis, Sqlite and in-memory storage.
Documentation
----------------
`See Huey documentation <https://huey.readthedocs.io/>`_.
Project page
---------------
`See source code and issue tracker on Github <https://github.com/coleifer/huey/>`_.
Huey is named in honor of my cat:
.. image:: http://m.charlesleifer.com/t/800x-/blog/photos/p1473037658.76.jpg?key=mD9_qMaKBAuGPi95KzXYqg
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