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 API documentation.
task() that adds two numbers:
# demo.py from huey import SqliteHuey huey = SqliteHuey(filename='/tmp/demo.db') @huey.task() def add(a, b): return a + b
To test, run the consumer, specifying the import path to the
$ huey_consumer.py demo.huey
In a Python shell, we can call our
>>> from demo import add >>> r = add(1, 2) >>> r() 3
If you try to resolve the result (
r) before the task has been executed,
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. 3
What happens when we call a task function?
add()function is called, a message representing the call is placed in a queue.
The function returns immediately without actually running, and returns a
Resulthandle, which can be used to retrieve the result once the task has been executed by the consumer.
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.
We can use the
Resulthandle to read the return value from the result store.
For more information, see the
task() decorator documentation.
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. 7
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
>>> eta = datetime.datetime.now() + datetime.timedelta(seconds=10) >>> r = add.schedule((4, 5), eta=eta) >>> r(blocking=True) # Will block for ~10 seconds. 9
What happens when we schedule a task?
When we call
schedule(), a message is placed on the queue instructing the consumer to call the
add()function in 10 seconds.
The function returns immediately, and returns a
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.
The consumer adds the message to a schedule.
In ~10 seconds, the scheduler will pick-up the message and place it back into the queue for execution.
A worker will dequeue the message, execute the
add()function, and place the return value in the result store.
Resulthandle from step 2 will now be able to read the return value from the task.
For more details, see the
schedule() API documentation.
Huey provides crontab-like functionality that enables functions to be executed automatically on a given schedule.
In the following example, we will declare a
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') @huey.task() def add(a, b): return a + b @huey.periodic_task(crontab(minute='*/3')) 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
Result handle to access the
result of a given periodic task execution.
crontab() function accepts the following arguments:
day_of_week (0=Sunday, 6=Saturday)
*- 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- 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
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?
Message is placed on the queue and a
Resulthandle is returned to the caller.
Consumer picks up the message and attempts to run the task, but the call to
random.randint()happens to return
0, raising an
The consumer puts the error into the result store and the exception is logged. If the caller resolves the
TaskExceptionwill be raised which contains information about the exception that occurred in our task.
The consumer notices that the task can be retried 2 times, so it decrements the retry count and re-enqueues it for execution.
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”).
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
argument. The task will be retried up-to two times, with a delay of 10 seconds
@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
RetryTask exception. When this exception is used, the task will
be retried regardless of whether it was declared with
the task’s remaining retries (if they were declared) will not be affected by
For more information, see the following API documentation:
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
To see how this works, lets define a task that has a priority (
@huey.task(priority=10) def send_email(to, subj, body): return mailer.send(to, 'firstname.lastname@example.org', 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)
send_email('email@example.com', '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('firstname.lastname@example.org', '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(('email@example.com', 'subj', 'msg'), delay=60) # Override, specifying priority=50 for this task. send_email.schedule(('firstname.lastname@example.org', 'subj', 'msg'), delay=60, priority=50)
Lastly, we can specify priority on
@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.
# 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. res.revoke() # 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). res.restore()
# Prevent all instances of the add() task from running. add.revoke() # 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: add.restore() # 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:
Result.is_revoked()for checking the status of a task instance.
TaskWrapper.is_revoked()for checking the status of the task function itself.
Canceling from within a Task¶
Huey provides a special
CancelExecution exception which can be
raised, either within a
pre_execute() hook or within the body of
task()-decorated function, to cancel the execution of the
task. Additionally, when raised from within a task, the
exception can override the task’s default retry policy, by specifying either
@huey.task(retries=2) def load_data(): if something_temporary_is_wrong(): # Task will be retried, even if it has run out of retries or is a # task that does not specify any automatic retries. raise CancelExecution(retry=True) elif something_fatal_is_wrong(): # Task will NOT be retried, even if it has more than one retry # remaining. raise CancelExecution(retry=False) elif cancel_and_maybe_retry(): # Task will only be retried if it has one or more retries # remaining (this is the default). raise CancelExecution() ...
For more information, see:
Canceling or pausing periodic tasks¶
restore() methods support some additional options
which may be especially useful for
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 = datetime.datetime.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:
Huey tasks can be configured with an expiration time. Setting an expiration time on tasks will prevent them being run after the given time has elapsed. Expiration times may be useful if your queue is busy and there may be a significant lag between the time a task is enqueued and the time the consumer starts executing it.
