Use Redis as a priority-enabled and time-based task queue.
This package intends to offer a priority-based remote task queue solution using Redis as the transport and persistence layer, and JSON for a common interchange format.
Semantically, this module implements a 0/1 queue with optional retries. That is, it attempts to execute every task once. If the task raises an exception, it will not automatically retry, but you can manually retry the task and specify the maximum attempts. See the Retries section below.
Full documentation is available: https://pythonhosted.org/rpqueue/
In order to execute tasks, you must ensure that rpqueue knows about your tasks that can be executed, you must configure rpqueue to connect to your Redis server, then you must start the task execution daemon:
from mytasks import usertasks1, usertasks2, ... import rpqueue rpqueue.set_redis_connection_settings(host, port, db) rpqueue.execute_tasks()
Alternatively, rpqueue offers a command-line interface to do the same, though you must provide the name of a module or package that imports all modules or packages that define tasks that you want to run. For example:
# tasks.py from tasks import accounting, cleanup, ... # any other imports or configuration necessary, put them here # run from the command-line python -m rpqueue.run --module=tasks --host=... --port=... --db=...
Say that you have a module usertasks1 with a task to be executed called echo_to_stdout. Your module may look like the following:
from rpqueue import task @task def echo_to_stdout(message): print message
To call the above task, you would use:
echo_to_stdout.execute(...) echo_to_stdout.execute(..., delay=delay_in_seconds)
You can also schedule a task to be repeatedly executed with the periodic_task decorator:
@periodic_task(25, queue="low") def function1(): # Will be executed every 25 seconds from within the 'low' queue. pass
Tasks may be provided an optional attempts argument, which specifies the total number of times the task will try to be executed before failing. By default, all tasks have attempts set at 1, unless otherwise specified:
@task(attempts=3) def fail_until_zero(value, **kwargs): try: if value != 0: value -= 1 raise Exception except: fail_until_zero.retry(value, **kwargs) else: print "succeeded"
If passed the value 3, “succeeded” will never be printed. Why? The first try has value=3, attempts=3, and fails. The second pass has value=2, attempts=2, and fails. The third pass has value=1, attempts=1, fails, and the retry returns without retrying. The attempts value is the total number of attempts, including the first, and all retries.
Waiting for task execution
As of version .19, RPQueue offers the ability to wait on a task until it begins execution:
@task def my_task(args): # do something executing_task = my_task.execute() if executing_task.wait(5): # task is either being executed, or it is done else: # task has not started execution yet
With the ability to wait for a task to complete, you can have the ability to add deadlines by inserting a call to executing_task.cancel() in the else block above.
Automatically storing results of tasks
As of version .19, RPQueue offers the ability to store the result returned by a task as it completes:
@task(save_results=30) def task_with_results(): return 5 etask = task_with_results.execute() if etask.wait(5): print etask.result # should print 5
The save_results argument can be passed to tasks, periodic tasks, and even cron tasks (described below). The value passed will be how long the result is stored in Redis, in seconds. All results must be json-encodable.
Support for cron_tasks using a crontab-like syntax requires the Python crontab module: http://pypi.python.org/pypi/crontab/ , allowing for:
@cron_task('0 5 tue * *') def function2(): # Will be executed every Tuesday at 5AM. pass