5f0b514a14
It is not needed to return the atom that was executed from the futures result() method, since we can just as easily set an attribute on the future and reference it from there when using it later. This is also required for a process based executor since it is not typically possible to send back a raw task object (and is not desireable to require this); even if it was possible the task would be pickled and unpickled multiple times so when this happens it can not be guaranteed to even be the same object (in fact it is not). Part of blueprint process-executor Change-Id: I4a05ea5dcdef97218312e3a88ed4a1dfdf1b1edf
242 lines
9.4 KiB
Python
242 lines
9.4 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2012 Yahoo! Inc. All Rights Reserved.
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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 logging
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from taskflow import states as st
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from taskflow.types import failure
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from taskflow.types import fsm
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# Waiting state timeout (in seconds).
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_WAITING_TIMEOUT = 60
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# Meta states the state machine uses.
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_UNDEFINED = 'UNDEFINED'
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_GAME_OVER = 'GAME_OVER'
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_META_STATES = (_GAME_OVER, _UNDEFINED)
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LOG = logging.getLogger(__name__)
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class _MachineMemory(object):
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"""State machine memory."""
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def __init__(self):
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self.next_nodes = set()
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self.not_done = set()
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self.failures = []
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self.done = set()
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class _MachineBuilder(object):
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"""State machine *builder* that the runner uses.
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NOTE(harlowja): the machine states that this build will for are::
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+--------------+-----------+------------+----------+---------+
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Start | Event | End | On Enter | On Exit
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+--------------+-----------+------------+----------+---------+
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ANALYZING | finished | GAME_OVER | |
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ANALYZING | schedule | SCHEDULING | |
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ANALYZING | wait | WAITING | |
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FAILURE[$] | | | |
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GAME_OVER | failed | FAILURE | |
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GAME_OVER | reverted | REVERTED | |
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GAME_OVER | success | SUCCESS | |
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GAME_OVER | suspended | SUSPENDED | |
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RESUMING | schedule | SCHEDULING | |
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REVERTED[$] | | | |
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SCHEDULING | wait | WAITING | |
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SUCCESS[$] | | | |
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SUSPENDED[$] | | | |
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UNDEFINED[^] | start | RESUMING | |
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WAITING | analyze | ANALYZING | |
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+--------------+-----------+------------+----------+---------+
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Between any of these yielded states (minus ``GAME_OVER`` and ``UNDEFINED``)
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if the engine has been suspended or the engine has failed (due to a
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non-resolveable task failure or scheduling failure) the machine will stop
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executing new tasks (currently running tasks will be allowed to complete)
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and this machines run loop will be broken.
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"""
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def __init__(self, runtime, waiter):
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self._analyzer = runtime.analyzer
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self._completer = runtime.completer
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self._scheduler = runtime.scheduler
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self._storage = runtime.storage
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self._waiter = waiter
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def runnable(self):
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return self._storage.get_flow_state() == st.RUNNING
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def build(self, timeout=None):
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memory = _MachineMemory()
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if timeout is None:
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timeout = _WAITING_TIMEOUT
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def resume(old_state, new_state, event):
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memory.next_nodes.update(self._completer.resume())
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memory.next_nodes.update(self._analyzer.get_next_nodes())
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return 'schedule'
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def game_over(old_state, new_state, event):
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if memory.failures:
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return 'failed'
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if self._analyzer.get_next_nodes():
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return 'suspended'
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elif self._analyzer.is_success():
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return 'success'
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else:
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return 'reverted'
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def schedule(old_state, new_state, event):
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if self.runnable() and memory.next_nodes:
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not_done, failures = self._scheduler.schedule(
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memory.next_nodes)
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if not_done:
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memory.not_done.update(not_done)
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if failures:
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memory.failures.extend(failures)
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memory.next_nodes.clear()
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return 'wait'
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def wait(old_state, new_state, event):
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# TODO(harlowja): maybe we should start doing 'yield from' this
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# call sometime in the future, or equivalent that will work in
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# py2 and py3.
