from itertools import product
from collections import defaultdict, namedtuple
class MigrationGraph(object):
def __init__(self, migrations):
self._build_graph(migrations)
def _build_graph(self, migrations):
'''Build a graph where the nodes are possible migration states and the
edges are transitions between states allowed by migrations.
'''
# Generate all states referenced by the given migrations. Also index
# nodes by state.
versions = defaultdict(lambda: [-1])
for mod,version in migrations:
versions[mod].append(version)
self._State = namedtuple('State', versions)
self._modules = versions.keys()
self._nodes = [ Node(self._State(*ver)) for ver in product(*versions.values()) ]
self.node_by_state = dict((n.state, n) for n in self._nodes)
# Index the nodes by (mod,version)
self._index = defaultdict(list)
for n in self._nodes:
for m in self._modules:
v = getattr(n.state, m)
self._index[m,v].append(n)
# Add edges for all the migrations
for m in migrations.itervalues():
for direction in 'up', 'down':
ms = MigrateStep(self, m, direction)
for prev, next in ms.transitions():
prev.succs.append((next, ms))
def nodes_with(self, requirements):
'''Return list of nodes that match the requirements listed in
requirements, which is either a dict or list of (mod,version) pairs.'''
if isinstance(requirements, dict):
requirements = requirements.iteritems()
nodes = None
for (mod, ver) in requirements:
if nodes is None: nodes = set(self._index[mod,ver])
else: nodes &= set(self._index[mod,ver])
return nodes
def shortest_path(self, start_requirements, end_requirements):
'''Dijkstra's algorithm for shortest path from the start Node to any end
Node'''
# Find the start node
start = dict((m, -1) for m in self._modules)
start.update(start_requirements)
start = dict((str(k), v) for k,v in start.iteritems()
if k in self._State._fields)
start_state = self._State(**start)
start = self.node_by_state[start_state]
# Find the end node(s)
end = self.nodes_with(end_requirements)
# Run the algorithm
start.distance = 0
nodes = priority_dict(
(node, node.distance)
for node in self._nodes)
while nodes:
cur = nodes.pop_smallest()
if cur.distance is None: # pragma no cover
raise ValueError, 'No migration path exists from %s to %s' % (
start, end)
if cur in end:
return list(cur.path())
cur.visit(nodes)
def as_dot(self): # pragma no cover
yield 'digraph G {'
for n in self._nodes:
yield 'node_%d[label="%r"];' % (id(n), n.state)
for n in self._nodes:
for (next, ms) in n.succs:
yield 'node_%d->node_%d[label="%r"];' % (id(n), id(next), ms)
yield '}'
class MigrateStep(object):
'''Object representing a single migration step in a single direction (either
up or down'''
def __init__(self, graph, migration, direction):
self.graph = graph
self.migration = migration
self.direction = direction
def transitions(self):
'''Returns all node->node transitions made possible by this migratestep'''
if self.direction == 'up':
reqs = self.migration.up_requires()
postcondition = self.migration.up_postcondition()
else:
reqs = self.migration.down_requires()
postcondition = self.migration.down_postcondition()
for prev in self.graph.nodes_with(reqs):
next_state = prev.state._replace(**postcondition)
next = self.graph.node_by_state[next_state]
yield prev, next
def apply(self, state):
'''Actually run the migration, updating the state passed in'''
if self.direction == 'up':
self.migration.up()
state.update(self.migration.up_postcondition())
else:
self.migration.down()
state.update(self.migration.down_postcondition())
def __repr__(self): # pragma no cover
return '<%s.%s %s>' % (
self.migration.module,
self.migration.version,
self.direction)
class Node(object):
def __init__(self, state):
self.state = state
self.visited = False
self.distance = 1e9 # effectively inf
self.pred = None # (state, migrationstep)
self.succs = [] # list of (state, migrationstep)
def visit(self, nodes):
'''The 'visit' step of Dijkstra's shortest-path algorithm'''
self.visited = True
new_dist = self.distance + 1
for succ, ms in self.succs:
if succ.visited: continue
if new_dist < succ.distance:
succ.distance = new_dist
succ.pred = (self, ms)
nodes[succ] = new_dist
def path(self):
'''Read back the shortest path from the 'predecessor' field'''
if self.pred:
for p in self.pred[0].path():
yield p
yield self.pred[1]
def __repr__(self): # pragma no cover
return '<Node %r (%s)>' % (self.state,self.distance)
# priority dictionary recipe copied from
# http://code.activestate.com/recipes/522995-priority-dict-a-priority-queue-with-updatable-prio/
# We use this rather than the raw heap because the priority_dict allows us to
# update the priority of a node, which heapq does not (natively) allow without
# re-running heapify() each time a priority changes. (And priorities change
# often in Dijkstra's algorithm.)
from heapq import heapify, heappush, heappop
class priority_dict(dict):
"""Dictionary that can be used as a priority queue.
Keys of the dictionary are items to be put into the queue, and values
are their respective priorities. All dictionary methods work as expected.
The advantage over a standard heapq-based priority queue is
that priorities of items can be efficiently updated (amortized O(1))
using code as 'thedict[item] = new_priority.'
The 'smallest' method can be used to return the object with lowest
priority, and 'pop_smallest' also removes it.
The 'sorted_iter' method provides a destructive sorted iterator.
"""
def __init__(self, *args, **kwargs):
super(priority_dict, self).__init__(*args, **kwargs)
self._rebuild_heap()
def _rebuild_heap(self):
self._heap = [(v, k) for k, v in self.iteritems()]
heapify(self._heap)
def smallest(self):
"""Return the item with the lowest priority.
Raises IndexError if the object is empty.
"""
heap = self._heap
v, k = heap[0]
while k not in self or self[k] != v:
heappop(heap)
v, k = heap[0]
return k
def pop_smallest(self):
"""Return the item with the lowest priority and remove it.
Raises IndexError if the object is empty.
"""
heap = self._heap
v, k = heappop(heap)
while k not in self or self[k] != v:
v, k = heappop(heap)
del self[k]
return k
def __setitem__(self, key, val):
# We are not going to remove the previous value from the heap,
# since this would have a cost O(n).
super(priority_dict, self).__setitem__(key, val)
if len(self._heap) < 2 * len(self):
heappush(self._heap, (val, key))
else:
# When the heap grows larger than 2 * len(self), we rebuild it
# from scratch to avoid wasting too much memory.
self._rebuild_heap()
def setdefault(self, key, val):
if key not in self:
self[key] = val
return val
return self[key]
def update(self, *args, **kwargs):
# Reimplementing dict.update is tricky -- see e.g.
# http://mail.python.org/pipermail/python-ideas/2007-May/000744.html
# We just rebuild the heap from scratch after passing to super.
super(priority_dict, self).update(*args, **kwargs)
self._rebuild_heap()
def sorted_iter(self):
"""Sorted iterator of the priority dictionary items.
Beware: this will destroy elements as they are returned.
"""
while self:
yield self.pop_smallest()
# End recipe