Is `namedtuple` Really As Efficient In Memory Usage As Tuples? My Test Says NO

It is stated in the Python documentation that one of the advantages of namedtuple is that it is as memory-efficient as tuples. To validate this, I used iPython with ipython_memory

Solution 1:

A simpler metric is to check the size of equivalent tuple and namedtuple objects. Given two roughly analogous objects:

from collections import namedtuple
import sys

point = namedtuple('point', 'x y z')
point1 = point(1, 2, 3)

point2 = (1, 2, 3)

Get the size of them in memory:

>>> sys.getsizeof(point1)
72

>>> sys.getsizeof(point2)
72

They look the same to me...

---

Taking this a step further to replicate your results, notice that if you create a list of identical tuples the way you're doing it, each tuple is the exact same object:

>>> test_list = [(1,2,3) for _ in range(10000000)]
>>> test_list[0] is test_list[-1]
True

So in your list of tuples, each index contains a reference the same object. There are not 10000000 tuples, there are 10000000 references to one tuple.

On the other hand, your list of namedtuple objects actually does create 10000000 unique objects.

A better apples-to-apples comparison would be to view the memory usage for

>>> test_list = [(i, i+1, i+2) for i in range(10000000)]

and:

>>> test_list_n = [point(x=i, y=i+1, z=i+2) for i in range(10000000)]

They have the same size:

>>> sys.getsizeof(test_list)
81528056

>>> sys.getsizeof(test_list_n)
81528056

Solution 2:

Doing some investigation myself (with Python 3.6.6). I run into following conclusions:

1. In all three cases (list of tuples, list of named tuples, list of dicts). sys.getsizeof returns size of the list, which stores only references anyway. So you get size: 81528056 in all three cases.

2. Sizes of elementary types are:

   sys.getsizeof((1,2,3)) 72

   sys.getsizeof(point(x=1, y=2, z=3)) 72

   sys.getsizeof(dict(x=1, y=2, z=3)) 240

3. timing is very bad for named tuple:
   list of tuples: 1.8s
   list of named tuples: 10s
   list of dicts: 4.6s

4. Looking to system load I become suspicious about results from getsizeof. After measuring the footprint of the Ptyhon3 process I get:

   test_list = [(i, i+1, i+2) for i in range(10000000)]
   increase by: 1 745 564K
   that is about 175B per element

   test_list_n = [point(x=i, y=i+1, z=i+2) for i in range(10000000)]
   increase by: 1 830 740K
   that is about 183B per element

   test_list_n = [point(x=i, y=i+1, z=i+2) for i in range(10000000)]
   increase by: 2 717 492 K
   that is about 272B per element