[Python-Dev] io.BytesIO slower than monkey-patching io.RawIOBase (original) (raw)

Serhiy Storchaka storchaka at gmail.com
Tue Jul 17 13:59:20 CEST 2012

On 17.07.12 06:34, Eli Bendersky wrote:

The second approach is consistently 10-20% faster than the first one (depending on input) for trunk Python 3.3

Is there any reason for this to be so? What does BytesIO give us that the second approach does not (I tried adding more methods to the patched RawIOBase to make it more functional, like seekable() and tell(), and it doesn't affect performance)?

BytesIO resizes underlying buffer if it overflowed (overallocating 1/8 of size and copying old content to new buffer). Total it makes log9/8 allocations and copy 8*N bytes (for large N). List uses the same strategy, but number of chunks usually significantly less than number of bytes. At the end all this chunks concatenated by join, which calculates sum of chunk lengths and allocate the resulting array with the desired size. That is why append/join is faster than BytesIO in this case.

There are other note, about ElementTree.tostringlist(). Creating DataStream class in every function call is too expensive, and that is why "monkeypatched" version several times is faster than DataStream version for small data. But for long data it is faster too, because data.append() is on one lookup slower than "monkeypatched" write=data.append.

This also raises a "moral" question - should I be using the second approach deep inside the stdlib (ET.tostring) just because it's faster?

Please note that the previous version of Python used a "monkeypatching".

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