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The purpose of this document is to present Huey using simple examples that cover the most common usage of the library. Detailed documentation can be found in the :ref:`API documentation <api>`.

Example :py:meth:`~Huey.task` that adds two numbers:

from huey import SqliteHuey

huey = SqliteHuey(filename='/tmp/demo.db')

def add(a, b):
    return a + b

To test, run the consumer, specifying the import path to the huey object:

$ demo.huey

In a Python shell, we can call our add task:

>>> from demo import add
>>> r = add(1, 2)
>>> r()


If you try to resolve the result (r) before the task has been executed, then r() will return None. You can avoid this by instructing the result to block until the task has finished and a result is ready:

>>> r = add(1, 2)
>>> r(blocking=True, timeout=5)  # Wait up to 5 seconds for result.

What happens when we call a task function?

  1. When the add() function is called, a message representing the call is placed in a queue.
  2. The function returns immediately without actually running, and returns a :py:class:`Result` handle, which can be used to retrieve the result once the task has been executed by the consumer.
  3. The consumer process sees that a message has arrived, and a worker will call the add() function and place the return value into the result store.
  4. We can use the :py:class:`Result` handle to read the return value from the result store.

For more information, see the :py:meth:`~Huey.task` decorator documentation.

Scheduling tasks

Tasks can be scheduled to execute at a certain time, or after a delay.

In the following example, we will schedule a call to add() to run in 10 seconds, and then will block until the result becomes available:

>>> r = add.schedule((3, 4), delay=10)
>>> r(blocking=True)  # Will block for ~10 seconds before returning.

If we wished to schedule the task to run at a particular time, we can use the eta parameter instead. The following example will run after a 10 second delay:

>>> eta = + datetime.timedelta(seconds=10)
>>> r = add.schedule((4, 5), eta=eta)
>>> r(blocking=True)  # Will block for ~10 seconds.

What happens when we schedule a task?

  1. When we call :py:meth:`~TaskWrapper.schedule`, a message is placed on the queue instructing the consumer to call the add() function in 10 seconds.
  2. The function returns immediately, and returns a :py:class:`Result` handle.
  3. The consumer process sees that a message has arrived, and will notice that the message is not yet ready to be executed, but should be run in ~10s.
  4. The consumer adds the message to a schedule.
  5. In ~10 seconds, the scheduler will pick-up the message and place it back into the queue for execution.
  6. A worker will dequeue the message, execute the add() function, and place the return value in the result store.
  7. The :py:class:`Result` handle from step 2 will now be able to read the return value from the task.

For more details, see the :py:meth:`~TaskWrapper.schedule` API documentation.

Periodic tasks

Huey provides crontab-like functionality that enables functions to be executed automatically on a given schedule.

In the following example, we will declare a :py:meth:`~Huey.periodic_task` that executes every 3 minutes and prints a message on consumer process stdout:

from huey import SqliteHuey
from huey import crontab

huey = SqliteHuey(filename='/tmp/demo.db')

def add(a, b):
    return a + b

def every_three_minutes():
    print('This task runs every three minutes')

Once a minute, the scheduler will check to see if any of the periodic tasks should be called. If so, the task will be enqueued for execution.


Because periodic tasks are called independent of any user interaction, they do not accept any arguments.

Similarly, the return-value for periodic tasks is discarded, rather than being put into the result store. This is because there is not an obvious way for an application to obtain a :py:class:`Result` handle to access the result of a given periodic task execution.

The :py:func:`crontab` function accepts the following arguments:

  • minute
  • hour
  • day
  • month
  • day_of_week (0=Sunday, 6=Saturday)

Acceptable inputs:

  • * - always true, e.g. if hour='*', then the rule matches any hour.
  • */n - every n interval, e.g. minute='*/15' means every 15 minutes.
  • m-n - run every time m..n inclusive.
  • m,n - run on m and n.

Multiple rules can be expressed by separating the individual rules with a comma, for example:

# Runs every 10 minutes between 9a and 11a, and 4p-6p.
crontab(minute='*/10', hour='9-11,16-18')

For more information see the following API documentation:

Retrying tasks that fail

Sometimes we may have a task that we anticipate might fail from time to time, in which case we should retry it. Huey supports automatically retrying tasks a given number of times, optionally with a delay between attempts.