Expiration times can be specified as:
datetime()instances, which are treated as absolute times.
int, which are relative to the time at which the task is enqueued.
A default expire time can be provided when declaring a task:
# Task must be executed by consumer within 60s of being enqueued. @huey.task(expires=60) def time_sensitive_task(...):
Expiration times can be specified per-invocation, as well:
# Task must be executed by consumer within 5 minutes of being enqueued. time_sensitive_task(report_file, expires=timedelta(seconds=300))
Expiration times can also be specified when scheduling tasks:
# Task scheduled to run in 1 hour, and once enqueued for execution, must be # run within 60 seconds. time_sensitive_task.schedule( args=(report_file,), delay=timedelta(seconds=3600), expires=timedelta(seconds=60)) # Example using absolute datetimes instead of relative deltas: one_hr = datetime.now() + timedelta(seconds=3600) time_sensitive_task.schedule( args=(report_file,), eta=one_hr, expires=one_hr + timedelta(seconds=60))
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:
@huey.task() def add(a, b): return a + b result = add(1, 2)
# 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))
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. print(result_group.get(blocking=True)) # [3, 6, 10, 15] # Alternatively, we could have iterated over the result group: for result in result_group: print(result.get(blocking=True)) # 3 # 6 # 10 # 15
When enqueueing a task pipeline, the return value will be a
ResultGroup, which encapsulates the
Result objects for
the individual tasks.
ResultGroup can be iterated over or you can
ResultGroup.get() method to get all the task return values as
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:
@huey.task() def fib(a, b=1): a, b = a + b, a return (a, b) # returns tuple, which is passed as *args pipe = (fib.s(1) .then(fib) .then(fib) .then(fib)) 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:
Task locking can be accomplished using the
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
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.periodic_task(crontab(minute='*/5')) @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. run_report() @huey.periodic_task(crontab(minute='0')) def backup(): # Generate backup of code do_code_backup() # 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'): do_db_backup()
Huey.lock_task() for API documentation.
Consumer sends signals as it processes tasks.
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.
@huey.signal() def print_signal_args(signal, task, exc=None): if signal == SIGNAL_ERROR: print('%s - %s - exception: %s' % (signal, task.id, exc)) else: print('%s - %s' % (signal, task.id))
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:
task. Additionally, our
handler accepts an optional third argument
exc which is only included with
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.
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 Changes in 2.0 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
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() 3 >>> add.revoke(revoke_once=True) # We can revoke tasks. >>> result = add(2, 3) >>> result() is None True >>> add(3, 4)() # No longer revoked, was restored automatically. 7
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. True
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
>>> 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.
Huey uses the standard library
logging module to log information about task
execution and consumer activity. Messages are logged to the
with consumer-specific messages being logged to
When the consumer is run, it binds a default
StreamHandler() to the huey
namespace so that all messages are logged to the console. The consumer logging
can be configured using the following consumer options:
-l FILE, --logfile=FILE- log to a file.
-v, --verbose- verbose logging (includes DEBUG level)
-q, --quiet- minimal logging
-S, --simple- simple logging format (“time message”)
If you would like to get email alerts when an error occurs, you can attach a
logging.handlers.SMTPHandler to the
huey namespace at level
from logging.handlers import SMTPHandler import logging mail_handler = SMTPHandler( mailhost=('smtp.gmail.com', 587), email@example.com', toaddrs=['firstname.lastname@example.org'], subject='Huey error log', credentials=('email@example.com', 'secret_password'), secure=()) mail_handler.setLevel(logging.ERROR) logging.getLogger('huey').addHandler(mail_handler)
Huey provides a number of different storage layers suitable to different types of workloads. Below I will try to sketch the differences, strengths, and weaknesses of each storage layer.
Huey’s capabilities are, to a large extent, informed by the functionality available in Redis. This is the most robust option available and can handle very busy workloads. Because Redis runs as a separate server process, it is even possible to run Huey consumers on multiple machines to facilitate “scale-out” operation.
Operations are guaranteed to be atomic, following the guarantees provided by Redis. The queue is stored in a Redis list, scheduled tasks use a sorted set, and the task result-store is kept in a hash.
Tasks that return a meaningful value must be sure that the caller “resolves” those return values at some point, to ensure that the result store does not become filled with unused data (to mitigate this, you can just modify your tasks to return
Noneif you never intend to use the result).
