35794109e9
Instead of returning tuples with fully expanded scopes return walker instances that internally know how to avoid recomputing the visible scopes (they do this by caching each visibility level and looking in the local cache before computing the scope and storing it in the cache). This makes the usage more uniform and avoids returning different items depending on what is found; making the code easier to follow and understand. Also makes the scope walker call to '_extract_atoms' to go via a static method so that if it is ever desired to alter what '_extract_atoms' does it can be more easily done (using standard inheritance). Change-Id: I5916838163e6be843429fe7b89a0b5622e9c2f36
134 lines
5.8 KiB
Python
134 lines
5.8 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 import atom as atom_type
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from taskflow import flow as flow_type
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from taskflow import logging
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LOG = logging.getLogger(__name__)
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def _extract_atoms(node, idx=-1):
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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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atoms = []
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for child in children_iter:
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if isinstance(child.item, flow_type.Flow):
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atoms.extend(_extract_atoms(child))
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elif isinstance(child.item, atom_type.Atom):
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atoms.append(child.item)
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else:
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raise TypeError(
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"Unknown extraction item '%s' (%s)" % (child.item,
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type(child.item)))
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return atoms
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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._graph = compilation.execution_graph
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self._names_only = names_only
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self._predecessors = None
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#: Function that extracts the *associated* atoms of a given tree node.
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_extract_atoms = staticmethod(_extract_atoms)
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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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if self._predecessors is None:
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pred_iter = self._graph.bfs_predecessors_iter(self._atom)
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self._predecessors = set(pred_iter)
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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 a in self._extract_atoms(parent, idx=last_idx):
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if a in predecessors:
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predecessors.remove(a)
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removals.add(a)
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visible.append(a)
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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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