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# Copyright (C) 2009 Canonical Ltd
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
"""Implementation of Graph algorithms when we have already loaded everything.
"""
import heapq
from bzrlib import (
revision,
)
class _KnownGraphNode(object):
"""Represents a single object in the known graph."""
__slots__ = ('key', 'parent_keys', 'child_keys', 'linear_dominator',
'gdfo', 'ancestor_of')
def __init__(self, key, parent_keys):
self.key = key
self.parent_keys = parent_keys
self.child_keys = []
# oldest ancestor, such that no parents between here and there have >1
# child or >1 parent.
self.linear_dominator = None
# Greatest distance from origin
self.gdfo = None
# This will become a tuple of known heads that have this node as an
# ancestor
self.ancestor_of = None
def __repr__(self):
return '%s(%s gdfo:%s par:%s child:%s %s)' % (
self.__class__.__name__, self.key, self.gdfo,
self.parent_keys, self.child_keys,
self.linear_dominator)
class KnownGraph(object):
"""This is a class which assumes we already know the full graph."""
def __init__(self, parent_map, do_cache=True):
"""Create a new KnownGraph instance.
:param parent_map: A dictionary mapping key => parent_keys
"""
self._nodes = {}
# Maps {sorted(revision_id, revision_id): heads}
self._known_heads = {}
self.do_cache = do_cache
self._initialize_nodes(parent_map)
self._find_linear_dominators()
self._find_gdfo()
def _initialize_nodes(self, parent_map):
"""Populate self._nodes.
After this has finished, self._nodes will have an entry for every entry
in parent_map. Ghosts will have a parent_keys = None, all nodes found
will also have .child_keys populated with all known child_keys.
"""
nodes = self._nodes
for key, parent_keys in parent_map.iteritems():
if key in nodes:
node = nodes[key]
node.parent_keys = parent_keys
else:
node = _KnownGraphNode(key, parent_keys)
nodes[key] = node
for parent_key in parent_keys:
try:
parent_node = nodes[parent_key]
except KeyError:
parent_node = _KnownGraphNode(parent_key, None)
nodes[parent_key] = parent_node
parent_node.child_keys.append(key)
def _find_linear_dominators(self):
"""For each node in the set, find any linear dominators.
For any given node, the 'linear dominator' is an ancestor, such that
all parents between this node and that one have a single parent, and a
single child. So if A->B->C->D then B,C,D all have a linear dominator
of A.
There are two main benefits:
1) When walking the graph, we can jump to the nearest linear dominator,
rather than walking all of the nodes inbetween.
2) When caching heads() results, dominators give the "same" results as
their children. (If the dominator is a head, then the descendant is
a head, if the dominator is not a head, then the child isn't
either.)
"""
def check_node(node):
if node.parent_keys is None or len(node.parent_keys) != 1:
# This node is either a ghost, a tail, or has multiple parents
# It its own dominator
node.linear_dominator = node.key
return None
parent_node = self._nodes[node.parent_keys[0]]
if len(parent_node.child_keys) > 1:
# The parent has multiple children, so *this* node is the
# dominator
node.linear_dominator = node.key
return None
# The parent is already filled in, so add and continue
if parent_node.linear_dominator is not None:
node.linear_dominator = parent_node.linear_dominator
return None
# We don't know this node, or its parent node, so start walking to
# next
return parent_node
for node in self._nodes.itervalues():
# The parent is not filled in, so walk until we get somewhere
if node.linear_dominator is not None: #already done
continue
next_node = check_node(node)
if next_node is None:
# Nothing more needs to be done
continue
stack = []
while next_node is not None:
stack.append(node)
node = next_node
next_node = check_node(node)
# The stack now contains the linear chain, and 'node' should have
# been labeled
dominator = node.linear_dominator
while stack:
next_node = stack.pop()
next_node.linear_dominator = dominator
node = next_node
def _find_gdfo(self):
def find_tails():
return [node for node in self._nodes.itervalues()
if not node.parent_keys]
tails = find_tails()
todo = []
heappush = heapq.heappush
heappop = heapq.heappop
nodes = self._nodes
for node in tails:
node.gdfo = 1
heappush(todo, (1, node))
processed = 0
while todo:
gdfo, next = heappop(todo)
processed += 1
if next.gdfo is not None and gdfo < next.gdfo:
# This node was reached from a longer path, we assume it was
# enqued correctly with the longer gdfo, so don't continue
# processing now
continue
next_gdfo = gdfo + 1
for child_key in next.child_keys:
child_node = nodes[child_key]
if child_node.gdfo is None or child_node.gdfo < next_gdfo:
# Only enque children when all of their parents have been
# resolved
for parent_key in child_node.parent_keys:
# We know that 'this' parent is counted
if parent_key != next.key:
parent_node = nodes[parent_key]
if parent_node.gdfo is None:
break
else:
child_node.gdfo = next_gdfo
heappush(todo, (next_gdfo, child_node))
def _get_dominators_to_nodes(self, candidate_nodes):
"""Get the reverse mapping from dominator_key => candidate_nodes.
As a side effect, this can also remove potential candidate nodes if we
determine that they share a dominator.
