cd922d4e46
This makes it possible to REVERT a subflow and have it also revert the parent flow if the parent flow doesn't have its own retry strategy. We will probably want to make this new behavior the default or only behavior in a future release. Change-Id: Iea5ac366380ba7396a87d0185703549fb0c2f825
385 lines
14 KiB
Python
385 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2013 Rackspace Hosting Inc. All Rights Reserved.
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# Copyright (C) 2013 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 abc
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import enum
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import six
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from taskflow import atom
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from taskflow import exceptions as exc
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from taskflow.utils import misc
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@enum.unique
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class Decision(misc.StrEnum):
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"""Decision results/strategy enumeration."""
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REVERT = "REVERT"
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"""Reverts only the surrounding/associated subflow.
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This strategy first consults the parent atom before reverting the
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associated subflow to determine if the parent retry object provides a
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different reconciliation strategy. This allows for safe nesting of
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flows with different retry strategies.
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If the parent flow has no retry strategy, the default behavior is
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to just revert the atoms in the associated subflow. This is
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generally not the desired behavior, but is left as the default in
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order to keep backwards-compatibility. The ``defer_reverts``
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engine option will let you change this behavior. If that is set
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to True, a REVERT will always defer to the parent, meaning that
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if the parent has no retry strategy, it will be reverted as well.
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"""
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REVERT_ALL = "REVERT_ALL"
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"""Reverts the entire flow, regardless of parent strategy.
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This strategy will revert every atom that has executed thus
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far, regardless of whether the parent flow has a separate
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retry strategy associated with it.
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"""
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#: Retries the surrounding/associated subflow again.
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RETRY = "RETRY"
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# Retain these aliases for a number of releases...
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REVERT = Decision.REVERT
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REVERT_ALL = Decision.REVERT_ALL
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RETRY = Decision.RETRY
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# Constants passed into revert/execute kwargs.
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#
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# Contains information about the past decisions and outcomes that have
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# occurred (if available).
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EXECUTE_REVERT_HISTORY = 'history'
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#
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# The cause of the flow failure/s
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REVERT_FLOW_FAILURES = 'flow_failures'
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class History(object):
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"""Helper that simplifies interactions with retry historical contents."""
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def __init__(self, contents, failure=None):
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self._contents = contents
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self._failure = failure
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@property
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def failure(self):
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"""Returns the retries own failure or none if not existent."""
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return self._failure
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def outcomes_iter(self, index=None):
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"""Iterates over the contained failure outcomes.
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If the index is not provided, then all outcomes are iterated over.
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NOTE(harlowja): if the retry itself failed, this will **not** include
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those types of failures. Use the :py:attr:`.failure` attribute to
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access that instead (if it exists, aka, non-none).
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"""
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if index is None:
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contents = self._contents
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else:
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contents = [
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self._contents[index],
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]
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for (provided, outcomes) in contents:
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for (owner, outcome) in six.iteritems(outcomes):
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yield (owner, outcome)
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def __len__(self):
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return len(self._contents)
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def provided_iter(self):
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"""Iterates over all the values the retry has attempted (in order)."""
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for (provided, outcomes) in self._contents:
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yield provided
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def __getitem__(self, index):
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return self._contents[index]
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def caused_by(self, exception_cls, index=None, include_retry=False):
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"""Checks if the exception class provided caused the failures.
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If the index is not provided, then all outcomes are iterated over.
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NOTE(harlowja): only if ``include_retry`` is provided as true (defaults
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to false) will the potential retries own failure be
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checked against as well.
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"""
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for (name, failure) in self.outcomes_iter(index=index):
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if failure.check(exception_cls):
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return True
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if include_retry and self._failure is not None:
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if self._failure.check(exception_cls):
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return True
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return False
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def __iter__(self):
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"""Iterates over the raw contents of this history object."""
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return iter(self._contents)
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@six.add_metaclass(abc.ABCMeta)
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class Retry(atom.Atom):
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"""A class that can decide how to resolve execution failures.
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This abstract base class is used to inherit from and provide different
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strategies that will be activated upon execution failures. Since a retry
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object is an atom it may also provide :meth:`~taskflow.retry.Retry.execute`
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and :meth:`~taskflow.retry.Retry.revert` methods to alter the inputs of
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connected atoms (depending on the desired strategy to be used this can be
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quite useful).
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NOTE(harlowja): the :meth:`~taskflow.retry.Retry.execute` and
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:meth:`~taskflow.retry.Retry.revert` and
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:meth:`~taskflow.retry.Retry.on_failure` will automatically be given
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a ``history`` parameter, which contains information about the past
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decisions and outcomes that have occurred (if available).
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"""
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default_provides = None
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def __init__(self, name=None, provides=None, requires=None,
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auto_extract=True, rebind=None):
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if provides is None:
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provides = self.default_provides
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super(Retry, self).__init__(name, provides)
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self._build_arg_mapping(self.execute, requires, rebind, auto_extract,
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ignore_list=[EXECUTE_REVERT_HISTORY])
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@property
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def name(self):
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return self._name
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@name.setter
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def name(self, name):
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self._name = name
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@abc.abstractmethod
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def execute(self, history, *args, **kwargs):
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"""Executes the given retry.
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This execution activates a given retry which will typically produce
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data required to start or restart a connected component using
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previously provided values and a ``history`` of prior failures from
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previous runs. The historical data can be analyzed to alter the
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resolution strategy that this retry controller will use.
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For example, a retry can provide the same values multiple times (after
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each run), the latest value or some other variation. Old values will be
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saved to the history of the retry atom automatically, that is a list of
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tuples (result, failures) are persisted where failures is a dictionary
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of failures indexed by task names and the result is the execution
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result returned by this retry during that failure resolution
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attempt.
