scalable bottleneck

[MRAB]

On 2019-04-03 22:42, Sayth Renshaw wrote:
> In an email, I received this question as part of a newsletter.
> 
> def fetch_squares ( max_root ):
>      squares = []
>      for x in range ( max_root ):
>          squares . append (x **2)
>      return squares
> 
> MAX = 5
> 
> for square in fetch_squares (MAX ):
>       do_something_with ( square )
> 
> 1) Do you see a memory bottleneck here? If so, what is it?
> 2) Can you think of a way to fix the memory bottleneck?
> 
> Want to know if I am trying to solve the correct bottleneck.
> I am thinking that the bottleneck is to create a list only to iterate the same list you created sort of doubling the time through.
> 
> Is that the correct problem to solve?
> 
> If it is then I thought the best way is just to supply the numbers on the fly, a generator.
> 
> def supply_squares(max_root):
>      for x in max_root:
>          yield x
> 
> MAX = 5
> 
> 
> So then I set up a loop and do whatever is needed. At this time I am creating generator objects. But is this the correct way to go? More of a am I thinking correctly questino.
> 
> item = 0
> while item < MAX:
>      print(supply_squares(item))
>      item += 1
>      
> <generator object supply_squares at 0x0000000004DEAC00>
> <generator object supply_squares at 0x0000000004DEAC00>
> <generator object supply_squares at 0x0000000004DEAC00>
> <generator object supply_squares at 0x0000000004DEAC00>
> <generator object supply_squares at 0x0000000004DEAC00>
> 
You should create a single generator that will yield the squares. The 
'for' loop should remain the same.