Today I learned not to cache generators.

Do not cache generators

I was optimising all images on my blog and I noticed my script was not working correctly. It was skipping some images...

That is when I realised I was caching some generators and I shouldn't!

Here is a simplified demonstration of the issue:

from functools import lru_cache

@lru_cache
def squares(n):
    return (num ** 2 for num in range(n))

for square in squares(3):
    print(square)

for square in squares(3):
    print(square)

What's the output of this code? I expected it to be

0
1
4
0
1
4

But it's actually

0
1
4

Why is that? Because the lru_cache cached the generator and the generator is an iterator. As soon as you go over it once, it is "depleted" and you can't iterate over it again!

Here is another demonstration of the issue:

from functools import lru_cache

@lru_cache
def squares(n):
    return (num ** 2 for num in range(n))

squares_to_3 = squares(3)
for square in squares_to_3:
    print(square)

new_squares_to_3 = squares(3)
print(new_squares_to_3 is squares_to_3)  # True, they are the SAME object.

# Hence, "new" squares_to_3 has actually been traversed:
print(next(new_squares_to_3, None))  # None, there is no next element.

That's it for now! Stay tuned and I'll see you around!

Thanks for reading ❤️

I hope you learned something new! If you did, consider following the footsteps of the readers who bought me a slice of pizza 🍕. Your contribution boosts my confidence and helps me produce this content for you.