Here we'll declare a task that fails approximately half of the time. To configure this task to be automatically retried, use the retries parameter of the :py:meth:`~Huey.task` decorator:

import random

@huey.task(retries=2)  # Retry the task up to 2 times.
def flaky_task():
    if random.randint(0, 1) == 0:
        raise Exception('failing!')
    return 'OK'

What happens when we call this task?

  1. Message is placed on the queue and a :py:class:`Result` handle is returned to the caller.
  2. Consumer picks up the message and attempts to run the task, but the call to random.randint() happens to return 0, raising an Exception.
  3. The consumer puts the error into the result store and the exception is logged. If the caller resolves the :py:class:`Result` now, a :py:class:`TaskException` will be raised which contains information about the exception that occurred in our task.
  4. The consumer notices that the task can be retried 2 times, so it decrements the retry count and re-enqueues it for execution.
  5. The consumer picks up the message again and runs the task. This time, the task succeeds! The new return value is placed into the result store ("OK").
  6. We can reset our :py:class:`Result` handle by calling :py:meth:`~Result.reset` and then re-resolve it. The result handle will now give us the new value, "OK".

Should the task fail on the first invocation, it will be retried up-to two times. Note that it will be retried immediately after it returns.

To specify a delay between retry attempts, we can add a retry_delay argument. The task will be retried up-to two times, with a delay of 10 seconds between attempts:

@huey.task(retries=2, retry_delay=10)
def flaky_task():
    # ...


Retries and retry delay arguments can also be specified for periodic tasks.

It is also possible to explicitly retry a task from within the task, by raising a :py:class:`RetryTask` exception. When this exception is used, the task will be retried regardless of whether it was declared with retries. Similarly, the task's remaining retries (if they were declared) will not be affected by raising :py:class:`RetryTask`.

For more information, see the following API documentation:

Task priority


Priority support for Redis requires Redis 5.0 or newer. To use task priorities with Redis, use the :py:class:`PriorityRedisHuey` instead of :py:class:`RedisHuey`.

Task prioritization is fully supported by :py:class:`SqliteHuey` and the in-memory storage layer used when :ref:`immediate` is enabled.

Huey tasks can be given a priority, allowing you to ensure that your most important tasks do not get delayed when the workers are busy.

Priorities can be assigned to a task function, in which case all invocations of the task will default to the given priority. Additionally, individual task invocations can be assigned a priority on a one-off basis.


When no priority is given, the task will default to a priority of 0.

To see how this works, lets define a task that has a priority (10):

def send_email(to, subj, body):
    return mailer.send(to, '', subj, body)

When we invoke this task, it will be processed before any other pending tasks whose priority is less than 10. So we could imagine our queue looking something like this:

  • process_payment - priority = 50
  • check_spam - priority = 1
  • make_thumbnail - priority = 0 (default)

Invoke the send_email() task:

send_email('', 'Welcome', 'blah blah')

Now the queue of pending tasks would be:

  • process_payment - priority = 50
  • send_email - priority = 10
  • check_spam - priority = 1
  • make_thumbnail - priority = 0

We can override the default priority by passing priority= as a keyword argument to the task function:

send_email('', 'Important!', 'etc', priority=90)

Now the queue of pending tasks would be:

  • send_email (to boss) - priority = 90
  • process_payment - priority = 50
  • send_email - priority = 10
  • check_spam - priority = 1
  • make_thumbnail - priority = 0

Task priority only affects the ordering of tasks as they are pulled from the queue of pending tasks. If there are periods of time where your workers are not able to keep up with the influx of tasks, Huey's priority feature can ensure that your most important tasks do not get delayed.

Task-specific priority overrides can also be specified when scheduling a task to run in the future:

# Uses priority=10, since that was the default we used when
# declaring the send_email task:
send_email.schedule(('', 'subj', 'msg'), delay=60)

# Override, specifying priority=50 for this task.
send_email.schedule(('', 'subj', 'msg'), delay=60, priority=50)

Lastly, we can specify priority on :py:class:`~Huey.periodic_task`:

@huey.periodic_task(crontab(minute='0', hour='*/3'), priority=10)
def some_periodic_task():
    # ...

