To help you get started, we've collected the most common ways that huey is being used within popular public projects.

coleifer / huey / examples / django_ex / one_queue / test_app / tasks.py View on Github

@db_task()
def foo(number):
    print('foo(%s)' % number)

View more ways to use huey

a lightweight alternative.

huey is:

• a task queue
• written in python
• clean and simple API
• redis, sqlite, file-system, or in-memory storage
• example code.
• read the 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
• task pipelines and chains

At a glance

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:

>>> from demo import add_numbers
>>> res = add_numbers(1, 2)
>>> res


>>> res()
3

Tasks can be scheduled to run in the future:

>>> 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:

$ huey_consumer.py my_app.huey -k process -w 4

To run the consumer with a single worker thread (default):

$ 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:

$ 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 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.

Project page

See source code and issue tracker on Github.

Huey is named in honor of my cat: