401 lines
13 KiB
Python
401 lines
13 KiB
Python
# vim: tabstop=4 shiftwidth=4 softtabstop=4
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import eventlet
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import functools
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import itertools
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import sys
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import types
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from time import time as wallclock
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from heat.openstack.common import excutils
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from heat.openstack.common import log as logging
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logger = logging.getLogger(__name__)
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def task_description(task):
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"""
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Return a human-readable string description of a task suitable for logging
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the status of the task.
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"""
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if isinstance(task, types.MethodType):
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name = getattr(task, '__name__')
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obj = getattr(task, '__self__')
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if name is not None and obj is not None:
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return '%s from %s' % (name, obj)
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return repr(task)
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class Timeout(BaseException):
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"""
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Timeout exception, raised within a task when it has exceeded its allotted
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(wallclock) running time.
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This allows the task to perform any necessary cleanup, as well as use a
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different exception to notify the controlling task if appropriate. If the
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task supresses the exception altogether, it will be cancelled but the
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controlling task will not be notified of the timeout.
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"""
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def __init__(self, task_runner, timeout):
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"""
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Initialise with the TaskRunner and a timeout period in seconds.
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"""
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message = _('%s Timed out') % task_runner
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super(Timeout, self).__init__(message)
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# Note that we don't attempt to handle leap seconds or large clock
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# jumps here. The latter are assumed to be rare and the former
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# negligible in the context of the timeout. Time zone adjustments,
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# Daylight Savings and the like *are* handled. PEP 418 adds a proper
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# monotonic clock, but only in Python 3.3.
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self._endtime = wallclock() + timeout
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def expired(self):
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return wallclock() > self._endtime
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class TaskRunner(object):
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"""
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Wrapper for a resumable task (co-routine).
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"""
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def __init__(self, task, *args, **kwargs):
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"""
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Initialise with a task function, and arguments to be passed to it when
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it is started.
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The task function may be a co-routine that yields control flow between
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steps.
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"""
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assert callable(task), "Task is not callable"
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self._task = task
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self._args = args
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self._kwargs = kwargs
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self._runner = None
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self._done = False
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self._timeout = None
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self.name = task_description(task)
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def __str__(self):
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"""Return a human-readable string representation of the task."""
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return 'Task %s' % self.name
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def _sleep(self, wait_time):
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"""Sleep for the specified number of seconds."""
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if wait_time is not None:
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logger.debug('%s sleeping' % str(self))
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eventlet.sleep(wait_time)
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def __call__(self, wait_time=1, timeout=None):
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"""
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Start and run the task to completion.
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The task will sleep for `wait_time` seconds between steps. To avoid
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sleeping, pass `None` for `wait_time`.
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"""
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self.start(timeout=timeout)
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self.run_to_completion(wait_time=wait_time)
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def start(self, timeout=None):
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"""
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Initialise the task and run its first step.
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If a timeout is specified, any attempt to step the task after that
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number of seconds has elapsed will result in a Timeout being
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raised inside the task.
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"""
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assert self._runner is None, "Task already started"
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logger.debug('%s starting' % str(self))
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if timeout is not None:
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self._timeout = Timeout(self, timeout)
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result = self._task(*self._args, **self._kwargs)
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if isinstance(result, types.GeneratorType):
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self._runner = result
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self.step()
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else:
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self._runner = False
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self._done = True
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logger.debug('%s done (not resumable)' % str(self))
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def step(self):
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"""
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Run another step of the task, and return True if the task is complete;
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False otherwise.
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"""
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if not self.done():
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assert self._runner is not None, "Task not started"
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if self._timeout is not None and self._timeout.expired():
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logger.info('%s timed out' % str(self))
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try:
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self._runner.throw(self._timeout)
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except StopIteration:
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self._done = True
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else:
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# Clean up in case task swallows exception without exiting
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self.cancel()
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else:
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logger.debug('%s running' % str(self))
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try:
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next(self._runner)
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except StopIteration:
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self._done = True
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logger.debug('%s complete' % str(self))
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return self._done
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def run_to_completion(self, wait_time=1):
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"""
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Run the task to completion.
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The task will sleep for `wait_time` seconds between steps. To avoid
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sleeping, pass `None` for `wait_time`.
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"""
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while not self.step():
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self._sleep(wait_time)
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def cancel(self):
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"""Cancel the task if it is running."""
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if self.started() and not self.done():
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logger.debug('%s cancelled' % str(self))
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self._runner.close()
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self._done = True
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def started(self):
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"""Return True if the task has been started."""
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return self._runner is not None
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def done(self):
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"""Return True if the task is complete."""
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return self._done
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def __nonzero__(self):
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"""Return True if there are steps remaining."""
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return not self.done()
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def wrappertask(task):
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"""
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Decorator for a task that needs to drive a subtask.
