Reputation: 657
I've tried to always declare class attributes inside the __init__
for clarity and organizational reasons. Recently, I've learned that strictly following this practice has extra non-aesthetic benefits too thanks to PEP 412 being added for Python 3.3. Specifically, if all attributes are defined in the __init__
, then the objects can reduce space by sharing their keys and hashes.
My question is, does object key-sharing happen when attributes are declared in a function that is called by __init__
?
Here's an example:
class Dog:
def __init__(self):
self.height = 5
self.weight = 25
class Cat:
def __init__(self):
self.set_shape()
def set_shape(self):
self.height = 2
self.weight = 10
In this case, all instances of Dog
would share the keys height
and weight
. Do instances of Cat
also share the keys height
and weight
(among each other, not with Dog
s of course).
As an aside, how would you test this?
Note that Brandon Rhodes said this about key-sharing in his Dictionary Even Mightier talk:
If a single key is added that is not in the prototypical set of keys, you loose the key sharing
Upvotes: 9
Views: 355
Reputation: 1124828
I think you are referring to the following paragraph of the PEP (in the Split-Table dictionaries section):
When resizing a split dictionary it is converted to a combined table. If resizing is as a result of storing an instance attribute, and there is only instance of a class, then the dictionary will be re-split immediately. Since most OO code will set attributes in the
__init__
method, all attributes will be set before a second instance is created and no more resizing will be necessary as all further instance dictionaries will have the correct size.
So a dictionary keys will remain shared, no matter what additions are made, before a second instance can be created. Doing so in __init__
is the most logical method of achieving this.
This does not mean that attributes set at a later time are not shared; they can still be shared between instances; so long as you don't cause any of the dictionaries to be combined. So after you create a second instance, the keys stop being shared only if any of the following happens:
a.foo = None
is set first, and then second instance b
sets b.bar = None
first, here b
has an incompatible insertion order, as the shared dictionary has foo
first.Python 3.11 improved shared-key dictionaries considerably, however. The values for the shared dictionary are inlined into an array as part of the instance, as long as there are fewer than 30 unique attributes (across all instances of a class), and deleting an attribute or inserting keys in a different order no longer affect dictionary key sharing.
So the moment you have two instances (and two dictionaries sharing keys), the keys won't be re-split as long as you don't trigger any of the above cases, your instances will continue to share keys.
It also means that delegating setting attributes to a helper method called from __init__
is not going to affect the above scenario, those attributes are still set before a second instance is created. After all __init__
won't be able to return yet before that second method has returned.
In other words, you should not worry too much about where you set your attributes. Setting them in the __init__
method lets you avoid combining scenarios more easily, but any attribute set before a second instance is created is guaranteed to be part of the shared keys.
There is no good way to detect if a dictionary is split or combined from Python, not reliably across versions. We can, however, access the C implementation details by using the ctypes
module. Given a pointer to a dictionary and the C header definition of a dictionary, you can test if the ma_values
field is NULL
. If not, it is a shared dictionary:
import ctypes
class PyObject(ctypes.Structure):
"""Python object header"""
_fields_ = [
("ob_refcnt", ctypes.c_ssize_t),
("ob_type", ctypes.c_void_p), # PyTypeObject*
]
class PyDictObject(ctypes.Structure):
"""A dictionary object."""
_fields_ = [
("ob_base", PyObject),
("ma_used", ctypes.c_ssize_t),
("ma_version_tag", ctypes.c_uint64),
("ma_keys", ctypes.c_void_p), # PyDictKeysObject*
("ma_values", ctypes.c_void_p), # PyObject** or PyDictValues*
]
Py_TPFLAGS_MANAGED_DICT = 1 << 4
def has_inlined_attributes(obj):
"""Test if an instance has inlined attributes (Python 3.11)"""
if not type(obj).__flags__ & Py_TPFLAGS_MANAGED_DICT:
return False
# the (inlined) values pointer is stored in the pre-header at offset -4
# (-3 is the dict pointer, remainder is the GC header)
return bool(ctypes.cast(id(a), ctypes.POINTER(ctypes.c_void_p))[-4])
def is_shared(d):
"""Test if the __dict__ of an instance is a PEP 412 shared dictionary"""
# Python 3.11 inlines the (shared dictionary) values as an array, unless you
# access __dict__. Don't clobber the inlined values.
if has_inlined_attributes(d):
return True
cdict = ctypes.cast(id(d.__dict__), ctypes.POINTER(PyDictObject)).contents
# if the ma_values pointer is not null, it's a shared dictionary
return bool(cdict.ma_values)
A quick demo (using Python 3.10):
>>> class Foo:
... pass
...
>>> a, b = Foo(), Foo() # two instances
>>> is_shared(a), is_shared(b) # they both share the keys
(True, True)
>>> a.bar = 'baz' # adding a single key
>>> is_shared(a), is_shared(b) # no change, the keys are still shared!
(True, True)
>>> a.spam, a.ham, a.monty, a.eric = (
... 'eggs', 'eggs and spam', 'python',
... 'idle') # more keys still
>>> is_shared(a), is_shared(b) # no change, the keys are still shared!
(True, True)
>>> a.holy, a.bunny, a.life = (
... 'grail', 'of caerbannog',
... 'of brian') # more keys, resize time
>>> is_shared(a), is_shared(b) # oops, we killed it
(False, True)
Only when the threshold was reached (for an empty dictionary with 8 spare slots, the resize takes place when you add a 6th key), did the dictionary for instance a
loose the shared property. (Later Python releases may push that resize point out further).
Dictionaries are resized when they are about 2/3rd full, and a resize generally doubles the table size. So the next resize will take place when the 11th key is added, then at 22, then 43, etc. So for a large instance dictionary, you have a lot more breathing room.
For Python 3.11, it takes a little longer still before is_shared()
returns False
; you need to insert 30 attributes:
>>> import sys, secrets
>>> sys.version_info
sys.version_info(major=3, minor=11, micro=0, releaselevel='final', serial=0)
>>> class Foo: pass
...
>>> a = Foo()
>>> count = 0
>>> while is_shared(a):
... count += 1
... setattr(a, secrets.token_urlsafe(), 42)
...
>>> count
30
Upvotes: 9
Reputation: 160667
does object key-sharing happen when attributes are declared in a function that is called by
__init__
?
Yes, regardless of where you set the attributes from, granted that after initialization both have the same set of keys, instance dictionaries use a shared-key dictionary implementation. Both cases presented have a reduced memory footprint.
You can test this by using sys.getsizeof
to grab the size of the instance dictionary and then compare it with a similar dict created from it. dict.__sizeof__
's implementation discriminates based on this to return different sizes:
# on 64bit version of Python 3.6.1
print(sys.getsizeof(vars(c)))
112
print(getsizeof(dict(vars(c))))
240
so, to find out, all you need to do is compare these.
As for your edit:
"If a single key is added that is not in the prototypical set of keys, you loose the key sharing"
Correct, this is one of the two things I've (currently) found that break the shared-key usage:
vars(inst).update
)The contents of the dictionaries of two instances of the same class deviating, this can be done by altering instance dictionaries. (single key added to that is not in the prototypical set of keys)
I'm not certain if this happens when a single key is added, this is an implementation detail that might change. (addendum: see Martijn's comments)
For a related discussion on this see a Q&A I did here: Why is the __dict__ of instances so small in Python 3?
Both these things will cause CPython to use a 'normal' dictionary instead. This, of course, is an implementation detail that shouldn't be relied upon. You might or might not find it in other implementations of Python and or future versions of CPython.
Upvotes: 7