Iterable

If you have an iterable object with arguments for a function, you can use async_iter() to async them with a single command:

# Async Iterable example
from django_q.tasks import async_iter, result

# set up a list of arguments for math.floor
iter = [i for i in range(100)]

# async_task iter them
id=async_iter('math.floor',iter)

# wait for the collated result for 1 second
result_list = result(id, wait=1000)

This will individually queue 100 tasks to the worker cluster, which will save their results in the cache backend for speed. Once all the 100 results are in the cache, they are collated into a list and saved as a single result in the database. The cache results are then cleared.

You can also use an Iter instance which can sometimes be more convenient:

from django_q.tasks import Iter

i = Iter('math.copysign')

# add some arguments
i.append(1, -1)
i.append(2, -1)
i.append(3, -1)

# run it
i.run()

# get the results
print(i.result())

[-1.0, -2.0, -3.0]

Reference

async_iter(func, args_iter, **kwargs)

Runs iterable arguments against the cache backend and returns a single collated result. Accepts the same options as async_task() except hook. See also the Iter class.

Parameters:
  • func (object) – The task function to execute
  • args – An iterable containing arguments for the task function
  • kwargs (dict) – Keyword arguments for the task function. Ignores hook.
Returns:

The uuid of the task
Return type:

str
class Iter(func=None, args=None, kwargs=None, cached=Conf.CACHED, sync=Conf.SYNC, broker=None)

An async task with iterable arguments. Serves as a convenient wrapper for async_iter() You can pass the iterable arguments at construction or you can append individual argument tuples.

param func:the function to execute
param args:an iterable of arguments.
param kwargs:the keyword arguments
param bool cached:
 run this against the cache backend
param bool sync:
 execute this inline instead of asynchronous
param broker:optional broker instance
append(*args)

Append arguments to the iter set. Returns the current set count.

param args:the arguments for a single execution
return:the current set count
rtype:int
run()

Start queueing the tasks to the worker cluster.

return:the task result id
result(wait=0)

return the full list of results.

param int wait:how many milliseconds to wait for a result
return:an unsorted list of results
fetch(wait=0)

get the task result objects.

param int wait:how many milliseconds to wait for a result
return:an unsorted list of task objects
length()

get the length of the arguments list

return int:length of the argument list