# -*- coding: utf-8 -*- # Copyright (C) 2014 Yahoo! Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from taskflow.engines.action_engine import analyzer as ca from taskflow.engines.action_engine import executor as ex from taskflow.engines.action_engine import retry_action as ra from taskflow.engines.action_engine import runner as ru from taskflow.engines.action_engine import task_action as ta from taskflow import exceptions as excp from taskflow import retry as retry_atom from taskflow import states as st from taskflow import task as task_atom from taskflow.utils import misc class Runtime(object): """A aggregate of runtime objects, properties, ... used during execution. This object contains various utility methods and properties that represent the collection of runtime components and functionality needed for an action engine to run to completion. """ def __init__(self, compilation, storage, task_notifier, task_executor): self._task_notifier = task_notifier self._task_executor = task_executor self._storage = storage self._compilation = compilation @property def compilation(self): return self._compilation @property def storage(self): return self._storage @misc.cachedproperty def analyzer(self): return ca.Analyzer(self._compilation, self._storage) @misc.cachedproperty def runner(self): return ru.Runner(self, self._task_executor) @misc.cachedproperty def completer(self): return Completer(self) @misc.cachedproperty def scheduler(self): return Scheduler(self) @misc.cachedproperty def retry_action(self): return ra.RetryAction(self.storage, self._task_notifier) @misc.cachedproperty def task_action(self): return ta.TaskAction(self.storage, self._task_executor, self._task_notifier) def reset_nodes(self, nodes, state=st.PENDING, intention=st.EXECUTE): for node in nodes: if state: if isinstance(node, task_atom.BaseTask): self.task_action.change_state(node, state, progress=0.0) elif isinstance(node, retry_atom.Retry): self.retry_action.change_state(node, state) else: raise TypeError("Unknown how to reset node %s, %s" % (node, type(node))) if intention: self.storage.set_atom_intention(node.name, intention) def reset_all(self, state=st.PENDING, intention=st.EXECUTE): self.reset_nodes(self.analyzer.iterate_all_nodes(), state=state, intention=intention) def reset_subgraph(self, node, state=st.PENDING, intention=st.EXECUTE): self.reset_nodes(self.analyzer.iterate_subgraph(node), state=state, intention=intention) # Various helper methods used by completer and scheduler. def _retry_subflow(retry, runtime): runtime.storage.set_atom_intention(retry.name, st.EXECUTE) runtime.reset_subgraph(retry) class Completer(object): """Completes atoms using actions to complete them.""" def __init__(self, runtime): self._analyzer = runtime.analyzer self._retry_action = runtime.retry_action self._runtime = runtime self._storage = runtime.storage self._task_action = runtime.task_action def _complete_task(self, task, event, result): """Completes the given task, processes task failure.""" if event == ex.EXECUTED: self._task_action.complete_execution(task, result) else: self._task_action.complete_reversion(task, result) def resume(self): """Resumes nodes in the contained graph. This is done to allow any previously completed or failed nodes to be analyzed, there results processed and any potential nodes affected to be adjusted as needed. This should return a set of nodes which should be the initial set of nodes that were previously not finished (due to a RUNNING or REVERTING attempt not previously finishing). """ for node in self._analyzer.iterate_all_nodes(): if self._analyzer.get_state(node) == st.FAILURE: self._process_atom_failure(node, self._storage.get(node.name)) for retry in self._analyzer.iterate_retries(st.RETRYING): _retry_subflow(retry, self._runtime) unfinished_nodes = set() for node in self._analyzer.iterate_all_nodes(): if self._analyzer.get_state(node) in (st.RUNNING, st.REVERTING): unfinished_nodes.add(node) return unfinished_nodes def complete(self, node, event, result): """Performs post-execution completion of a node. Returns whether the result should be saved into an accumulator of failures or whether this should not be done. """ if isinstance(node, task_atom.BaseTask): self._complete_task(node, event, result) if isinstance(result, misc.Failure): if event == ex.EXECUTED: self._process_atom_failure(node, result) else: return True return False def _process_atom_failure(self, atom, failure): """Processes atom failure & applies resolution strategies. On atom failure this will find the atoms associated retry controller and ask that controller for the strategy to perform to resolve that failure. After getting a resolution strategy decision this method will then adjust the needed other atoms intentions, and states, ... so that the failure can be worked around. """ retry = self._analyzer.find_atom_retry(atom) if retry: # Ask retry controller what to do in case of failure action = self._retry_action.on_failure(retry, atom, failure) if action == retry_atom.RETRY: # Prepare subflow for revert self._storage.set_atom_intention(retry.name, st.RETRY) self._runtime.reset_subgraph(retry, state=None, intention=st.REVERT) elif action == retry_atom.REVERT: # Ask parent checkpoint self._process_atom_failure(retry, failure) elif action == retry_atom.REVERT_ALL: # Prepare all flow for revert self._revert_all() else: # Prepare all flow for revert self._revert_all() def _revert_all(self): """Attempts to set all nodes to the REVERT intention.""" self._runtime.reset_nodes(self._analyzer.iterate_all_nodes(), state=None, intention=st.REVERT) class Scheduler(object): """Schedules atoms using actions to schedule.""" def __init__(self, runtime): self._analyzer = runtime.analyzer self._retry_action = runtime.retry_action self._runtime = runtime self._storage = runtime.storage self._task_action = runtime.task_action def _schedule_node(self, node): """Schedule a single node for execution.""" # TODO(harlowja): we need to rework this so that we aren't doing type # checking here, type checking usually means something isn't done right # and usually will limit extensibility in the future. if isinstance(node, task_atom.BaseTask): return self._schedule_task(node) elif isinstance(node, retry_atom.Retry): return self._schedule_retry(node) else: raise TypeError("Unknown how to schedule node %s, %s" % (node, type(node))) def _schedule_retry(self, retry): """Schedules the given retry atom for *future* completion. Depending on the atoms stored intention this may schedule the retry atom for reversion or execution. """ intention = self._storage.get_atom_intention(retry.name) if intention == st.EXECUTE: return self._retry_action.execute(retry) elif intention == st.REVERT: return self._retry_action.revert(retry) elif intention == st.RETRY: self._retry_action.change_state(retry, st.RETRYING) _retry_subflow(retry, self._runtime) return self._retry_action.execute(retry) else: raise excp.ExecutionFailure("Unknown how to schedule retry with" " intention: %s" % intention) def _schedule_task(self, task): """Schedules the given task atom for *future* completion. Depending on the atoms stored intention this may schedule the task atom for reversion or execution. """ intention = self._storage.get_atom_intention(task.name) if intention == st.EXECUTE: return self._task_action.schedule_execution(task) elif intention == st.REVERT: return self._task_action.schedule_reversion(task) else: raise excp.ExecutionFailure("Unknown how to schedule task with" " intention: %s" % intention) def schedule(self, nodes): """Schedules the provided nodes for *future* completion. This method should schedule a future for each node provided and return a set of those futures to be waited on (or used for other similar purposes). It should also return any failure objects that represented scheduling failures that may have occurred during this scheduling process. """ futures = set() for node in nodes: try: futures.add(self._schedule_node(node)) except Exception: # Immediately stop scheduling future work so that we can # exit execution early (rather than later) if a single task # fails to schedule correctly. return (futures, [misc.Failure()]) return (futures, [])