~bzr-pqm/bzr/bzr.dev : contents of bzrlib/graph.py at revision 2592.3.231

~bzr-pqm/bzr/bzr.dev : (revision 2592.3.231)
# Copyright (C) 2007 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., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA

from bzrlib import (
    errors,
    tsort,
    )
from bzrlib.deprecated_graph import (node_distances, select_farthest)
from bzrlib.revision import NULL_REVISION

# DIAGRAM of terminology
#       A
#       /\
#      B  C
#      |  |\
#      D  E F
#      |\/| |
#      |/\|/
#      G  H
#
# In this diagram, relative to G and H:
# A, B, C, D, E are common ancestors.
# C, D and E are border ancestors, because each has a non-common descendant.
# D and E are least common ancestors because none of their descendants are
# common ancestors.
# C is not a least common ancestor because its descendant, E, is a common
# ancestor.
#
# The find_unique_lca algorithm will pick A in two steps:
# 1. find_lca('G', 'H') => ['D', 'E']
# 2. Since len(['D', 'E']) > 1, find_lca('D', 'E') => ['A']



class _StackedParentsProvider(object):

    def __init__(self, parent_providers):
        self._parent_providers = parent_providers

    def __repr__(self):
        return "_StackedParentsProvider(%r)" % self._parent_providers

    def get_parents(self, revision_ids):
        """Find revision ids of the parents of a list of revisions

        A list is returned of the same length as the input.  Each entry
        is a list of parent ids for the corresponding input revision.

        [NULL_REVISION] is used as the parent of the first user-committed
        revision.  Its parent list is empty.

        If the revision is not present (i.e. a ghost), None is used in place
        of the list of parents.
        """
        found = {}
        for parents_provider in self._parent_providers:
            pending_revisions = [r for r in revision_ids if r not in found]
            parent_list = parents_provider.get_parents(pending_revisions)
            new_found = dict((k, v) for k, v in zip(pending_revisions,
                             parent_list) if v is not None)
            found.update(new_found)
            if len(found) == len(revision_ids):
                break
        return [found.get(r, None) for r in revision_ids]


class Graph(object):
    """Provide incremental access to revision graphs.

    This is the generic implementation; it is intended to be subclassed to
    specialize it for other repository types.
    """

    def __init__(self, parents_provider):
        """Construct a Graph that uses several graphs as its input

        This should not normally be invoked directly, because there may be
        specialized implementations for particular repository types.  See
        Repository.get_graph()

        :param parents_func: an object providing a get_parents call
            conforming to the behavior of StackedParentsProvider.get_parents
        """
        self.get_parents = parents_provider.get_parents
        self._parents_provider = parents_provider

    def __repr__(self):
        return 'Graph(%r)' % self._parents_provider

    def find_lca(self, *revisions):
        """Determine the lowest common ancestors of the provided revisions

        A lowest common ancestor is a common ancestor none of whose
        descendants are common ancestors.  In graphs, unlike trees, there may
        be multiple lowest common ancestors.

        This algorithm has two phases.  Phase 1 identifies border ancestors,
        and phase 2 filters border ancestors to determine lowest common
        ancestors.

        In phase 1, border ancestors are identified, using a breadth-first
        search starting at the bottom of the graph.  Searches are stopped
        whenever a node or one of its descendants is determined to be common

        In phase 2, the border ancestors are filtered to find the least
        common ancestors.  This is done by searching the ancestries of each
        border ancestor.

        Phase 2 is perfomed on the principle that a border ancestor that is
        not an ancestor of any other border ancestor is a least common
        ancestor.

        Searches are stopped when they find a node that is determined to be a
        common ancestor of all border ancestors, because this shows that it
        cannot be a descendant of any border ancestor.

        The scaling of this operation should be proportional to
        1. The number of uncommon ancestors
        2. The number of border ancestors
        3. The length of the shortest path between a border ancestor and an
           ancestor of all border ancestors.
        """
        border_common, common, sides = self._find_border_ancestors(revisions)
        # We may have common ancestors that can be reached from each other.
        # - ask for the heads of them to filter it down to only ones that
        # cannot be reached from each other - phase 2.
        return self.heads(border_common)

    def find_difference(self, left_revision, right_revision):
        """Determine the graph difference between two revisions"""
        border, common, (left, right) = self._find_border_ancestors(
            [left_revision, right_revision])
        return (left.difference(right).difference(common),
                right.difference(left).difference(common))

    def _make_breadth_first_searcher(self, revisions):
        return _BreadthFirstSearcher(revisions, self)

    def _find_border_ancestors(self, revisions):
        """Find common ancestors with at least one uncommon descendant.

