a32f9245f9
Since flattening is only one way to compile a flow and nested flows and atoms into a compilation unit move this functionality into the engine module where it is used. Change-Id: Ifea6b56cf5f2a9c1d16acabfaae6f28aeb6534a0
206 lines
8.2 KiB
Python
206 lines
8.2 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2014 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 logging
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from taskflow import exceptions as exc
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from taskflow import flow
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from taskflow import retry
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from taskflow import task
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from taskflow.types import graph as gr
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from taskflow.utils import misc
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LOG = logging.getLogger(__name__)
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# The result of a compilers compile() is this tuple (for now it is just a
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# execution graph but in the future it may grow to include more attributes
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# that help the runtime units execute in a more optimal/featureful manner).
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Compilation = collections.namedtuple("Compilation", ["execution_graph"])
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class PatternCompiler(object):
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"""Compiles patterns & atoms (potentially nested) into an compilation
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unit with a *logically* equivalent directed acyclic graph representation.
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NOTE(harlowja): during this pattern translation process any nested flows
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will be converted into there equivalent subgraphs. This currently implies
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that contained atoms in those nested flows, post-translation will no longer
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be associated with there previously containing flow but instead will lose
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this identity and what will remain is the logical constraints that there
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contained flow mandated. In the future this may be changed so that this
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association is not lost via the compilation process (since it is sometime
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useful to retain part of this relationship).
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"""
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def compile(self, root):
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graph = _Flattener(root).flatten()
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if graph.number_of_nodes() == 0:
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# Try to get a name attribute, otherwise just use the object
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# string representation directly if that attribute does not exist.
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name = getattr(root, 'name', root)
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raise exc.Empty("Root container '%s' (%s) is empty."
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% (name, type(root)))
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return Compilation(graph)
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_RETRY_EDGE_DATA = {
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'retry': True,
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}
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class _Flattener(object):
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"""Flattens a root item (task/flow) into a execution graph."""
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def __init__(self, root, freeze=True):
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self._root = root
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self._graph = None
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self._history = set()
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self._freeze = bool(freeze)
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def _add_new_edges(self, graph, nodes_from, nodes_to, edge_attrs):
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"""Adds new edges from nodes to other nodes in the specified graph,
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with the following edge attributes (defaulting to the class provided
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edge_data if None), if the edge does not already exist.
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"""
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nodes_to = list(nodes_to)
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for u in nodes_from:
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for v in nodes_to:
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if not graph.has_edge(u, v):
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# NOTE(harlowja): give each edge its own attr copy so that
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# if it's later modified that the same copy isn't modified.
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graph.add_edge(u, v, attr_dict=edge_attrs.copy())
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def _flatten(self, item):
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functor = self._find_flattener(item)
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if not functor:
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raise TypeError("Unknown type requested to flatten: %s (%s)"
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% (item, type(item)))
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self._pre_item_flatten(item)
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graph = functor(item)
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self._post_item_flatten(item, graph)
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return graph
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def _find_flattener(self, item):
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"""Locates the flattening function to use to flatten the given item."""
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if isinstance(item, flow.Flow):
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return self._flatten_flow
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elif isinstance(item, task.BaseTask):
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return self._flatten_task
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elif isinstance(item, retry.Retry):
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raise TypeError("Retry controller %s (%s) is used not as a flow "
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"parameter" % (item, type(item)))
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else:
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return None
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def _connect_retry(self, retry, graph):
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graph.add_node(retry)
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# All graph nodes that have no predecessors should depend on its retry
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nodes_to = [n for n in graph.no_predecessors_iter() if n != retry]
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self._add_new_edges(graph, [retry], nodes_to, _RETRY_EDGE_DATA)
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# Add link to retry for each node of subgraph that hasn't
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# a parent retry
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for n in graph.nodes_iter():
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if n != retry and 'retry' not in graph.node[n]:
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graph.node[n]['retry'] = retry
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def _flatten_task(self, task):
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"""Flattens a individual task."""
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graph = gr.DiGraph(name=task.name)
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graph.add_node(task)
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return graph
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def _flatten_flow(self, flow):
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"""Flattens a graph flow."""
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graph = gr.DiGraph(name=flow.name)
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# Flatten all nodes into a single subgraph per node.
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subgraph_map = {}
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for item in flow:
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subgraph = self._flatten(item)
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subgraph_map[item] = subgraph
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graph = gr.merge_graphs([graph, subgraph])
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# Reconnect all node edges to their corresponding subgraphs.
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for (u, v, attrs) in flow.iter_links():
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u_g = subgraph_map[u]
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v_g = subgraph_map[v]
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if any(attrs.get(k) for k in ('invariant', 'manual', 'retry')):
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# Connect nodes with no predecessors in v to nodes with
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# no successors in u (thus maintaining the edge dependency).
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self._add_new_edges(graph,
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u_g.no_successors_iter(),
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v_g.no_predecessors_iter(),
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edge_attrs=attrs)
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else:
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# This is dependency-only edge, connect corresponding
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# providers and consumers.
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for provider in u_g:
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for consumer in v_g:
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reasons = provider.provides & consumer.requires
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if reasons:
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graph.add_edge(provider, consumer, reasons=reasons)
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if flow.retry is not None:
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self._connect_retry(flow.retry, graph)
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return graph
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def _pre_item_flatten(self, item):
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"""Called before a item is flattened; any pre-flattening actions."""
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if id(item) in self._history:
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raise ValueError("Already flattened item: %s (%s), recursive"
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" flattening not supported" % (item, id(item)))
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self._history.add(id(item))
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def _post_item_flatten(self, item, graph):
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"""Called before a item is flattened; any post-flattening actions."""
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def _pre_flatten(self):
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"""Called before the flattening of the item starts."""
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self._history.clear()
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def _post_flatten(self, graph):
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"""Called after the flattening of the item finishes successfully."""
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dup_names = misc.get_duplicate_keys(graph.nodes_iter(),
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key=lambda node: node.name)
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if dup_names:
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dup_names = ', '.join(sorted(dup_names))
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raise exc.Duplicate("Atoms with duplicate names "
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"found: %s" % (dup_names))
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self._history.clear()
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# NOTE(harlowja): this one can be expensive to calculate (especially
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# the cycle detection), so only do it if we know debugging is enabled
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# and not under all cases.
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if LOG.isEnabledFor(logging.DEBUG):
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LOG.debug("Translated '%s' into a graph:", self._root)
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for line in graph.pformat().splitlines():
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# Indent it so that it's slightly offset from the above line.
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LOG.debug(" %s", line)
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def flatten(self):
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"""Flattens a item (a task or flow) into a single execution graph."""
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if self._graph is not None:
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return self._graph
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self._pre_flatten()
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graph = self._flatten(self._root)
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self._post_flatten(graph)
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self._graph = graph
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if self._freeze:
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self._graph.freeze()
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return self._graph
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