INTPYTHON-617 - Improve DictField to_python performance

[NoahStapp]

Improves performance of large documents with nested DictFields by nearly 20x, addressing #1230.

Modified benchmark script pulled from https://stackoverflow.com/questions/35257305/mongoengine-is-very-slow-on-large-documents-compared-to-native-pymongo-usage/:

import datetime
import itertools
import random
import timeit
from collections import defaultdict

import mongoengine as db

db.connect("test-dicts")

class MyModel(db.Document):
    date = db.DateTimeField(required=True, default=datetime.date.today)
    data_dict_1 = db.DictField(required=False)

MyModel.drop_collection()

data_1 = ['foo', 'bar']
data_2 = ['spam', 'eggs', 'ham']
data_3 = ["subf{}".format(f) for f in range(5)]

m = MyModel()
tree = lambda: defaultdict(tree)
data = tree()
for _d1, _d2, _d3 in itertools.product(data_1, data_2, data_3):
    data[_d1][_d2][_d3] = list(random.sample(range(50000), 20000))
m.data_dict_1 = data
m.save()

def pymongo_doc():
    return db.connection.get_connection()["test-dicts"]['my_model'].find_one()

def mongoengine_doc():
    model = MyModel.objects.first()
    return model

if __name__ == '__main__':
    print("pymongo took {:2.2f}s".format(timeit.timeit(pymongo_doc, number=10)))
    print("mongoengine took {:2.2f}s".format(timeit.timeit(mongoengine_doc, number=10)))

Before:

pymongo took 0.21s
mongoengine took 4.98s

After:

pymongo took 0.20s
mongoengine took 0.20s

[terencehonles]

Does your benchmark still apply? You seem to have changed the code substantially, and I would assume that the to_python caching you do at the top level not to be that impactful (but maybe it is). If this is really the case, then it may make sense to move your change down into the base class so this applies to lists too.