python - OrderedDict performance (compared to deque)

Question

I've been trying to performance optimize a BFS implementation in Python and my original implementation was using deque to store the queue of nodes to expand and a dict to store the same nodes so that I would have efficient lookup to see if it is already open.

I attempted to optimize (simplicity and efficiency) by moving to an OrderedDict. However, this takes significantly more time. 400 sample searches done take 2 seconds with deque/dict and 3.5 seconds with just an OrderedDict.

My question is, if OrderedDict does the same functionality as the two original data structures, should it not at least be similar in performance? Or am I missing something here? Code examples below.

Using just an OrderedDict:

open_nodes = OrderedDict()
closed_nodes = {}
current = Node(start_position, None, 0)
open_nodes[current.position] = current

while open_nodes:
  current = open_nodes.popitem(False)[1]
  closed_nodes[current.position] = (current)

  if goal(current.position):
    return trace_path(current, open_nodes, closed_nodes)

  # Nodes bordering current
  for neighbor in self.environment.neighbors[current.position]:
    new_node = Node(neighbor, current, current.depth + 1)
    open_nodes[new_node.position] = new_node

Using both a deque and a dictionary:

open_queue = deque()
open_nodes = {}
closed_nodes = {}
current = Node(start_position, None, 0)
open_queue.append(current)
open_nodes[current.position] = current

while open_queue:
  current = open_queue.popleft()
  del open_nodes[current.position]
  closed_nodes[current.position] = (current)

  if goal_function(current.position):
    return trace_path(current, open_nodes, closed_nodes)

  # Nodes bordering current
  for neighbor in self.environment.neighbors[current.position]:
    new_node = Node(neighbor, current, current.depth + 1)
    open_queue.append(new_node)
    open_nodes[new_node.position] = new_node

Answer 1

Both deque and dict are implemented in C and will run faster than OrderedDict which is implemented in pure Python.

The advantage of the OrderedDict is that it has O(1) getitem, setitem, and delitem just like regular dicts. This means that it scales very well, despite the slower pure python implementation.

Competing implementations using deques, lists, or binary trees usually forgo fast big-Oh times in one of those categories in order to get a speed or space benefit in another category.

Update: Starting with Python 3.5, OrderedDict() now has a C implementation. And though it hasn't been highly optimized like some of the other containers. It should run much faster than the pure python implementation. Then starting with Python 3.6, regular dictionaries has been ordered (though the ordering behavior is not yet guaranteed). Those should run faster still :-)