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if memory.not_done:
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done, not_done = self._waiter.wait_for_any(memory.not_done,
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timeout)
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memory.done.update(done)
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memory.not_done = not_done
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return 'analyze'
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def analyze(old_state, new_state, event):
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next_nodes = set()
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while memory.done:
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fut = memory.done.pop()
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node = fut.atom
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try:
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event, result = fut.result()
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retain = self._completer.complete(node, event, result)
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if retain and isinstance(result, failure.Failure):
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memory.failures.append(result)
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except Exception:
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memory.failures.append(failure.Failure())
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else:
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try:
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more_nodes = self._analyzer.get_next_nodes(node)
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except Exception:
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memory.failures.append(failure.Failure())
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else:
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next_nodes.update(more_nodes)
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if self.runnable() and next_nodes and not memory.failures:
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memory.next_nodes.update(next_nodes)
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return 'schedule'
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elif memory.not_done:
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return 'wait'
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else:
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return 'finished'
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def on_exit(old_state, event):
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LOG.debug("Exiting old state '%s' in response to event '%s'",
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old_state, event)
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def on_enter(new_state, event):
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LOG.debug("Entering new state '%s' in response to event '%s'",
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new_state, event)
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# NOTE(harlowja): when ran in debugging mode it is quite useful
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# to track the various state transitions as they happen...
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watchers = {}
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if LOG.isEnabledFor(logging.DEBUG):
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watchers['on_exit'] = on_exit
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watchers['on_enter'] = on_enter
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m = fsm.FSM(_UNDEFINED)
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m.add_state(_GAME_OVER, **watchers)
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m.add_state(_UNDEFINED, **watchers)
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m.add_state(st.ANALYZING, **watchers)
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m.add_state(st.RESUMING, **watchers)
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m.add_state(st.REVERTED, terminal=True, **watchers)
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m.add_state(st.SCHEDULING, **watchers)
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m.add_state(st.SUCCESS, terminal=True, **watchers)
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m.add_state(st.SUSPENDED, terminal=True, **watchers)
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m.add_state(st.WAITING, **watchers)
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m.add_state(st.FAILURE, terminal=True, **watchers)
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m.add_transition(_GAME_OVER, st.REVERTED, 'reverted')
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m.add_transition(_GAME_OVER, st.SUCCESS, 'success')
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m.add_transition(_GAME_OVER, st.SUSPENDED, 'suspended')
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m.add_transition(_GAME_OVER, st.FAILURE, 'failed')
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m.add_transition(_UNDEFINED, st.RESUMING, 'start')
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m.add_transition(st.ANALYZING, _GAME_OVER, 'finished')
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m.add_transition(st.ANALYZING, st.SCHEDULING, 'schedule')
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m.add_transition(st.ANALYZING, st.WAITING, 'wait')
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m.add_transition(st.RESUMING, st.SCHEDULING, 'schedule')
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m.add_transition(st.SCHEDULING, st.WAITING, 'wait')
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m.add_transition(st.WAITING, st.ANALYZING, 'analyze')
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m.add_reaction(_GAME_OVER, 'finished', game_over)
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m.add_reaction(st.ANALYZING, 'analyze', analyze)
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m.add_reaction(st.RESUMING, 'start', resume)
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m.add_reaction(st.SCHEDULING, 'schedule', schedule)
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m.add_reaction(st.WAITING, 'wait', wait)
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m.freeze()
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return (m, memory)
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class Runner(object):
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"""Runner that iterates while executing nodes using the given runtime.
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This runner acts as the action engine run loop/state-machine, it resumes
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the workflow, schedules all task it can for execution using the runtimes
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scheduler and analyzer components, and than waits on returned futures and
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then activates the runtimes completion component to finish up those tasks
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and so on...
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NOTE(harlowja): If the runtimes scheduler component is able to schedule
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tasks in parallel, this enables parallel running and/or reversion.
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"""
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# Informational states this action yields while running, not useful to
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# have the engine record but useful to provide to end-users when doing
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# execution iterations.
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ignorable_states = (st.SCHEDULING, st.WAITING, st.RESUMING, st.ANALYZING)
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def __init__(self, runtime, waiter):
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self._builder = _MachineBuilder(runtime, waiter)
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@property
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def builder(self):
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return self._builder
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def runnable(self):
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return self._builder.runnable()
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def run_iter(self, timeout=None):
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"""Runs the nodes using a built state machine."""
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machine, memory = self.builder.build(timeout=timeout)
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for (_prior_state, new_state) in machine.run_iter('start'):
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# NOTE(harlowja): skip over meta-states.
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if new_state not in _META_STATES:
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if new_state == st.FAILURE:
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yield (new_state, memory.failures)
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else:
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yield (new_state, [])
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