A multithreaded task scheduler for task queues and recurrent tasks
taskutils is a implements a multithreaded scheduler in Python. It provide methods to enqueue tasks and set simple policies, for example:
It can used to power more complex programs that need concurrency, for example a crawler.
Here is an example of usage:
### Install it
pip install taskutils
### Import the relevant objects
from taskutils import TaskHandler, Task, Recurrent
### Create the main task handler object
task_handler = TaskHandler(max_num_threads=3, sleep_seconds=2)
This will create 3 threads that wait for tasks to be queued every 2 seconds.
### Queue some simple tasks
def foo(k): print ‘hello world from task %s’ % k
- for k in range(10):
- task = Task(foo, kwargs=dict(k=k), priority=k, repeats_on_failure=3) task_handler.enqueue_task(task)
This will enqueue 10 tasks (with arbitrary parameter k=0…9) which will be executed by the task handler threads when free. If a task fails it will be retried 3 times before being discarded. Notice that a task contain a function (in this case foo) and its arguments.
### Queue a recurrent task
task = Task(foo, kwargs=dict(k=10), repeats_on_failure=0) task_handler.recurrent_tasks[‘abc’] = Recurrent(task, interval=3, repeats=5)
Recurrent tasks are wrapped into the Recurrent() object and must be named (for example ‘abc’). Recurrent tasks are named because they are not placed in the normal queue but are executed always with max priority when their time comes. In the above example the ‘abc’ task is executed 5 times every 3 seconds. A recurrent task can be removed via
### Other goodies
from taskutils import LockWrapper with_a_lock = LockWrapper()
It defines a decorator that makes sure all code called with the decorator is always serialized, even if called in different Tasks:
@with_a_lock def f(): print ‘a’
@with_a_lock def g(): print ‘b’
This makes sure that the excution of functions f and g is never concurrent.
Created by Massimo Di Pierro (http://experts4solutions.com) @2016 BSDv3 License