[Python-Dev] Investigating Python memory footprint of one real Web application

[INADA Naoki]

More detailed information:

## With annotations

=== tracemalloc stat ===
traced: (46969277, 46983753)
18,048,888 / 181112
  File "<frozen importlib._bootstrap_external>", line 488
  File "<frozen importlib._bootstrap_external>", line 780
  File "<frozen importlib._bootstrap_external>", line 675

=== size by types ===
dict                     9,083,816 (8,870.91KB) / 21846 = 415.811bytes (21.38%)
tuple                    6,420,960 (6,270.47KB) / 86781 = 73.990bytes (15.11%)
str                      6,050,213 (5,908.41KB) / 77527 = 78.040bytes (14.24%)
function                 2,772,224 (2,707.25KB) / 20384 = 136.000bytes (6.53%)
code                     2,744,888 (2,680.55KB) / 18987 = 144.567bytes (6.46%)
type                     2,713,552 (2,649.95KB) / 2769 = 979.975bytes (6.39%)
bytes                    2,650,838 (2,588.71KB) / 38723 = 68.456bytes (6.24%)
set                      2,445,280 (2,387.97KB) / 6969 = 350.880bytes (5.76%)
weakref                  1,255,600 (1,226.17KB) / 15695 = 80.000bytes (2.96%)
list                       707,336 (690.76KB) / 6628 = 106.719bytes (1.66%)

=== dict stat ===
t,         size,        total (%) / count
3,          256,    1,479,424 (15.68%) / 5779.0
3,        1,200,    1,330,800 (14.11%) / 1109.0
3,    1,310,832,    1,310,832 (13.90%) / 1.0
3,          664,    1,287,496 (13.65%) / 1939.0
7,          128,      756,352 (8.02%) / 5909.0
3,          384,      707,328 (7.50%) / 1842.0
3,        2,296,      642,880 (6.81%) / 280.0
0,          256,      378,112 (4.01%) / 1477.0
7,          168,      251,832 (2.67%) / 1499.0
3,        4,720,      221,840 (2.35%) / 47.0
3,        9,336,      130,704 (1.39%) / 14.0
7,           88,      105,072 (1.11%) / 1194.0

* t=7 key-sharing dict, t=3 interned string key only, t=1 string key
only, t=0 non string key is used


## Stripped annotations

=== tracemalloc stat ===
traced: (42383739, 42397983)

18,069,806 / 181346
  File "<frozen importlib._bootstrap_external>", line 488
  File "<frozen importlib._bootstrap_external>", line 780
  File "<frozen importlib._bootstrap_external>", line 675

=== size by types ===
dict                     7,913,144 (7,727.68KB) / 17598 = 449.662bytes (20.62%)
tuple                    6,149,120 (6,005.00KB) / 82734 = 74.324bytes (16.02%)
str                      6,070,083 (5,927.82KB) / 77741 = 78.081bytes (15.82%)
code                     2,744,312 (2,679.99KB) / 18983 = 144.567bytes (7.15%)
type                     2,713,552 (2,649.95KB) / 2769 = 979.975bytes (7.07%)
bytes                    2,650,464 (2,588.34KB) / 38715 = 68.461bytes (6.91%)
function                 2,547,280 (2,487.58KB) / 18730 = 136.000bytes (6.64%)
set                      1,423,520 (1,390.16KB) / 4627 = 307.655bytes (3.71%)
list                       634,472 (619.60KB) / 5454 = 116.331bytes (1.65%)
int                        608,784 (594.52KB) / 21021 = 28.961bytes (1.59%)

=== dict stat ===
t,         size,        total (%) / count
3,        1,200,    1,316,400 (16.06%) / 1097.0
3,    1,310,832,    1,310,832 (16.00%) / 1.0
3,          664,      942,216 (11.50%) / 1419.0
3,          256,      861,184 (10.51%) / 3364.0
3,          384,      657,024 (8.02%) / 1711.0
3,        2,296,      640,584 (7.82%) / 279.0
7,          128,      606,464 (7.40%) / 4738.0
0,          256,      379,904 (4.64%) / 1484.0
7,          168,      251,832 (3.07%) / 1499.0
3,        4,720,      221,840 (2.71%) / 47.0
3,        9,336,      130,704 (1.59%) / 14.0
7,           88,      105,248 (1.28%) / 1196.0
7,          256,       86,784 (1.06%) / 339.0


## Stripped annotation + without pydebug

=== tracemalloc stat ===
traced: (37371660, 40814265)
9,812,083 / 111082
  File "<frozen importlib._bootstrap>", line 205
  File "<frozen importlib._bootstrap_external>", line 742
  File "<frozen importlib._bootstrap_external>", line 782
6,761,207 / 85614
  File "<frozen importlib._bootstrap_external>", line 488
  File "<frozen importlib._bootstrap_external>", line 780
  File "<frozen importlib._bootstrap_external>", line 675


## Ideas about memory optimize

a) Split PyGC_Head from object

Reduces 2words (16byte) from each tuples.

>>> 82734 * 16 / 1024
1292.71875

So estimated -1.2MB


b) concat co_consts, co_names, co_varnames, co_freevars into one
tuple, or embed them into code.

Each tuple has 3 (gc head) + 3 (refcnt, *type, length) = 6 words
overhead. (or 4 words if (a) is applied)
If we can reduce 3 tuples, 18 words = 144byte (or 12 words=96byte) can
be reuduced.

>>> 18983 * 144
2733552
>>> 18983 * 96
1822368

But co_freevars is empty tuple in most cases.  So real effect is
smaller than 2.7MB.
If we can embed them into code object, we can estimate -2.7MB.

(There are co_cellvars too. But I don't know about it much, especially
it is GC tracked or not)


c) (interned) string key only dict.

20% of memory is used for dict, and 70% of dict has interned string keys.
Current DictKeyEntry is 3 words: {key, hash, value}.
But if we can assume all key is string, hash can be get from the key.

If we use 2 words entry: {key, value} for such dict, I think dict can
be 25% smaller.

>>> 7913144 * 0.25 / 1024
1931.919921875

So I estimate -1.9MB

If we can implement (a)~(c) I estimate memory usage on Python
(--without-pydebug)
can be reduced from 35.6MB to 30MB, roughly.

But I think -Onoannotation option is top priority.  It can reduce 4MB,
even we use
annotations only in our code.
If major libraries start using annotations, this number will be larger.