Files
deb-python-taskflow/taskflow/engines/action_engine/graph_analyzer.py
Joshua Harlow 58a5a0932d Persistence cleanup part one
- Convert the various functions that take a task detail into
  ones that take atom details (since this is now the generic
  type they should take).
- Don't expose the detail type strings as part of the atom
  detail api, leave those as private hidden strings and provide
  conversion functions from string<->class instead.
- Have the logbook objects contain the following new methods
  to reduce the dependence on persistence_utils to do the same.
  - to_dict() which converts the current object into a dict
  - from_dict() which converts the provided dict into a object
  - merge() which merges a incoming objects data with the current
    objects
- Have the persistence backends + storage + action engine use these
  new methods instead of there current usage.
- Don't compare to logbook.RETRY_DETAIL or logbook.TASK_DETAIL since
  python has the isinstance function just use it (ideally we should
  fix the code so that this isn't even needed, usage of isinstance
  means something is not designed/structured right).
- In storage tests we can't assume that failures will be non-lossy
  since under certain backends when a failure is stored information
  about the internally held exc_info is lost, so take this into
  account when testing by using matches() where applicable.

Change-Id: Ie8a274cfd4cb4e64e87c355dc99d466d74a4e82c
2014-03-26 12:48:40 -07:00

155 lines
5.2 KiB
Python

# -*- coding: utf-8 -*-
# Copyright (C) 2013 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 networkx.algorithms import traversal
import six
from taskflow import retry as r
from taskflow import states as st
class GraphAnalyzer(object):
"""Analyzes a execution graph to get the next nodes for execution or
reversion by utilizing the graphs nodes and edge relations and comparing
the node state against the states stored in storage.
"""
def __init__(self, graph, storage):
self._graph = graph
self._storage = storage
@property
def execution_graph(self):
return self._graph
def get_next_nodes(self, node=None):
if node is None:
execute = self.browse_nodes_for_execute()
revert = self.browse_nodes_for_revert()
return execute + revert
state = self.get_state(node)
intention = self._storage.get_atom_intention(node.name)
if state == st.SUCCESS:
if intention == st.REVERT:
return [node]
elif intention == st.EXECUTE:
return self.browse_nodes_for_execute(node)
else:
return []
elif state == st.REVERTED:
return self.browse_nodes_for_revert(node)
elif state == st.FAILURE:
return self.browse_nodes_for_revert()
else:
return []
def browse_nodes_for_execute(self, node=None):
"""Browse next nodes to execute for given node if specified and
for whole graph otherwise.
"""
if node:
nodes = self._graph.successors(node)
else:
nodes = self._graph.nodes_iter()
available_nodes = []
for node in nodes:
if self._is_ready_for_execute(node):
available_nodes.append(node)
return available_nodes
def browse_nodes_for_revert(self, node=None):
"""Browse next nodes to revert for given node if specified and
for whole graph otherwise.
"""
if node:
nodes = self._graph.predecessors(node)
else:
nodes = self._graph.nodes_iter()
available_nodes = []
for node in nodes:
if self._is_ready_for_revert(node):
available_nodes.append(node)
return available_nodes
def _is_ready_for_execute(self, task):
"""Checks if task is ready to be executed."""
state = self.get_state(task)
intention = self._storage.get_atom_intention(task.name)
transition = st.check_task_transition(state, st.RUNNING)
if not transition or intention != st.EXECUTE:
return False
task_names = []
for prev_task in self._graph.predecessors(task):
task_names.append(prev_task.name)
task_states = self._storage.get_atoms_states(task_names)
return all(state == st.SUCCESS and intention == st.EXECUTE
for state, intention in six.itervalues(task_states))
def _is_ready_for_revert(self, task):
"""Checks if task is ready to be reverted."""
state = self.get_state(task)
intention = self._storage.get_atom_intention(task.name)
transition = st.check_task_transition(state, st.REVERTING)
if not transition or intention not in (st.REVERT, st.RETRY):
return False
task_names = []
for prev_task in self._graph.successors(task):
task_names.append(prev_task.name)
task_states = self._storage.get_atoms_states(task_names)
return all(state in (st.PENDING, st.REVERTED)
for state, intention in six.itervalues(task_states))
def iterate_subgraph(self, retry):
"""Iterates a subgraph connected to current retry controller, including
nested retry controllers and its nodes.
"""
for _src, dst in traversal.dfs_edges(self._graph, retry):
yield dst
def iterate_retries(self, state=None):
"""Iterates retry controllers of a graph with given state or all
retries if state is None.
"""
for node in self._graph.nodes_iter():
if isinstance(node, r.Retry):
if not state or self.get_state(node) == state:
yield node
def iterate_all_nodes(self):
for node in self._graph.nodes_iter():
yield node
def find_atom_retry(self, atom):
return self._graph.node[atom].get('retry')
def is_success(self):
for node in self._graph.nodes_iter():
if self.get_state(node) != st.SUCCESS:
return False
return True
def get_state(self, node):
return self._storage.get_atom_state(node.name)