Adjust the graph analyzer to be a more generic compilation analyzer which analyzes compilation objects (which are now changed to be an object and not a named tuple) and provides utility functions ontop of that object. This helps it become possible to provide other useful analysis functions that are not directly tied to the execution graph component but can be provided ontop of other compilation components. Change-Id: I2ab08db4f566d5f329d7e79b1c50ed65aad9e4b3
153 lines
5.3 KiB
Python
153 lines
5.3 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 retry_atom
|
|
from taskflow import states as st
|
|
|
|
|
|
class Analyzer(object):
|
|
"""Analyzes a compilation output to get the next atoms for execution or
|
|
reversion by utilizing the compilations underlying structures (graphs,
|
|
nodes and edge relations...) and using this information along with the
|
|
atom state/states stored in storage to provide useful analysis functions
|
|
to the rest of the runtime system.
|
|
"""
|
|
|
|
def __init__(self, compilation, storage):
|
|
self._storage = storage
|
|
self._graph = compilation.execution_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, retry_atom.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)
|