Source code for taskflow.retry

# -*- coding: utf-8 -*-

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#         http://www.apache.org/licenses/LICENSE-2.0
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import abc

import enum

from taskflow import atom
from taskflow import exceptions as exc
from taskflow.utils import misc


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