        Border ancestors are identified using a breadth-first
        search starting at the bottom of the graph.  Searches are stopped
        whenever a node or one of its descendants is determined to be common.

        This will scale with the number of uncommon ancestors.

        As well as the border ancestors, a set of seen common ancestors and a
        list of sets of seen ancestors for each input revision is returned.
        This allows calculation of graph difference from the results of this
        operation.
        """
        if None in revisions:
            raise errors.InvalidRevisionId(None, self)
        common_searcher = self._make_breadth_first_searcher([])
        common_ancestors = set()
        searchers = [self._make_breadth_first_searcher([r])
                     for r in revisions]
        active_searchers = searchers[:]
        border_ancestors = set()
        def update_common(searcher, revisions):
            w_seen_ancestors = searcher.find_seen_ancestors(
                revision)
            stopped = searcher.stop_searching_any(w_seen_ancestors)
            common_ancestors.update(w_seen_ancestors)
            common_searcher.start_searching(stopped)

        while True:
            if len(active_searchers) == 0:
                return border_ancestors, common_ancestors, [s.seen for s in
                                                            searchers]
            try:
                new_common = common_searcher.next()
                common_ancestors.update(new_common)
            except StopIteration:
                pass
            else:
                for searcher in active_searchers:
                    for revision in new_common.intersection(searcher.seen):
                        update_common(searcher, revision)

            newly_seen = set()
            new_active_searchers = []
            for searcher in active_searchers:
                try:
                    newly_seen.update(searcher.next())
                except StopIteration:
                    pass
                else:
                    new_active_searchers.append(searcher)
            active_searchers = new_active_searchers
            for revision in newly_seen:
                if revision in common_ancestors:
                    for searcher in searchers:
                        update_common(searcher, revision)
                    continue
                for searcher in searchers:
                    if revision not in searcher.seen:
                        break
                else:
                    border_ancestors.add(revision)
                    for searcher in searchers:
                        update_common(searcher, revision)

    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_heads = set(keys)
        if len(candidate_heads) < 2:
            return candidate_heads
        searchers = dict((c, self._make_breadth_first_searcher([c]))
                          for c in candidate_heads)
        active_searchers = dict(searchers)
        # skip over the actual candidate for each searcher
        for searcher in active_searchers.itervalues():
            searcher.next()
        # The common walker finds nodes that are common to two or more of the
        # input keys, so that we don't access all history when a currently
        # uncommon search point actually meets up with something behind a
        # common search point. Common search points do not keep searches
        # active; they just allow us to make searches inactive without
        # accessing all history.
        common_walker = self._make_breadth_first_searcher([])
        while len(active_searchers) > 0:
            ancestors = set()
            # advance searches
            try:
                common_walker.next()
            except StopIteration:
                pass
            for candidate in active_searchers.keys():
                try:
                    searcher = active_searchers[candidate]
                except KeyError:
                    # rare case: we deleted candidate in a previous iteration
                    # through this for loop, because it was determined to be
                    # a descendant of another candidate.
                    continue
                try:
                    ancestors.update(searcher.next())
                except StopIteration:
                    del active_searchers[candidate]
                    continue
            # process found nodes
            new_common = set()
            for ancestor in ancestors:
                if ancestor in candidate_heads:
                    candidate_heads.remove(ancestor)
                    del searchers[ancestor]
                    if ancestor in active_searchers:
                        del active_searchers[ancestor]
                # it may meet up with a known common node
                already_common = ancestor in common_walker.seen
                if not already_common:
                    # or it may have been just reached by all the searchers:
                    for searcher in searchers.itervalues():
                        if ancestor not in searcher.seen:
                            common = False
                            break
                    else:
                        common = True
                if already_common:
                    # some searcher has encountered our known common nodes:
                    # just stop it
                    ancestor_set = set([ancestor])
                    for searcher in searchers.itervalues():
                        searcher.stop_searching_any(ancestor_set)
                if common:
                    # The final active searcher has just reached this node,
                    # making it be known as a descendant of all candidates, so
                    # we can stop searching it, and any seen ancestors
                    new_common.add(ancestor)
                    for searcher in searchers.itervalues():
                        seen_ancestors =\
                            searcher.find_seen_ancestors(ancestor)
                        searcher.stop_searching_any(seen_ancestors)
            common_walker.start_searching(new_common)
        return candidate_heads

    def find_unique_lca(self, left_revision, right_revision):
        """Find a unique LCA.

        Find lowest common ancestors.  If there is no unique  common
        ancestor, find the lowest common ancestors of those ancestors.

        Iteration stops when a unique lowest common ancestor is found.
        The graph origin is necessarily a unique lowest common ancestor.