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:param args: positional arguments that retry requires to execute.
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:param kwargs: any keyword arguments that retry requires to execute.
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"""
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def revert(self, history, *args, **kwargs):
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"""Reverts this retry.
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On revert call all results that had been provided by previous tries
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and all errors caused during reversion are provided. This method
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will be called *only* if a subflow must be reverted without the
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retry (that is to say that the controller has ran out of resolution
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options and has either given up resolution or has failed to handle
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a execution failure).
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:param args: positional arguments that the retry required to execute.
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:param kwargs: any keyword arguments that the retry required to
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execute.
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"""
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@abc.abstractmethod
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def on_failure(self, history, *args, **kwargs):
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"""Makes a decision about the future.
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This method will typically use information about prior failures (if
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this historical failure information is not available or was not
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persisted the provided history will be empty).
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Returns a retry constant (one of):
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* ``RETRY``: when the controlling flow must be reverted and restarted
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again (for example with new parameters).
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* ``REVERT``: when this controlling flow must be completely reverted
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and the parent flow (if any) should make a decision about further
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flow execution.
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* ``REVERT_ALL``: when this controlling flow and the parent
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flow (if any) must be reverted and marked as a ``FAILURE``.
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"""
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class AlwaysRevert(Retry):
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"""Retry that always reverts subflow."""
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def on_failure(self, *args, **kwargs):
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return REVERT
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def execute(self, *args, **kwargs):
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pass
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class AlwaysRevertAll(Retry):
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"""Retry that always reverts a whole flow."""
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def on_failure(self, **kwargs):
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return REVERT_ALL
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def execute(self, **kwargs):
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pass
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class Times(Retry):
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"""Retries subflow given number of times. Returns attempt number.
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:param attempts: number of attempts to retry the associated subflow
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before giving up
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:type attempts: int
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:param revert_all: when provided this will cause the full flow to revert
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when the number of attempts that have been tried
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has been reached (when false, it will only locally
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revert the associated subflow)
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:type revert_all: bool
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Further arguments are interpreted as defined in the
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:py:class:`~taskflow.atom.Atom` constructor.
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"""
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def __init__(self, attempts=1, name=None, provides=None, requires=None,
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auto_extract=True, rebind=None, revert_all=False):
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super(Times, self).__init__(name, provides, requires,
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auto_extract, rebind)
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self._attempts = attempts
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if revert_all:
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self._revert_action = REVERT_ALL
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else:
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self._revert_action = REVERT
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def on_failure(self, history, *args, **kwargs):
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if len(history) < self._attempts:
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return RETRY
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return self._revert_action
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def execute(self, history, *args, **kwargs):
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return len(history) + 1
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class ForEachBase(Retry):
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"""Base class for retries that iterate over a given collection."""
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def __init__(self, name=None, provides=None, requires=None,
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auto_extract=True, rebind=None, revert_all=False):
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super(ForEachBase, self).__init__(name, provides, requires,
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auto_extract, rebind)
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if revert_all:
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self._revert_action = REVERT_ALL
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else:
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self._revert_action = REVERT
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def _get_next_value(self, values, history):
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# Fetches the next resolution result to try, removes overlapping
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# entries with what has already been tried and then returns the first
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# resolution strategy remaining.
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remaining = misc.sequence_minus(values, history.provided_iter())
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if not remaining:
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raise exc.NotFound("No elements left in collection of iterable "
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"retry controller %s" % self.name)
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return remaining[0]
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def _on_failure(self, values, history):
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try:
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self._get_next_value(values, history)
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except exc.NotFound:
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return self._revert_action
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else:
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return RETRY
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class ForEach(ForEachBase):
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"""Applies a statically provided collection of strategies.
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Accepts a collection of decision strategies on construction and returns the
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next element of the collection on each try.
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:param values: values collection to iterate over and provide to
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atoms other in the flow as a result of this functions
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:py:meth:`~taskflow.atom.Atom.execute` method, which
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other dependent atoms can consume (for example, to alter
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their own behavior)
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:type values: list
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:param revert_all: when provided this will cause the full flow to revert
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when the number of attempts that have been tried
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has been reached (when false, it will only locally
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revert the associated subflow)
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:type revert_all: bool
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Further arguments are interpreted as defined in the
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:py:class:`~taskflow.atom.Atom` constructor.
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"""
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def __init__(self, values, name=None, provides=None, requires=None,
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auto_extract=True, rebind=None, revert_all=False):
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super(ForEach, self).__init__(name, provides, requires,
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auto_extract, rebind, revert_all)
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self._values = values
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def on_failure(self, history, *args, **kwargs):
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return self._on_failure(self._values, history)
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def execute(self, history, *args, **kwargs):
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# NOTE(harlowja): This allows any connected components to know the
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# current resolution strategy being attempted.
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return self._get_next_value(self._values, history)
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class ParameterizedForEach(ForEachBase):
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"""Applies a dynamically provided collection of strategies.
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Accepts a collection of decision strategies from a predecessor (or from
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storage) as a parameter and returns the next element of that collection on
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each try.
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:param revert_all: when provided this will cause the full flow to revert
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when the number of attempts that have been tried
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has been reached (when false, it will only locally
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revert the associated subflow)
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:type revert_all: bool
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Further arguments are interpreted as defined in the
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:py:class:`~taskflow.atom.Atom` constructor.
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"""
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def __init__(self, name=None, provides=None, requires=None,
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auto_extract=True, rebind=None, revert_all=False):
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super(ParameterizedForEach, self).__init__(name, provides, requires,
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auto_extract, rebind,
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revert_all)
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def on_failure(self, values, history, *args, **kwargs):
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return self._on_failure(values, history)
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def execute(self, values, history, *args, **kwargs):
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return self._get_next_value(values, history)
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