For more information:

Canceling or pausing tasks

Huey tasks can be cancelled dynamically at runtime. This applies to regular tasks, tasks scheduled to execute in the future, and periodic tasks.

Any task can be canceled ("revoked"), provided the task has not started executing yet. Similarly, a revoked task can be restored, provided it has not already been processed and discarded by the consumer.

Using the :py:meth:`Result.revoke` and :py:meth:`Result.restore` methods:

# Schedule a task to execute in 60 seconds.
res = add.schedule((1, 2), delay=60)

# Provided the 60s has not elapsed, the task can be canceled
# by calling the `revoke()` method on the result object.

# We can check to see if the task is revoked.
res.is_revoked()  # -> True

# Similarly, we can restore the task, provided the 60s has
# not elapsed (at which point it would have been read and
# discarded by the consumer).

To revoke all instances of a given task, use the :py:meth:`~TaskWrapper.revoke` and :py:meth:`~TaskWrapper.restore` methods on the task function itself:

# Prevent all instances of the add() task from running.

# We can check to see that all instances of the add() task
# are revoked:
add.is_revoked()  # -> True

# We can enqueue an instance of the add task, and then check
# to verify that it is revoked:
res = add(1, 2)
res.is_revoked()  # -> True

# To re-enable a task, we'll use the restore() method on
# the task function:

# Is the add() task enabled again?
add.is_revoked()  # -> False

Huey provides APIs to revoke / restore on both individual instances of a task, as well as all instances of the task. For more information, see the following API docs:

Canceling or pausing periodic tasks

The revoke() and restore() methods support some additional options which may be especially useful for :py:meth:`~Huey.periodic_task`.

The :py:meth:`~TaskWrapper.revoke` method accepts two optional parameters:

  • revoke_once - boolean flag, if set then only the next occurrence of the task will be revoked, after which it will be restored automatically.
  • revoke_until - datetime, which specifies the time at which the task should be automatically restored.

For example, suppose we have a task that sends email notifications, but our mail server goes down and won't be fixed for a while. We can revoke the task for a couple of hours, after which time it will start executing again:

@huey.periodic_task(crontab(minute='0', hour='*'))
def send_notification_emails():
    # ... code to send emails ...

Here is how we might revoke the task for the next 3 hours:

>>> now =
>>> eta = now + datetime.timedelta(hours=3)
>>> send_notification_emails.revoke(revoke_until=eta)

Alternatively, we could use revoke_once=True to just skip the next execution of the task:

>>> send_notification_emails.revoke(revoke_once=True)

At any time, the task can be restored using the usual :py:meth:`~TaskWrapper.restore` method, and it's status can be checked using the :py:meth:`~TaskWrapper.is_revoked` method.

Task pipelines

Huey supports pipelines (or chains) of one or more tasks that should be executed sequentially.

To get started, let's review the usual way we execute tasks:

def add(a, b):
    return a + b

result = add(1, 2)

An equivalent, but more verbose, way is to use the :py:meth:`~TaskWrapper.s` method to create a :py:class:`Task` instance and then enqueue it explicitly:

# Create a task representing the execution of add(1, 2).
task = add.s(1, 2)

# Enqueue the task instance, which returns a Result handle.
result = huey.enqueue(task)

So the following are equivalent:

result = add(1, 2)

# And:
result = huey.enqueue(add.s(1, 2))

The :py:meth:`TaskWrapper.s` method is used to create a :py:class:`Task` instance (which represents the execution of the given function). The Task is what gets serialized and sent to the consumer.

To create a pipeline, we will use the :py:meth:`TaskWrapper.s` method to create a :py:class:`Task` instance. We can then chain additional tasks using the :py:meth:`Task.then` method:

add_task = add.s(1, 2)  # Create Task to represent add(1, 2) invocation.

# Add additional tasks to pipeline by calling add_task.then().
pipeline = (add_task
            .then(add, 3)  # Call add() with previous result (1+2) and 3.
            .then(add, 4)  # Previous result ((1+2)+3) and 4.
            .then(add, 5)) # Etc.

