Files
deb-python-taskflow/taskflow/engines/action_engine/graph_analyzer.py
Ivan A. Melnikov 4559fdd841 Optimize dependency links in flattening
With this change a link between two subflows caused by task dependency
does not impose execution ordering between all tasks of that subflows
any more: only provider and consumer of the dependency are now linked.
For some cases this is a major optimization, as it allows more
independent parts of the flow to be executed or reverted in parallel. It
also allows, for example, converting parts of flow into subflows without
loosing performance.

Because with this change parts of parent flow that depend on some tasks
in subflow can be executed before all tasks in the subflow are
completed, graph analyzer is changed to not stop at subflow border when
iterating subgraphs.

Change-Id: I45587a4e851febe3c43cfa7b74bdfc50f61e0279
2014-03-26 13:17:38 +04:00

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_tasks_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_tasks_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_task_state(node.name)