By default Huey performs a “blocking” pop on the queue, which reduces latency, although polling can be used instead by passing
Task priorities are not supported by
Redis storage layer that supports task priorities. In order to make this possible and efficient,
PriorityRedisHueystores the queue in a sorted set. Since sorted sets require the key to be unique, Huey will use the timestamp in microseconds to differentiate tasks enqueued with the same priority.
Redis storage layer that stores task results in top-level keys, in order to add an expiration time to them. Putting an expiration on task result keys can ensure that the result-store does not fill up with unresolved result values. The default expire time is 86400 seconds, although this can be controlled by setting the
expire_timeparameter during instantiation.
Sqlite works well for many workloads (see Appropriate uses for Sqlite), and Huey’s Sqlite storage layer works well regardless of the worker-type chosen. Sqlite locks the database during writes, ensuring only a single writer can write to the database at any given time. Writes generally happen very quickly, however, so in practice this is rarely an issue. Because the database is stored in a single file, taking backups is quite simple.
SqliteHueymay be a good choice for moderate workloads where the operational complexity of running a separate server process like Redis is undesirable.
Stores the queue, schedule and task results in files on the filesystem. This implementation is provided mostly for testing and development. An exclusive lock is used around all file-system operations, since multiple operations (list directory, read file, unlink file, e.g.) are typically required for each storage primitive (enqueue, dequeue, store result, etc).
In-memory implementation of the storage layer used for Immediate mode.
All storage methods are no-ops.
Tips and tricks¶
To call a task-decorated function in its original form, you can use
@huey.task() 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:
@huey.task() 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
>>> @huey.task() ... def add(a, b): ... return a + b ... >>> params = [(i, i ** 2) for i in range(10)] >>> result_group = add.map(params) >>> 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:
@huey.task() 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() else: # 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
Exponential Backoff Retries¶
Huey tasks support specifying a number of
retries and a
but does not support exponential backoff out-of-the-box. That’s not a problem,
as we can use a couple decorators to implement it ourselves quite easily:
import functools def exp_backoff_task(retries=10, retry_backoff=1.15): def deco(fn): @functools.wraps(fn) def inner(*args, **kwargs): # We will register this task with `context=True`, which causes # Huey to pass the task instance as a keyword argument to the # decorated task function. This enables us to modify its retry # delay, multiplying it by our backoff factor, in the event of # an exception. task = kwargs.pop('task') try: return fn(*args, **kwargs) except Exception as exc: task.retry_delay *= retry_backoff raise exc # Register our wrapped task (inner()), which handles delegating to # our function, and in the event of an unhandled exception, # increases the retry delay by the given factor. return huey.task(retries=retries, retry_delay=1, context=True)(inner) return deco
@exp_backoff_task(retries=5, retry_backoff=2) def test_backoff(message): print('test_backoff called:', message) raise ValueError('forcing retry')
If the consumer started executing our task at 12:00:00, then it would be retried at the following times:
12:00:00 (first call)
12:00:02 (retry 1)
12:00:06 (retry 2)
12:00:14 (retry 3)
12:00:30 (retry 4)
12:01:02 (retry 5)
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) @huey.task() 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(): dynamic_ptask(message) # 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.
Run Arbitrary Functions as Tasks¶
Instead of explicitly needing to declare all of your tasks up-front, you can write a special task that accepts a dotted-path to a callable and run anything inside of huey (provided it is available wherever the consumer is running):
from importlib import import_module @huey.task() def path_task(path, *args, **kwargs): path, name = path.rsplit('.', 1) # e.g. path.to.module.function mod = import_module(path) # Dynamically import the module. return getattr(mod, name)(*args, **kwargs) # Call the function. # Example usage might be: # foo.py def add_these(a, b): return a + b # Somewhere else, we can tell the consumer to use the "path_task" to import # the foo module and call "add_these(1, 2)", storing the result in the # result-store like any other task. path_task('foo.add_these', 1, 2)
That sums up the basic usage patterns of huey. Below are links for details on other aspects of the APIs:
Huey- responsible for coordinating executable tasks and queue backends
Huey.task()- decorator to indicate an executable task.
Result- handle for interacting with a task.
Huey.periodic_task()- decorator to indicate a task that executes at periodic intervals.
crontab()- define what intervals to execute a periodic command.
For information about managing shared resources like database connections, refer to the shared resources document.
Also check out the notes on running the consumer.