huey

.. 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), sqlite, file-system, or in-memory storage
• example code <https://github.com/coleifer/huey/tree/master/examples/>_.
• django <https://huey.readthedocs.io/en/latest/django.html>_ integration (native or via django.tasks)
• 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, 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

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