1/ #Python tip: The functools.cache() decorator is astonishingly fast.
Even an empty function that returns None can be sped-up by caching it. 🤨
docs.python.org/3/library/func…
Even an empty function that returns None can be sped-up by caching it. 🤨
docs.python.org/3/library/func…
2/ Here are the timings:
$ python3.9 -m timeit -r11 -s 'def s(x):pass' 's(5)'
5000000 loops, best of 11: 72.1 nsec per loop
$ python3.9 -m timeit -r11 -s 'from functools import cache' -s 'def s(x):pass' -s 'c=cache(s)' 'c(5)'
5000000 loops, best of 11: 60.6 nsec per loop
$ python3.9 -m timeit -r11 -s 'def s(x):pass' 's(5)'
5000000 loops, best of 11: 72.1 nsec per loop
$ python3.9 -m timeit -r11 -s 'from functools import cache' -s 'def s(x):pass' -s 'c=cache(s)' 'c(5)'
5000000 loops, best of 11: 60.6 nsec per loop
3/ Behind the scenes, there is a single dictionary lookup and a counter increment.
Some kinds arguments will take longer to hash, but you get the idea, @cache has very little overhead.
Some kinds arguments will take longer to hash, but you get the idea, @cache has very little overhead.
4/ It's implemented with fast C code, equivalent to:
key = make_key(args, kwds, typed)
result = cache_get(key, sentinel)
if result is not sentinel:
hits += 1
return result
misses += 1
result = user_function(*args, **kwds)
cache[key] = result
return result
key = make_key(args, kwds, typed)
result = cache_get(key, sentinel)
if result is not sentinel:
hits += 1
return result
misses += 1
result = user_function(*args, **kwds)
cache[key] = result
return result
5/ The @cache decorator is new in #Python version 3.9.
Prior to that, you can get the same effect with an lru_cache where the maxsize is set to None:
@lru_cache(maxsize=None)
def f(x):
...
So, really @cache is just a pretty way to spell it.
Prior to that, you can get the same effect with an lru_cache where the maxsize is set to None:
@lru_cache(maxsize=None)
def f(x):
...
So, really @cache is just a pretty way to spell it.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