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This is essentially a replacement for the Python 3-only "yield from"
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keyword (PEP 380), using the "yield" keyword that is supported in
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Python 2. For example:
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@wrappertask
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def parent_task(self):
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self.setup()
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yield self.child_task()
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self.cleanup()
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"""
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@functools.wraps(task)
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def wrapper(*args, **kwargs):
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parent = task(*args, **kwargs)
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for subtask in parent:
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try:
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if subtask is not None:
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for step in subtask:
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try:
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yield step
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except GeneratorExit as exit:
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subtask.close()
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raise exit
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except:
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try:
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subtask.throw(*sys.exc_info())
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except StopIteration:
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break
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else:
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yield
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except GeneratorExit as exit:
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parent.close()
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raise exit
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except:
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try:
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parent.throw(*sys.exc_info())
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except StopIteration:
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break
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return wrapper
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class DependencyTaskGroup(object):
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"""
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A task which manages a group of subtasks that have ordering dependencies.
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"""
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def __init__(self, dependencies, make_task=lambda o: o,
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reverse=False, name=None):
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"""
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Initialise with the task dependencies and (optionally) a function for
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creating a task from each dependency object.
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"""
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self._runners = dict((o, TaskRunner(make_task(o)))
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for o in dependencies)
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self._graph = dependencies.graph(reverse=reverse)
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if name is None:
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name = '(%s) %s' % (getattr(make_task, '__name__',
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task_description(make_task)),
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str(dependencies))
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self.name = name
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def __repr__(self):
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"""Return a string representation of the task."""
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return '%s(%s)' % (type(self).__name__, self.name)
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def __call__(self):
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"""Return a co-routine which runs the task group."""
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try:
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while any(self._runners.itervalues()):
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for k, r in self._ready():
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r.start()
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yield
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for k, r in self._running():
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if r.step():
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del self._graph[k]
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except:
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with excutils.save_and_reraise_exception():
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for r in self._runners.itervalues():
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r.cancel()
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def _ready(self):
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"""
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Iterate over all subtasks that are ready to start - i.e. all their
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dependencies have been satisfied but they have not yet been started.
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"""
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for k, n in self._graph.iteritems():
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if not n:
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runner = self._runners[k]
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if not runner.started():
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yield k, runner
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def _running(self):
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"""
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Iterate over all subtasks that are currently running - i.e. they have
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been started but have not yet completed.
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"""
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running = lambda (k, r): k in self._graph and r.started()
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return itertools.ifilter(running, self._runners.iteritems())
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class PollingTaskGroup(object):
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"""
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A task which manages a group of subtasks.
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When the task is started, all of its subtasks are also started. The task
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completes when all subtasks are complete.
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Once started, the subtasks are assumed to be only polling for completion
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of an asynchronous operation, so no attempt is made to give them equal
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scheduling slots.
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"""
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def __init__(self, tasks, name=None):
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"""Initialise with a list of tasks."""
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self._tasks = list(tasks)
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if name is None:
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name = ', '.join(task_description(t) for t in self._tasks)
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self.name = name
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@staticmethod
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def _args(arg_lists):
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"""Return a list containing the positional args for each subtask."""
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return zip(*arg_lists)
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@staticmethod
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def _kwargs(kwarg_lists):
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"""Return a list containing the keyword args for each subtask."""
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keygroups = (itertools.izip(itertools.repeat(name),
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arglist)
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for name, arglist in kwarg_lists.iteritems())
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return [dict(kwargs) for kwargs in itertools.izip(*keygroups)]
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@classmethod
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def from_task_with_args(cls, task, *arg_lists, **kwarg_lists):
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"""
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Return a new PollingTaskGroup where each subtask is identical except
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for the arguments passed to it.
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Each argument to use should be passed as a list (or iterable) of values
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such that one is passed in the corresponding position for each subtask.
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The number of subtasks spawned depends on the length of the argument
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lists. For example:
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PollingTaskGroup.from_task_with_args(my_task,
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[1, 2, 3],
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alpha=['a', 'b', 'c'])
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will start three TaskRunners that will run:
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my_task(1, alpha='a')
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my_task(2, alpha='b')
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my_task(3, alpha='c')
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respectively.
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If multiple arguments are supplied, each list should be of the same
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length. In the case of any discrepancy, the length of the shortest
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argument list will be used, and any extra arguments discarded.
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"""
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args_list = cls._args(arg_lists)
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kwargs_list = cls._kwargs(kwarg_lists)
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if kwarg_lists and not arg_lists:
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args_list = [[]] * len(kwargs_list)
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elif arg_lists and not kwarg_lists:
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kwargs_list = [{}] * len(args_list)
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task_args = itertools.izip(args_list, kwargs_list)
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tasks = (functools.partial(task, *a, **kwa) for a, kwa in task_args)
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return cls(tasks, name=task_description(task))
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def __repr__(self):
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"""Return a string representation of the task group."""
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return '%s(%s)' % (type(self).__name__, self.name)
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def __call__(self):
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"""Return a co-routine which runs the task group."""
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runners = [TaskRunner(t) for t in self._tasks]
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try:
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for r in runners:
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r.start()
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while runners:
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yield
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runners = list(itertools.dropwhile(lambda r: r.step(),
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runners))
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except:
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with excutils.save_and_reraise_exception():
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for r in runners:
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r.cancel()
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