.. image:: https://media.charlesleifer.com/blog/photos/huey3-logo.png
*a lightweight alternative*.
huey is:
* a task queue
* written in python
* clean and simple API
huey has:
* support for redis (or valkey/redict), postgres, sqlite, file-system, or in-memory storage
* zero dependencies (``redis-py`` required to use redis-like brokers,
``psycopg`` for postgres).
* `example code `_.
* `django `_ integration (native or via django.tasks)
with admin integration for visibility and management.
* `documentation `_.
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, rate-limits and timeouts
* task pipelines and chains
* groups (fan-out), chords (map / reduce)
.. image:: http://i.imgur.com/2EpRs.jpg
At a glance
-----------
.. code-block:: python
from huey import RedisHuey, crontab
# Or PostgresHuey, SqliteHuey, FileHuey, etc...
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
>>> 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 `_
or take a look at the `example code `_.
Running the consumer
^^^^^^^^^^^^^^^^^^^^
Run the consumer with four worker processes:
.. code-block:: console
$ huey_consumer my_app.huey -k process -w 4
To run the consumer with a single worker thread (default):
.. code-block:: console
$ huey_consumer 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 my_app.huey -k greenlet -w 32
Storage
-------
Huey's design and feature-set were informed by the capabilities of the
`Redis `_ 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, Postgres, Sqlite, File-system, and
in-memory storage.
Frameworks
----------
Huey provides Django integration either natively or via ``django.tasks``. Huey
also provides an optional admin integration for Django:
.. image:: https://huey.readthedocs.io/en/latest/_images/django-admin.png
Huey also provides `flask-peewee `_ admin
integration based on the same underlying stat-tracking system:
.. image:: https://huey.readthedocs.io/en/latest/_images/flask-admin-panel.png
Other frameworks can use the `stats `_
extension to collect and display this information.
Documentation
----------------
`See Huey documentation `_.
Project page
---------------
`See source code and issue tracker on Github `_.
Huey is named in honor of my cat:
.. image:: http://m.charlesleifer.com/t/800x-/blog/photos/p1473037658.76.jpg?key=mD9_qMaKBAuGPi95KzXYqg