# When a pipeline is enqueued, a ResultGroup is returned (which is
# comprised of individual Result instances).
result_group = huey.enqueue(pipeline)

# Print results of above pipeline.
# [3, 6, 10, 15]

# Alternatively, we could have iterated over the result group:
for result in result_group:
# 3
# 6
# 10
# 15

When enqueueing a task pipeline, the return value will be a :py:class:`ResultGroup`, which encapsulates the :py:class:`Result` objects for the individual tasks. :py:class:`ResultGroup` can be iterated over or you can use the :py:meth:`ResultGroup.get` method to get all the task return values as a list.

Note that the return value from the parent task is passed to the next task in the pipeline, and so on.

If the value returned by the parent function is a tuple, then the tuple will be used to extend the *args for the next task. Likewise, if the parent function returns a dict, then the dict will be used to update the **kwargs for the next task.

Example of chaining fibonacci calculations:

def fib(a, b=1):
    a, b = a + b, a
    return (a, b)  # returns tuple, which is passed as *args

pipe = (fib.s(1)
results = huey.enqueue(pipe)

print(results(True))  # Resolve results, blocking until all are finished.
# [(2, 1), (3, 2), (5, 3), (8, 5)]

For more information, see the following API docs:

Locking tasks

Task locking can be accomplished using the :py:meth:`Huey.lock_task` method, which can be used as a context-manager or decorator.

This lock prevents multiple invocations of a task from running concurrently.

If a second invocation occurs and the lock cannot be acquired, then a special :py:class:`TaskLockedException` is raised and the task will not be executed. If the task is configured to be retried, then it will be retried normally.


@huey.lock_task('reports-lock')  # Goes *after* the task decorator.
def generate_report():
    # If a report takes longer than 5 minutes to generate, we do
    # not want to kick off another until the previous invocation
    # has finished.

def backup():
    # Generate backup of code

    # Generate database backup. Since this may take longer than an
    # hour, we want to ensure that it is not run concurrently.
    with huey.lock_task('db-backup'):

See :py:meth:`Huey.lock_task` for API documentation.


The :py:class:`Consumer` sends :ref:`signals <signals>` as it processes tasks. The :py:meth:`Huey.signal` method can be used to attach a callback to one or more signals, which will be invoked synchronously by the consumer when the signal is sent.

For a simple example, we can add a signal handler that simply prints the signal name and the ID of the related task.

def print_signal_args(signal, task, exc=None):
    if signal == SIGNAL_ERROR:
        print('%s - %s - exception: %s' % (signal,, exc))
        print('%s - %s' % (signal,

The :py:meth:`~Huey.signal` method is used to decorate the signal-handling function. It accepts an optional list of signals. If none are provided, as in our example, then the handler will be called for any signal.

The callback function (print_signal_args) accepts two required arguments, which are present on every signal: signal and task. Additionally, our handler accepts an optional third argument exc which is only included with SIGNAL_ERROR. SIGNAL_ERROR is only sent when a task raises an uncaught exception during execution.


Signal handlers are executed synchronously by the consumer, so it is typically a bad idea to introduce any slow operations into a signal handler.

For a complete list of Huey's signals and their meaning, see the :ref:`signals` document, and the :py:meth:`Huey.signal` API documentation.

Immediate mode


Immediate mode replaces the always eager mode available prior to the release of Huey 2. It offers many improvements over always eager mode, which are described in the :ref:`changes` document.

Huey can be run in a special mode called immediate mode, which is very useful during testing and development. In immediate mode, Huey will execute task functions immediately rather than enqueueing them, while still preserving the APIs and behaviors one would expect when running a dedicated consumer process.

Immediate mode can be enabled in two ways:

huey = RedisHuey('my-app', immediate=True)

# Or at any time, via the "immediate" attribute:
huey = RedisHuey('my-app')
huey.immediate = True

To disable immediate mode:

huey.immediate = False

By default, enabling immediate mode will switch your Huey instance to using in-memory storage. This is to prevent accidentally reading or writing to live storage while doing development or testing. If you prefer to use immediate mode with live storage, you can specify immediate_use_memory=False when creating your :py:class:`Huey` instance:

huey = RedisHuey('my-app', immediate_use_memory=False)

You can try out immediate mode quite easily in the Python shell. In the following example, everything happens within the interpreter -- no separate consumer process is needed. In fact, because immediate mode switches to an in-memory storage when enabled, we don't even have to be running a Redis server:

>>> from huey import RedisHuey
>>> huey = RedisHuey()
>>> huey.immediate = True

>>> @huey.task()
... def add(a, b):
...     return a + b

>>> result = add(1, 2)
>>> result()

>>> add.revoke(revoke_once=True)  # We can revoke tasks.
>>> result = add(2, 3)
>>> result() is None

>>> add(3, 4)()  # No longer revoked, was restored automatically.

