import logging
logger = logging.getLogger(__name__)
[docs]class SchedulerPlugin(object):
""" Interface to extend the Scheduler
The scheduler operates by triggering and responding to events like
``task_finished``, ``update_graph``, ``task_erred``, etc..
A plugin enables custom code to run at each of those same events. The
scheduler will run the analogous methods on this class when each event is
triggered. This runs user code within the scheduler thread that can
perform arbitrary operations in synchrony with the scheduler itself.
Plugins are often used for diagnostics and measurement, but have full
access to the scheduler and could in principle affect core scheduling.
To implement a plugin implement some of the methods of this class and add
the plugin to the scheduler with ``Scheduler.add_plugin(myplugin)``.
Examples
--------
>>> class Counter(SchedulerPlugin):
... def __init__(self):
... self.counter = 0
...
... def transition(self, key, start, finish, *args, **kwargs):
... if start == 'processing' and finish == 'memory':
... self.counter += 1
...
... def restart(self, scheduler):
... self.counter = 0
>>> c = Counter()
>>> scheduler.add_plugin(c) # doctest: +SKIP
"""
[docs] def update_graph(self, scheduler, dsk=None, keys=None, restrictions=None, **kwargs):
""" Run when a new graph / tasks enter the scheduler """
[docs] def restart(self, scheduler, **kwargs):
""" Run when the scheduler restarts itself """
[docs] def transition(self, key, start, finish, *args, **kwargs):
""" Run whenever a task changes state
Parameters
----------
key: string
start: string
Start state of the transition.
One of released, waiting, processing, memory, error.
finish: string
Final state of the transition.
*args, **kwargs: More options passed when transitioning
This may include worker ID, compute time, etc.
"""
[docs] def add_worker(self, scheduler=None, worker=None, **kwargs):
""" Run when a new worker enters the cluster """
[docs] def remove_worker(self, scheduler=None, worker=None, **kwargs):
""" Run when a worker leaves the cluster"""