Run your task asynchronously and control/communicate with them.
Run your task asynchronously in the background and control/communicate with them.
pip install task.py
Basic use case of task.py is to run some unit of work asynchronously (in a separate thread or process) while being able to control and communicate with that unit of work. Example:
Lets define a simple_task that:
- Expects some arguments
- Does some work (sleep) until signaled to stop
- return “done.” as final result
` >>> def simple_task(t): # will receive a Task instance as first argument ... assert t.args == 'hello' ... assert t.kwargs['world'] == 'world' ... while not t.stopped(): ... time.sleep(0.1) ... return 'done.' `
Next, create a Task instance with simple_task as unit to work. Additionally specify arguments expected by our sleep_task:
` >>> t1 = Task(simple_task, 'hello', world='world') >>> >>> # start our task asynchronously >>> t1.start() >>> >>> # after 1 second signal our task to stop >>> time.sleep(1) >>> t1.stop() >>> >>> t1.done True >>> >>> t1.result 'done.' >>> >>> t1.exception == None True `
Lets create a new Task instance, this time skipping the expected arguments:
` >>> t2 = Task(simple_task) >>> >>> # start our task asynchronously >>> t2.start() >>> >>> t2.done True >>> >>> # No result found >>> t2.result == None True >>> >>> # our task raised an exception >>> t2.exception IndexError('tuple index out of range',) `
Communicating with Task
Task provide a communication queue over which external threads can send data to the executing unit of work. Example:
Lets define a sleep_task which will receive amount of time to sleep over the communication queue:
` >>> def sleep_task(t): ... secs = t.recv() # recv API, default blocks until we have some data ... time.sleep(int(secs)) ... >>> t3 = Task(sleep_task) >>> t3.start() >>> >>> # our task is waiting >>> t3.done False >>> >>> # send 1 sec sleep time >>> t3.send(1) >>> >>> # alright, done >>> t3.done True `
Task and Celery
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