What happens if we try to schedule a task for execution in the future, while using immediate mode?

>>> result = add.schedule((4, 5), delay=60)
>>> result() is None  # No result.

As you can see, the task was not executed. So what happened to it? The answer is that the task was added to the in-memory storage layer's schedule. We can check this by calling :py:meth:`Huey.scheduled`:

>>> huey.scheduled()
[__main__.add: 8873...bcbd @2019-03-27 02:50:06]

Since immediate mode is fully synchronous, there is not a separate thread monitoring the schedule. The schedule can still be read or written to, but scheduled tasks will not automatically be executed.

Tips and tricks

To call a task-decorated function in its original form, you can use :py:meth:`~TaskWrapper.call_local`:

def add(a, b):
    return a + b

# Call the add() function in "un-decorated" form, skipping all
# the huey stuff:
add.call_local(3, 4)  # Returns 7.

It's also worth mentioning that python decorators are just syntactical sugar for wrapping a function with another function. Thus, the following two examples are equivalent:

def add(a, b):
    return a + b

# Equivalent to:
def _add(a, b):
    return a + b

add = huey.task()(_add)

Task functions can be applied multiple times to a list (or iterable) of parameters using the :py:meth:`` method:

>>> @huey.task()
... def add(a, b):
...     return a + b

>>> params = [(i, i ** 2) for i in range(10)]
>>> result_group =
>>> result_group.get(blocking=True)
[0, 2, 6, 12, 20, 30, 42, 56, 72, 90]

The Huey result-store can be used directly if you need a convenient way to cache arbitrary key/value data:

def calculate_something():
    # By default, the result store treats get() like a pop(), so in
    # order to preserve the data so it can be read again, we specify
    # the second argument, peek=True.
    prev_results = huey.get('calculate-something.result', peek=True)
    if prev_results is None:
        # No previous results found, start from the beginning.
        data = start_from_beginning()
        # Only calculate what has changed since last time.
        data = just_what_changed(prev_results)

    # We can store the updated data back in the result store.
    huey.put('calculate-something.result', data)
    return data

See :py:meth:`Huey.get` and :py:meth:`Huey.put` for additional details.

Dynamic periodic tasks

To create periodic tasks dynamically we need to register them so that they are added to the in-memory schedule managed by the consumer's scheduler thread. Since this registry is in-memory, any dynamically defined tasks must be registered within the process that will ultimately schedule them: the consumer.


The following example will not work with the process worker-type option, since there is currently no way to interact with the scheduler process. When threads or greenlets are used, the worker threads share the same in-memory schedule as the scheduler thread, allowing modification to take place.


def dynamic_ptask(message):
    print('dynamically-created periodic task: "%s"' % message)

def schedule_message(message, cron_minutes, cron_hours='*'):
    # Create a new function that represents the application
    # of the "dynamic_ptask" with the provided message.
    def wrapper():

    # The schedule that was specified for this task.
    schedule = crontab(cron_minutes, cron_hours)

    # Need to provide a unique name for the task. There are any number of
    # ways you can do this -- based on the arguments, etc. -- but for our
    # example we'll just use the time at which it was declared.
    task_name = 'dynamic_ptask_%s' % int(time.time())

    huey.periodic_task(schedule, name=task_name)(wrapper)

Assuming the consumer is running, we can now set up as many instances as we like of the "dynamic ptask" function:

>>> from demo import schedule_message
>>> schedule_message('I run every 5 minutes', '*/5')
<Result: task ...>
>>> schedule_message('I run between 0-15 and 30-45', '0-15,30-45')
<Result: task ...>

When the consumer executes the "schedule_message" tasks, our new periodic task will be registered and added to the schedule.

Reading more

That sums up the basic usage patterns of huey. Below are links for details on other aspects of the APIs:

Also check out the :ref:`notes on running the consumer <consuming-tasks>`.

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