Optimizing list processing

[duncan smith]

On 11/12/13 23:54, Steven D'Aprano wrote:
> I have some code which produces a list from an iterable using at least
> one temporary list, using a Decorate-Sort-Undecorate idiom. The algorithm
> looks something like this (simplified):
>
> table = sorted([(x, i) for i,x in enumerate(iterable)])
> table = [i for x,i in table]
>
> The problem here is that for large iterables, say 10 million items or so,
> this is *painfully* slow, as my system has to page memory like mad to fit
> two large lists into memory at once. So I came up with an in-place
> version that saves (approximately) two-thirds of the memory needed.
>
> table = [(x, i) for i,x in enumerate(iterable)]
> table.sort()
> for x, i in table:
>      table[i] = x
>
> For giant iterables (ten million items), this version is a big
> improvement, about three times faster than the list comp version. Since
> we're talking about the difference between 4 seconds and 12 seconds (plus
> an additional 40-80 seconds of general slow-down as the computer pages
> memory into and out of virtual memory), this is a good, solid
> optimization.
>
> Except that for more reasonably sized iterables, it's a pessimization.
> With one million items, the ratio is the other way around: the list comp
> version is 2-3 times faster than the in-place version. For smaller lists,
> the ratio varies, but the list comp version is typically around twice as
> fast. A good example of trading memory for time.
>
> So, ideally I'd like to write my code like this:
>
>
> table = [(x, i) for i,x in enumerate(iterable)]
> table.sort()
> if len(table) < ?????:
>      table = [i for x,i in table]
> else:
>      for x, i in table:
>          table[i] = x
>
> where ????? no doubt will depend on how much memory is available in one
> contiguous chunk.
>
> Is there any way to determine which branch I should run, apart from hard-
> coding some arbitrary and constant cut-off value?
>

I had a slightly similar problem a while ago. I actually wanted to 
process data from a large file in sorted order. In the end I read chunks 
of data from the file, sorted them, then wrote each chunk of data to a 
temporary file. Then I used heapq.merge to merge the data in the 
temporary files. It vastly reduced memory consumption, and was 'quick 
enough'. It was based on Guido's solution for sorting a million 32-bit 
integers in 2MB of RAM 
(http://neopythonic.blogspot.co.uk/2008/10/sorting-million-32-bit-integers-in-2mb.html). 
Cheers.

Duncan