Hi all
I was wondering about the performance comparison of either using a
dictionary or an object for a large collection of "things". Therefore
I have run the attached test. I create a dictionary and an object.
Both get the same number of items/attributes, respectively. Then, for
both the values are read often (iterations).
Here are the results:
attributes 100
iterations 1000000
dic 25.515999794
obj 138.570000172
Is the test meaningful and are you surprised by the results? I am,
actually, because I would have assumed that attribute access with an
object should be faster because lookup can be precompiled.
kind regards
Andre
import time
import random
ITERATIONS = 1000000
ATTRIBUTES = 100
class O(object):
pass
class Test(object):
def __init__(self):
self.attributes = []
self.dic = {}
self.obj = O()
for i in range(ATTRIBUTES):
value = ""
for s in range(12):
value = value + chr(random.randint(ord('a'), ord('z')))
# print value
attr = ""
for s in range(6):
attr = attr + chr(random.randint(ord('a'), ord('z')))
# print attr
self.attributes.append(attr)
self.dic[attr] = value
self.obj.__setattr__(attr, value)
print "attributes", len(self.attributes)
def run_dic(self):
t0 = time.time()
for i in range(ITERATIONS):
for a in self.attributes:
v = self.dic[a]
# print i, v
t1 = time.time()
return t1 - t0
def run_obj(self):
t0 = time.time()
for i in range(ITERATIONS):
for a in self.attributes:
v = self.obj.__getattribute__(a)
# print i, v
t1 = time.time()
return t1 - t0
print "iterations", ITERATIONS
t = Test()
td = t.run_dic()
to = t.run_obj()
print "dic", td
print "obj", to
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