bb8ea5686f
To allow other components to more easily import these modules without causing circular imports split these two classes into there own modules so that they can be referenced as/where needed in the future. Change-Id: I9cf1fd1dd133be58ef521a8911d1740f1daa9950
101 lines
4.1 KiB
Python
101 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 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._runtime = runtime
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self._analyzer = runtime.analyzer
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self._retry_action = runtime.retry_action
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self._runtime = runtime
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self._storage = runtime.storage
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self._task_action = runtime.task_action
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def _schedule_node(self, node):
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"""Schedule a single node for execution."""
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# TODO(harlowja): we need to rework this so that we aren't doing type
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# checking here, type checking usually means something isn't done right
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# and usually will limit extensibility in the future.
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if isinstance(node, task_atom.BaseTask):
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return self._schedule_task(node)
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elif isinstance(node, retry_atom.Retry):
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return self._schedule_retry(node)
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else:
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raise TypeError("Unknown how to schedule atom '%s' (%s)"
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% (node, type(node)))
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def _schedule_retry(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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def _schedule_task(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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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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