        Note that None is not an acceptable substitute for NULL_REVISION.
        in the input for this method.
        """
        revisions = [left_revision, right_revision]
        while True:
            lca = self.find_lca(*revisions)
            if len(lca) == 1:
                return lca.pop()
            if len(lca) == 0:
                raise errors.NoCommonAncestor(left_revision, right_revision)
            revisions = lca

    def iter_topo_order(self, revisions):
        """Iterate through the input revisions in topological order.

        This sorting only ensures that parents come before their children.
        An ancestor may sort after a descendant if the relationship is not
        visible in the supplied list of revisions.
        """
        sorter = tsort.TopoSorter(zip(revisions, self.get_parents(revisions)))
        return sorter.iter_topo_order()

    def is_ancestor(self, candidate_ancestor, candidate_descendant):
        """Determine whether a revision is an ancestor of another.

        We answer this using heads() as heads() has the logic to perform the
        smallest number of parent looksup to determine the ancestral
        relationship between N revisions.
        """
        return set([candidate_descendant]) == self.heads(
            [candidate_ancestor, candidate_descendant])


class HeadsCache(object):
    """A cache of results for graph heads calls."""

    def __init__(self, graph):
        self.graph = graph
        self._heads = {}

    def heads(self, keys):
        """Return the heads of keys.

        This matches the API of Graph.heads(), specifically the return value is
        a set which can be mutated, and ordering of the input is not preserved
        in the output.

        :see also: Graph.heads.
        :param keys: The keys to calculate heads for.
        :return: A set containing the heads, which may be mutated without
            affecting future lookups.
        """
        keys = frozenset(keys)
        try:
            return set(self._heads[keys])
        except KeyError:
            heads = self.graph.heads(keys)
            self._heads[keys] = heads
            return set(heads)


class HeadsCache(object):
    """A cache of results for graph heads calls."""

    def __init__(self, graph):
        self.graph = graph
        self._heads = {}

    def heads(self, keys):
        """Return the heads of keys.

        :see also: Graph.heads.
        :param keys: The keys to calculate heads for.
        :return: A set containing the heads, which may be mutated without
            affecting future lookups.
        """
        keys = frozenset(keys)
        try:
            return set(self._heads[keys])
        except KeyError:
            heads = self.graph.heads(keys)
            self._heads[keys] = heads
            return set(heads)


class _BreadthFirstSearcher(object):
    """Parallel search the breadth-first the ancestry of revisions.

    This class implements the iterator protocol, but additionally
    1. provides a set of seen ancestors, and
    2. allows some ancestries to be unsearched, via stop_searching_any
    """

    def __init__(self, revisions, parents_provider):
        self._start = set(revisions)
        self._search_revisions = None
        self.seen = set(revisions)
        self._parents_provider = parents_provider 

    def __repr__(self):
        return ('_BreadthFirstSearcher(self._search_revisions=%r,'
                ' self.seen=%r)' % (self._search_revisions, self.seen))

    def next(self):
        """Return the next ancestors of this revision.

        Ancestors are returned in the order they are seen in a breadth-first
        traversal.  No ancestor will be returned more than once.
        """
        if self._search_revisions is None:
            self._search_revisions = self._start
        else:
            new_search_revisions = set()
            for parents in self._parents_provider.get_parents(
                self._search_revisions):
                if parents is None:
                    continue
                new_search_revisions.update(p for p in parents if
                                            p not in self.seen)
            self._search_revisions = new_search_revisions
        if len(self._search_revisions) == 0:
            raise StopIteration()
        self.seen.update(self._search_revisions)
        return self._search_revisions

    def __iter__(self):
        return self

    def find_seen_ancestors(self, revision):
        """Find ancestors of this revision that have already been seen."""
        searcher = _BreadthFirstSearcher([revision], self._parents_provider)
        seen_ancestors = set()
        for ancestors in searcher:
            for ancestor in ancestors:
                if ancestor not in self.seen:
                    searcher.stop_searching_any([ancestor])
                else:
                    seen_ancestors.add(ancestor)
        return seen_ancestors

    def stop_searching_any(self, revisions):
        """
        Remove any of the specified revisions from the search list.

        None of the specified revisions are required to be present in the
        search list.  In this case, the call is a no-op.
        """
        stopped = self._search_revisions.intersection(revisions)
        self._search_revisions = self._search_revisions.difference(revisions)
        return stopped

    def start_searching(self, revisions):
        if self._search_revisions is None:
            self._start = set(revisions)
        else:
            self._search_revisions.update(revisions.difference(self.seen))
        self.seen.update(revisions)