问题
My question is are the following two pieces of code run the same by the interpreter:
class A(object):
def __init__(self):
self.__x = None
@property
def x(self):
if not self.__x:
self.__x = ... #some complicated action
return self.__x
and the much simpler:
class A(object):
@property
def x(self):
return ... #some complicated action
I.e., is the interpreter smart enough to cache the property x?
My assumption is that x does not change - finding it is hard, but once you find it once there is no reason to find it again.
回答1:
No, the getter will be called every time you access the property.
回答2:
No you need to add a memoize decorator:
class memoized(object):
"""Decorator that caches a function's return value each time it is called.
If called later with the same arguments, the cached value is returned, and
not re-evaluated.
"""
def __init__(self, func):
self.func = func
self.cache = {}
def __call__(self, *args):
try:
return self.cache[args]
except KeyError:
value = self.func(*args)
self.cache[args] = value
return value
except TypeError:
# uncachable -- for instance, passing a list as an argument.
# Better to not cache than to blow up entirely.
return self.func(*args)
def __repr__(self):
"""Return the function's docstring."""
return self.func.__doc__
def __get__(self, obj, objtype):
"""Support instance methods."""
return functools.partial(self.__call__, obj)
@memoized
def fibonacci(n):
"Return the nth fibonacci number."
if n in (0, 1):
return n
return fibonacci(n-1) + fibonacci(n-2)
print fibonacci(12)
回答3:
Properties do not automatically cache their return values. The getter (and setters) are intended to be called each time the property is accessed.
However, Denis Otkidach has written a wonderful cached attribute decorator (published in the Python Cookbook, 2nd edition and also originally on ActiveState under the PSF license) for just this purpose:
class cache(object):
'''Computes attribute value and caches it in the instance.
Python Cookbook (Denis Otkidach) https://stackoverflow.com/users/168352/denis-otkidach
This decorator allows you to create a property which can be computed once and
accessed many times. Sort of like memoization.
'''
def __init__(self, method, name=None):
# record the unbound-method and the name
self.method = method
self.name = name or method.__name__
self.__doc__ = method.__doc__
def __get__(self, inst, cls):
# self: <__main__.cache object at 0xb781340c>
# inst: <__main__.Foo object at 0xb781348c>
# cls: <class '__main__.Foo'>
if inst is None:
# instance attribute accessed on class, return self
# You get here if you write `Foo.bar`
return self
# compute, cache and return the instance's attribute value
result = self.method(inst)
# setattr redefines the instance's attribute so this doesn't get called again
setattr(inst, self.name, result)
return result
Here is an example demonstrating its use:
def demo_cache():
class Foo(object):
@cache
def bar(self):
print 'Calculating self.bar'
return 42
foo=Foo()
print(foo.bar)
# Calculating self.bar
# 42
print(foo.bar)
# 42
foo.bar=1
print(foo.bar)
# 1
print(Foo.bar)
# __get__ called with inst = None
# <__main__.cache object at 0xb7709b4c>
# Deleting `foo.bar` from `foo.__dict__` re-exposes the property defined in `Foo`.
# Thus, calling `foo.bar` again recalculates the value again.
del foo.bar
print(foo.bar)
# Calculating self.bar
# 42
demo_cache()
回答4:
Python 3.2 onwards offers a built-in decorator that you can use to create a LRU cache:
@functools.lru_cache(maxsize=128, typed=False)
Alternatively, if you're using Flask / Werkzeug, there's the @cached_property decorator.
For Django, try from django.utils.functional import cached_property
回答5:
I've had to look it up, since I had this same question.
The functools package from the standard library will be getting a cached_property decorator as well. Unfortunately, it's only available from Python 3.8 (as of time of this post, it's 3.8a0). The alternative to waiting is to use a custom one, such as this one as mentioned by 0xc0de) or Django's, for now, then switch later:
from django.utils.functional import cached_property
# from functools import cached_property # Only 3.8+ :(
回答6:
The decorator from Denis Otkidach mentioned in @unutbu's answer was published in O'Reilly's Python Cookbook. Unfortunately O'Reilly doesn't specify any license for code examples – just as informal permission to reuse the code.
If you need a cached property decorator with a liberal license, you can use Ken Seehof's @cached_property from ActiveState code recipes. It's explicitly published under the MIT license.
def cached_property(f):
"""returns a cached property that is calculated by function f"""
def get(self):
try:
return self._property_cache[f]
except AttributeError:
self._property_cache = {}
x = self._property_cache[f] = f(self)
return x
except KeyError:
x = self._property_cache[f] = f(self)
return x
return property(get)
回答7:
To anyone who might be reading this in 2020, this functionality is now available in the funcutils module as part of the standard library as of Python 3.8.
https://docs.python.org/dev/library/functools.html#functools.cached_property
Important to note, classes that define their own __dict__ (or do not define one at all) or use __slots__ might not work as expected. For example, NamedTuple and metaclasses.
回答8:
Note: Adding for the sake of completeness of available options.
No, property is not cached by default. However there are several options to get that behaviour, I would like to add one more to that:
https://github.com/pydanny/cached-property
来源：https://stackoverflow.com/questions/6428723/python-are-property-fields-being-cached-automatically