This change adds graph flow like pattern that is able to ignore graph nodes (not even add them to flow) if they are not needed to run certain task. Implements blueprint targeted-graph-flow Change-Id: I57d5a1c0598bd032d77e2a262b2d9644418ce3f6
225 lines
7.5 KiB
Python
225 lines
7.5 KiB
Python
# -*- coding: utf-8 -*-
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# vim: tabstop=4 shiftwidth=4 softtabstop=4
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# Copyright (C) 2012 Yahoo! Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import collections
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import networkx as nx
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from networkx.algorithms import traversal
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from taskflow import exceptions as exc
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from taskflow import flow
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from taskflow.utils import graph_utils
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class Flow(flow.Flow):
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"""Graph flow pattern
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Contained *flows/tasks* will be executed according to their dependencies
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which will be resolved by using the *flows/tasks* provides and requires
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mappings or by following manually created dependency links.
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From dependencies directed graph is build. If it has edge A -> B, this
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means B depends on A.
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Note: Cyclic dependencies are not allowed.
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"""
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def __init__(self, name):
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super(Flow, self).__init__(name)
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self._graph = nx.freeze(nx.DiGraph())
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def _validate(self, graph=None):
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if graph is None:
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graph = self._graph
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# Ensure that there is a valid topological ordering.
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if not nx.is_directed_acyclic_graph(graph):
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raise exc.DependencyFailure("No path through the items in the"
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" graph produces an ordering that"
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" will allow for correct dependency"
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" resolution")
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def link(self, u, v):
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"""Link existing node u as a runtime dependency of existing node v."""
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if not self._graph.has_node(u):
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raise ValueError('Item %s not found to link from' % (u))
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if not self._graph.has_node(v):
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raise ValueError('Item %s not found to link to' % (v))
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self._swap(self._link(u, v, manual=True))
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return self
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def _link(self, u, v, graph=None, reason=None, manual=False):
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mutable_graph = True
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if graph is None:
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graph = self._graph
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mutable_graph = False
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# NOTE(harlowja): Add an edge to a temporary copy and only if that
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# copy is valid then do we swap with the underlying graph.
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attrs = graph_utils.get_edge_attrs(graph, u, v)
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if not attrs:
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attrs = {}
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if manual:
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attrs['manual'] = True
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if reason is not None:
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if 'reasons' not in attrs:
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attrs['reasons'] = set()
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attrs['reasons'].add(reason)
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if not mutable_graph:
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graph = nx.DiGraph(graph)
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graph.add_edge(u, v, **attrs)
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return graph
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def _swap(self, replacement_graph):
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"""Validates the replacement graph and then swaps the underlying graph
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with a frozen version of the replacement graph (this maintains the
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invariant that the underlying graph is immutable).
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"""
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self._validate(replacement_graph)
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self._graph = nx.freeze(replacement_graph)
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def add(self, *items):
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"""Adds a given task/tasks/flow/flows to this flow."""
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items = [i for i in items if not self._graph.has_node(i)]
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if not items:
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return self
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requirements = collections.defaultdict(list)
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provided = {}
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def update_requirements(node):
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for value in node.requires:
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requirements[value].append(node)
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for node in self:
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update_requirements(node)
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for value in node.provides:
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provided[value] = node
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# NOTE(harlowja): Add items and edges to a temporary copy of the
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# underlying graph and only if that is successful added to do we then
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# swap with the underlying graph.
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tmp_graph = nx.DiGraph(self._graph)
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for item in items:
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tmp_graph.add_node(item)
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update_requirements(item)
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for value in item.provides:
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if value in provided:
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raise exc.DependencyFailure(
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"%(item)s provides %(value)s but is already being"
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" provided by %(flow)s and duplicate producers"
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" are disallowed"
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% dict(item=item.name,
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flow=provided[value].name,
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value=value))
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provided[value] = item
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for value in item.requires:
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if value in provided:
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self._link(provided[value], item,
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graph=tmp_graph, reason=value)
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for value in item.provides:
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if value in requirements:
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for node in requirements[value]:
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self._link(item, node,
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graph=tmp_graph, reason=value)
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self._swap(tmp_graph)
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return self
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def __len__(self):
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return self.graph.number_of_nodes()
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def __iter__(self):
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for n in self.graph.nodes_iter():
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yield n
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@property
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def provides(self):
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provides = set()
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for subflow in self:
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provides.update(subflow.provides)
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return provides
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@property
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def requires(self):
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requires = set()
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for subflow in self:
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requires.update(subflow.requires)
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return requires - self.provides
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@property
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def graph(self):
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return self._graph
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class TargetedFlow(Flow):
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"""Graph flow with a target.
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Adds possibility to execute a flow up to certain graph node
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(task or subflow).
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"""
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def __init__(self, *args, **kwargs):
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super(TargetedFlow, self).__init__(*args, **kwargs)
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self._subgraph = None
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self._target = None
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def set_target(self, target_item):
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"""Set target for the flow.
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Any items (tasks or subflows) not needed for the target
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item will not be executed.
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"""
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if not self._graph.has_node(target_item):
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raise ValueError('Item %s not found' % target_item)
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self._target = target_item
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self._subgraph = None
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def reset_target(self):
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"""Reset target for the flow.
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All items of the flow will be executed.
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"""
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self._target = None
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self._subgraph = None
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def add(self, *items):
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"""Adds a given task/tasks/flow/flows to this flow."""
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super(TargetedFlow, self).add(*items)
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# reset cached subgraph, in case it was affected
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self._subgraph = None
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def link(self, u, v):
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"""Link existing node u as a runtime dependency of existing node v."""
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super(TargetedFlow, self).link(u, v)
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# reset cached subgraph, in case it was affected
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self._subgraph = None
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@property
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def graph(self):
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if self._subgraph is not None:
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return self._subgraph
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if self._target is None:
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return self._graph
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nodes = [self._target]
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nodes.extend(dst for _src, dst in
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traversal.dfs_edges(self._graph.reverse(), self._target))
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self._subgraph = nx.freeze(self._graph.subgraph(nodes))
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return self._subgraph
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