"""
dom_to_node = {}
keys_to_remove = []
for node in candidate_nodes.values():
if node.linear_dominator in dom_to_node:
# This node already exists, resolve which node supersedes the
# other
other_node = dom_to_node[node.linear_dominator]
# There should be no way that nodes sharing a dominator could
# 'tie' for gdfo
if other_node.gdfo > node.gdfo:
# The other node has this node as an ancestor
keys_to_remove.append(node.key)
else:
# Replace the other node, and set this as the new key
keys_to_remove.append(other_node.key)
dom_to_node[node.linear_dominator] = node
else:
dom_to_node[node.linear_dominator] = node
for key in keys_to_remove:
candidate_nodes.pop(key)
return dom_to_node
def heads(self, keys):
"""Return the heads from amongst keys.
This is done by searching the ancestries of each key. Any key that is
reachable from another key is not returned; all the others are.
This operation scales with the relative depth between any two keys. If
any two keys are completely disconnected all ancestry of both sides
will be retrieved.
:param keys: An iterable of keys.
:return: A set of the heads. Note that as a set there is no ordering
information. Callers will need to filter their input to create
order if they need it.
"""
candidate_nodes = dict((key, self._nodes[key]) for key in keys)
if revision.NULL_REVISION in candidate_nodes:
# NULL_REVISION is only a head if it is the only entry
candidate_nodes.pop(revision.NULL_REVISION)
if not candidate_nodes:
return set([revision.NULL_REVISION])
if len(candidate_nodes) < 2:
return frozenset(candidate_nodes)
heads_key = frozenset(candidate_nodes)
if heads_key != frozenset(keys):
note('%s != %s', heads_key, frozenset(keys))
try:
heads = self._known_heads[heads_key]
return heads
except KeyError:
pass # compute it ourselves
dom_to_node = self._get_dominators_to_nodes(candidate_nodes)
if len(candidate_nodes) < 2:
# We shrunk candidate_nodes and determined a new head
return frozenset(candidate_nodes)
dom_heads_key = None
# Check the linear dominators of these keys, to see if we already
# know the heads answer
dom_heads_key = frozenset([node.linear_dominator
for node in candidate_nodes.itervalues()])
if dom_heads_key in self._known_heads:
# map back into the original keys
heads = self._known_heads[dom_heads_key]
heads = frozenset([dom_to_node[key].key for key in heads])
return heads
heads = self._heads_from_candidate_nodes(candidate_nodes, dom_to_node)
if self.do_cache:
self._known_heads[heads_key] = heads
# Cache the dominator heads
if dom_heads_key is not None:
dom_heads = frozenset([candidate_nodes[key].linear_dominator
for key in heads])
self._known_heads[dom_heads_key] = dom_heads
return heads
def _heads_from_candidate_nodes(self, candidate_nodes, dom_to_node):
queue = []
to_cleanup = []
to_cleanup_append = to_cleanup.append
for node in candidate_nodes.itervalues():
node.ancestor_of = (node.key,)
queue.append((-node.gdfo, node))
to_cleanup_append(node)
heapq.heapify(queue)
# These are nodes that we determined are 'common' that we are no longer
# walking
# Now we walk nodes until all nodes that are being walked are 'common'
num_candidates = len(candidate_nodes)
nodes = self._nodes
heappop = heapq.heappop
heappush = heapq.heappush
while queue and len(candidate_nodes) > 1:
_, node = heappop(queue)
next_ancestor_of = node.ancestor_of
if len(next_ancestor_of) == num_candidates:
# This node is now considered 'common'
# Make sure all parent nodes are marked as such
for parent_key in node.parent_keys:
parent_node = nodes[parent_key]
if parent_node.ancestor_of is not None:
parent_node.ancestor_of = next_ancestor_of
if node.linear_dominator != node.key:
parent_node = nodes[node.linear_dominator]
if parent_node.ancestor_of is not None:
parent_node.ancestor_of = next_ancestor_of
continue
if node.parent_keys is None:
# This is a ghost
continue
# Now project the current nodes ancestor list to the parent nodes,
# and queue them up to be walked
# Note: using linear_dominator speeds things up quite a bit
# enough that we actually start to be slightly faster
# than the default heads() implementation
if node.linear_dominator != node.key:
# We are at the tip of a long linear region
# We know that there is nothing between here and the tail
# that is interesting, so skip to the end
parent_keys = [node.linear_dominator]
else:
parent_keys = node.parent_keys
for parent_key in parent_keys:
if parent_key in candidate_nodes:
candidate_nodes.pop(parent_key)
if len(candidate_nodes) <= 1:
break
elif parent_key in dom_to_node:
orig_node = dom_to_node[parent_key]
if orig_node is not node:
if orig_node.key in candidate_nodes:
candidate_nodes.pop(orig_node.key)
if len(candidate_nodes) <= 1:
break
parent_node = nodes[parent_key]
ancestor_of = parent_node.ancestor_of
if ancestor_of is None:
# This node hasn't been walked yet
parent_node.ancestor_of = next_ancestor_of
# Enqueue this node
heappush(queue, (-parent_node.gdfo, parent_node))
to_cleanup_append(parent_node)
elif ancestor_of != next_ancestor_of:
# Combine to get the full set of parents
all_ancestors = set(ancestor_of)
all_ancestors.update(next_ancestor_of)
parent_node.ancestor_of = tuple(sorted(all_ancestors))
def cleanup():
for node in to_cleanup:
node.ancestor_of = None
cleanup()
return frozenset(candidate_nodes)
|