1e8fabd0cb
Instead of having a larger scheduler class that contains logic for both the retry routine and the task routine split this into two classes and have the scheduler class use those sub-schedulers for internal scheduling. Change-Id: I6309a5fd172d5b20a01a2ba8b3e4cf8512d085fb
115 lines
4.1 KiB
Python
115 lines
4.1 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2014 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 exceptions as excp
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from taskflow import retry as retry_atom
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from taskflow import states as st
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from taskflow import task as task_atom
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from taskflow.types import failure
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class _RetryScheduler(object):
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def __init__(self, runtime):
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self._runtime = runtime
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self._retry_action = runtime.retry_action
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self._storage = runtime.storage
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@staticmethod
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def handles(atom):
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return isinstance(atom, retry_atom.Retry)
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def schedule(self, retry):
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"""Schedules the given retry atom for *future* completion.
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Depending on the atoms stored intention this may schedule the retry
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atom for reversion or execution.
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"""
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intention = self._storage.get_atom_intention(retry.name)
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if intention == st.EXECUTE:
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return self._retry_action.execute(retry)
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elif intention == st.REVERT:
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return self._retry_action.revert(retry)
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elif intention == st.RETRY:
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self._retry_action.change_state(retry, st.RETRYING)
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self._runtime.retry_subflow(retry)
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return self._retry_action.execute(retry)
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else:
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raise excp.ExecutionFailure("Unknown how to schedule retry with"
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" intention: %s" % intention)
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class _TaskScheduler(object):
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def __init__(self, runtime):
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self._storage = runtime.storage
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self._task_action = runtime.task_action
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@staticmethod
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def handles(atom):
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return isinstance(atom, task_atom.BaseTask)
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def schedule(self, task):
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"""Schedules the given task atom for *future* completion.
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Depending on the atoms stored intention this may schedule the task
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atom for reversion or execution.
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"""
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intention = self._storage.get_atom_intention(task.name)
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if intention == st.EXECUTE:
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return self._task_action.schedule_execution(task)
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elif intention == st.REVERT:
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return self._task_action.schedule_reversion(task)
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else:
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raise excp.ExecutionFailure("Unknown how to schedule task with"
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" intention: %s" % intention)
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class Scheduler(object):
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"""Schedules atoms using actions to schedule."""
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def __init__(self, runtime):
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self._schedulers = [
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_RetryScheduler(runtime),
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_TaskScheduler(runtime),
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]
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def _schedule_node(self, node):
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"""Schedule a single node for execution."""
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for sched in self._schedulers:
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if sched.handles(node):
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return sched.schedule(node)
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else:
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raise TypeError("Unknown how to schedule '%s'" % node)
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def schedule(self, nodes):
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"""Schedules the provided nodes for *future* completion.
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This method should schedule a future for each node provided and return
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a set of those futures to be waited on (or used for other similar
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purposes). It should also return any failure objects that represented
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scheduling failures that may have occurred during this scheduling
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process.
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"""
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futures = set()
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for node in nodes:
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try:
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futures.add(self._schedule_node(node))
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except Exception:
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# Immediately stop scheduling future work so that we can
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# exit execution early (rather than later) if a single task
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# fails to schedule correctly.
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return (futures, [failure.Failure()])
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return (futures, [])
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