Files
deb-python-taskflow/taskflow/utils/flow_utils.py
Joshua Harlow 5ca61f956e Add a directed graph type (new types module)
Most of the utility graph functions we have can
be connected to a directed graph class that itself
derives (and adds on to) the networkx base class.

Doing this allows for functionality that isn't exposed
in networkx to be exposed in our subclass (which is a
useful pattern to have).

It also makes it possible (if ever needed) to replace
the networkx usage in taskflow with something else if
this ever becomes a major request.

Change-Id: I0a825d5637236d7b5dbdbda0d426adb0183d5ba3
2014-04-20 17:28:27 -07:00

181 lines
6.9 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.
import logging
from taskflow import exceptions
from taskflow import flow
from taskflow import retry
from taskflow import task
from taskflow.types import graph as gr
from taskflow.utils import misc
LOG = logging.getLogger(__name__)
RETRY_EDGE_DATA = {
'retry': True,
}
class Flattener(object):
def __init__(self, root, freeze=True):
self._root = root
self._graph = None
self._history = set()
self._freeze = bool(freeze)
def _add_new_edges(self, graph, nodes_from, nodes_to, edge_attrs):
"""Adds new edges from nodes to other nodes in the specified graph,
with the following edge attributes (defaulting to the class provided
edge_data if None), if the edge does not already exist.
"""
nodes_to = list(nodes_to)
for u in nodes_from:
for v in nodes_to:
if not graph.has_edge(u, v):
# NOTE(harlowja): give each edge its own attr copy so that
# if it's later modified that the same copy isn't modified.
graph.add_edge(u, v, attr_dict=edge_attrs.copy())
def _flatten(self, item):
functor = self._find_flattener(item)
if not functor:
raise TypeError("Unknown type requested to flatten: %s (%s)"
% (item, type(item)))
self._pre_item_flatten(item)
graph = functor(item)
self._post_item_flatten(item, graph)
return graph
def _find_flattener(self, item):
"""Locates the flattening function to use to flatten the given item."""
if isinstance(item, flow.Flow):
return self._flatten_flow
elif isinstance(item, task.BaseTask):
return self._flatten_task
elif isinstance(item, retry.Retry):
raise TypeError("Retry controller %s (%s) is used not as a flow "
"parameter" % (item, type(item)))
else:
return None
def _connect_retry(self, retry, graph):
graph.add_node(retry)
# All graph nodes that have no predecessors should depend on its retry
nodes_to = [n for n in graph.no_predecessors_iter() if n != retry]
self._add_new_edges(graph, [retry], nodes_to, RETRY_EDGE_DATA)
# Add link to retry for each node of subgraph that hasn't
# a parent retry
for n in graph.nodes_iter():
if n != retry and 'retry' not in graph.node[n]:
graph.node[n]['retry'] = retry
def _flatten_task(self, task):
"""Flattens a individual task."""
graph = gr.DiGraph(name=task.name)
graph.add_node(task)
return graph
def _flatten_flow(self, flow):
"""Flattens a graph flow."""
graph = gr.DiGraph(name=flow.name)
# Flatten all nodes into a single subgraph per node.
subgraph_map = {}
for item in flow:
subgraph = self._flatten(item)
subgraph_map[item] = subgraph
graph = gr.merge_graphs([graph, subgraph])
# Reconnect all node edges to their corresponding subgraphs.
for (u, v, attrs) in flow.iter_links():
u_g = subgraph_map[u]
v_g = subgraph_map[v]
if any(attrs.get(k) for k in ('invariant', 'manual', 'retry')):
# Connect nodes with no predecessors in v to nodes with
# no successors in u (thus maintaining the edge dependency).
self._add_new_edges(graph,
u_g.no_successors_iter(),
v_g.no_predecessors_iter(),
edge_attrs=attrs)
else:
# This is dependency-only edge, connect corresponding
# providers and consumers.
for provider in u_g:
for consumer in v_g:
reasons = provider.provides & consumer.requires
if reasons:
graph.add_edge(provider, consumer, reasons=reasons)
if flow.retry is not None:
self._connect_retry(flow.retry, graph)
return graph
def _pre_item_flatten(self, item):
"""Called before a item is flattened; any pre-flattening actions."""
if id(item) in self._history:
raise ValueError("Already flattened item: %s (%s), recursive"
" flattening not supported" % (item, id(item)))
LOG.debug("Starting to flatten '%s'", item)
self._history.add(id(item))
def _post_item_flatten(self, item, graph):
"""Called before a item is flattened; any post-flattening actions."""
LOG.debug("Finished flattening '%s'", item)
# NOTE(harlowja): this one can be expensive to calculate (especially
# the cycle detection), so only do it if we know debugging is enabled
# and not under all cases.
if LOG.isEnabledFor(logging.DEBUG):
LOG.debug("Translated '%s' into a graph:", item)
for line in graph.pformat().splitlines():
# Indent it so that it's slightly offset from the above line.
LOG.debug(" %s", line)
def _pre_flatten(self):
"""Called before the flattening of the item starts."""
self._history.clear()
def _post_flatten(self, graph):
"""Called after the flattening of the item finishes successfully."""
dup_names = misc.get_duplicate_keys(graph.nodes_iter(),
key=lambda node: node.name)
if dup_names:
dup_names = ', '.join(sorted(dup_names))
raise exceptions.Duplicate("Tasks with duplicate names "
"found: %s" % (dup_names))
self._history.clear()
def flatten(self):
"""Flattens a item (a task or flow) into a single execution graph."""
if self._graph is not None:
return self._graph
self._pre_flatten()
graph = self._flatten(self._root)
self._post_flatten(graph)
self._graph = graph
if self._freeze:
self._graph.freeze()
return self._graph
def flatten(item, freeze=True):
"""Flattens a item (a task or flow) into a single execution graph."""
return Flattener(item, freeze=freeze).flatten()