python reading file memory cost

Thomas Jollans t at
Tue Aug 2 13:09:57 CEST 2011

On 02/08/11 13:00, 张彤 wrote:
> Thanks Peter! Your explanation is great!
> And one more question:
> Why it is still keeping the memory even when I del the large array in
> interactive python mode?

This is an optimisation of the way the Python interpreter allocates
memory: it holds on to memory it's not using any more for a while so it
can be easily re-used for new objects --- this is more efficient than
giving the memory back to the operating system only to request it again
shortly afterwards.

> -----Original Message-----
> From: Peter Otten [mailto:__peter__ at] 
> Sent: Tuesday, August 02, 2011 4:26 PM
> To: python-list at
> Subject: Re: python reading file memory cost
> Chris Rebert wrote:
>>> The running result was that read a 500M file consume almost 2GB RAM, 
>>> I cannot figure it out, somebody help!
>> If you could store the floats themselves, rather than their string 
>> representations, that would be more space-efficient. You could then 
>> also use the `array` module, which is more space-efficient than lists 
>> ( ). Numpy would also be 
>> worth investigating since multidimensional arrays are involved.
>> The next obvious question would then be: do you /really/ need /all/ of 
>> the data in memory at once?
> This is what you (OP) should think about really hard before resorting to the
> optimizations mentioned above. Perhaps you can explain what you are doing
> with the data once you've loaded it into memory?
>> Also, just so you're aware:
> To give you an idea how memory usage explodes:
>>>> line = "1.23 4.56 7.89 0.12\n"
>>>> len(line) # size in the file
> 20
>>>> sys.getsizeof(line)
> 60
>>>> formatted = ["%2.6E" % float(x) for x in line.split()]
>>>> sys.getsizeof(formatted) + sum(sys.getsizeof(s) for s in formatted)
> 312

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