75517eb0e0
We can just use the non-recursive depth first iteration of nodes when scanning for atoms to select for a given scope level instead of using recursive calls to achieve the same effect. This makes it possible to have large and heavily nested flows that are not restricted by the python stack limit. Change-Id: I0d18565680f777adbdfca9d4983636c6b3e848da
138 lines
6.0 KiB
Python
138 lines
6.0 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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from taskflow.engines.action_engine import compiler as co
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from taskflow import logging
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LOG = logging.getLogger(__name__)
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def _depth_first_reverse_iterate(node, idx=-1):
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"""Iterates connected (in reverse) nodes (from starting node).
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Jumps through nodes with ``FLOW`` ``kind`` attribute (does not yield
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them back).
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"""
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# Always go left to right, since right to left is the pattern order
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# and we want to go backwards and not forwards through that ordering...
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if idx == -1:
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children_iter = node.reverse_iter()
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else:
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children_iter = reversed(node[0:idx])
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for child in children_iter:
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if child.metadata['kind'] == co.FLOW:
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# Jump through these...
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for child_child in child.dfs_iter(right_to_left=False):
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if child_child.metadata['kind'] in co.ATOMS:
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yield child_child.item
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else:
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yield child.item
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class ScopeWalker(object):
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"""Walks through the scopes of a atom using a engines compilation.
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NOTE(harlowja): for internal usage only.
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This will walk the visible scopes that are accessible for the given
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atom, which can be used by some external entity in some meaningful way,
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for example to find dependent values...
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"""
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def __init__(self, compilation, atom, names_only=False):
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self._node = compilation.hierarchy.find(atom)
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if self._node is None:
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raise ValueError("Unable to find atom '%s' in compilation"
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" hierarchy" % atom)
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self._level_cache = {}
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self._atom = atom
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self._execution_graph = compilation.execution_graph
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self._names_only = names_only
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self._predecessors = None
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def __iter__(self):
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"""Iterates over the visible scopes.
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How this works is the following:
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We first grab all the predecessors of the given atom (lets call it
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``Y``) by using the :py:class:`~.compiler.Compilation` execution
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graph (and doing a reverse breadth-first expansion to gather its
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predecessors), this is useful since we know they *always* will
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exist (and execute) before this atom but it does not tell us the
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corresponding scope *level* (flow, nested flow...) that each
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predecessor was created in, so we need to find this information.
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For that information we consult the location of the atom ``Y`` in the
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:py:class:`~.compiler.Compilation` hierarchy/tree. We lookup in a
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reverse order the parent ``X`` of ``Y`` and traverse backwards from
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the index in the parent where ``Y`` exists to all siblings (and
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children of those siblings) in ``X`` that we encounter in this
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backwards search (if a sibling is a flow itself, its atom(s)
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will be recursively expanded and included). This collection will
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then be assumed to be at the same scope. This is what is called
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a *potential* single scope, to make an *actual* scope we remove the
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items from the *potential* scope that are **not** predecessors
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of ``Y`` to form the *actual* scope which we then yield back.
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Then for additional scopes we continue up the tree, by finding the
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parent of ``X`` (lets call it ``Z``) and perform the same operation,
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going through the children in a reverse manner from the index in
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parent ``Z`` where ``X`` was located. This forms another *potential*
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scope which we provide back as an *actual* scope after reducing the
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potential set to only include predecessors previously gathered. We
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then repeat this process until we no longer have any parent
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nodes (aka we have reached the top of the tree) or we run out of
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predecessors.
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"""
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graph = self._execution_graph
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if self._predecessors is None:
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predecessors = set(
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node for node in graph.bfs_predecessors_iter(self._atom)
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if graph.node[node]['kind'] in co.ATOMS)
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self._predecessors = predecessors.copy()
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else:
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predecessors = self._predecessors.copy()
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last = self._node
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for lvl, parent in enumerate(self._node.path_iter(include_self=False)):
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if not predecessors:
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break
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last_idx = parent.index(last.item)
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try:
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visible, removals = self._level_cache[lvl]
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predecessors = predecessors - removals
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except KeyError:
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visible = []
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removals = set()
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for atom in _depth_first_reverse_iterate(parent, idx=last_idx):
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if atom in predecessors:
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predecessors.remove(atom)
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removals.add(atom)
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visible.append(atom)
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if not predecessors:
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break
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self._level_cache[lvl] = (visible, removals)
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if LOG.isEnabledFor(logging.BLATHER):
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visible_names = [a.name for a in visible]
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LOG.blather("Scope visible to '%s' (limited by parent '%s'"
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" index < %s) is: %s", self._atom,
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parent.item.name, last_idx, visible_names)
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if self._names_only:
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yield [a.name for a in visible]
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else:
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yield visible
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last = parent
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