d736bdbfae
Instead of recursively executing subflows which causes dead locks when they parent and subflows share the same executor we can instead flatten the parent and subflows into a single graph, composed with only tasks and run this instead, which will not have the issue of subflows dead locking, since after flattening there is no concept of a subflow. Fixes bug: 1225759 Change-Id: I79b9b194cd81e36ce75ba34a673e3e9d3e96c4cd
117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
# -*- coding: utf-8 -*-
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# vim: tabstop=4 shiftwidth=4 softtabstop=4
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# Copyright (C) 2013 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 networkx as nx
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from taskflow.patterns import graph_flow as gf
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from taskflow.patterns import linear_flow as lf
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from taskflow.patterns import unordered_flow as uf
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from taskflow import task
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from taskflow.utils import graph_utils as gu
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# Use the 'flatten' reason as the need to add an edge here, which is useful for
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# doing later analysis of the edges (to determine why the edges were created).
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FLATTEN_REASON = 'flatten'
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FLATTEN_EDGE_DATA = {
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'reason': FLATTEN_REASON,
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}
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def _graph_name(flow):
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return "F:%s:%s" % (flow.name, flow.uuid)
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def _flatten_linear(flow, flattened):
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graph = nx.DiGraph(name=_graph_name(flow))
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previous_nodes = []
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for f in flow:
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subgraph = _flatten(f, flattened)
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graph = gu.merge_graphs([graph, subgraph])
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# Find nodes that have no predecessor, make them have a predecessor of
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# the previous nodes so that the linearity ordering is maintained. Find
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# the ones with no successors and use this list to connect the next
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# subgraph (if any).
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for n in gu.get_no_predecessors(subgraph):
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graph.add_edges_from(((n2, n, FLATTEN_EDGE_DATA)
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for n2 in previous_nodes
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if not graph.has_edge(n2, n)))
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# There should always be someone without successors, otherwise we have
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# a cycle A -> B -> A situation, which should not be possible.
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previous_nodes = list(gu.get_no_successors(subgraph))
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return graph
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def _flatten_unordered(flow, flattened):
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graph = nx.DiGraph(name=_graph_name(flow))
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for f in flow:
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graph = gu.merge_graphs([graph, _flatten(f, flattened)])
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return graph
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def _flatten_task(task):
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graph = nx.DiGraph(name='T:%s' % (task))
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graph.add_node(task)
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return graph
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def _flatten_graph(flow, flattened):
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graph = nx.DiGraph(name=_graph_name(flow))
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subgraph_map = {}
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# Flatten all nodes
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for n in flow.graph.nodes_iter():
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subgraph = _flatten(n, flattened)
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subgraph_map[n] = subgraph
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graph = gu.merge_graphs([graph, subgraph])
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# Reconnect all nodes to there corresponding subgraphs
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for (u, v) in flow.graph.edges_iter():
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u_no_succ = list(gu.get_no_successors(subgraph_map[u]))
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# Connect the ones with no predecessors in v to the ones with no
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# successors in u (thus maintaining the edge dependency).
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for n in gu.get_no_predecessors(subgraph_map[v]):
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graph.add_edges_from(((n2, n, FLATTEN_EDGE_DATA)
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for n2 in u_no_succ
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if not graph.has_edge(n2, n)))
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return graph
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def _flatten(item, flattened):
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"""Flattens a item (task/flow+subflows) into an execution graph."""
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if item in flattened:
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raise ValueError("Already flattened item: %s" % (item))
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if isinstance(item, lf.Flow):
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f = _flatten_linear(item, flattened)
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elif isinstance(item, uf.Flow):
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f = _flatten_unordered(item, flattened)
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elif isinstance(item, gf.Flow):
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f = _flatten_graph(item, flattened)
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elif isinstance(item, task.BaseTask):
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f = _flatten_task(item)
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else:
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raise TypeError("Unknown item: %r, %s" % (type(item), item))
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flattened.add(item)
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return f
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def flatten(item, freeze=True):
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graph = _flatten(item, set())
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if freeze:
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# Frozen graph can't be modified...
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return nx.freeze(graph)
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return graph
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