1ed0f22fd3
Instead of using strings it is better if we can use constants (that may be the same/adjusted strings) and use those instead in the state machine used in the runner. The names are adjusted (and the state graph diagram and docstring) to reflect names that fit better with there intended meaning and usage. Change-Id: Iaf229d6e37730545ba9f2708d118697cb7145992
252 lines
9.8 KiB
Python
252 lines
9.8 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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from taskflow 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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# Event name constants the state machine uses.
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_SCHEDULE = 'schedule_next'
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_WAIT = 'wait_finished'
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_ANALYZE = 'examine_finished'
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_FINISH = 'completed'
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_FAILED = 'failed'
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_SUSPENDED = 'suspended'
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_SUCCESS = 'success'
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_REVERTED = 'reverted'
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_START = 'start'
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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 | completed | GAME_OVER | |
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ANALYZING | schedule_next | SCHEDULING | |
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ANALYZING | wait_finished | WAITING | |
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FAILURE[$] | | | |
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GAME_OVER | failed | FAILURE | |
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GAME_OVER | reverted | REVERTED | |
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GAME_OVER | success | SUCCESS | |
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GAME_OVER | suspended | SUSPENDED | |
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RESUMING | schedule_next | SCHEDULING | |
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REVERTED[$] | | | |
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SCHEDULING | wait_finished | WAITING | |
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SUCCESS[$] | | | |
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SUSPENDED[$] | | | |
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UNDEFINED[^] | start | RESUMING | |
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WAITING | examine_finished | 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 _FINISH
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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, _FINISH)
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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, _FINISH, 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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