Greets,
Sorry for my late answer, google groups lost my post...
First, thanks you for your explanations about memory handling in the
os and python.
I've tried with python 2.5 under linux :
For the parsing of a 66 Mb xml file with cElementTree :
When starting python : 2.1 Mb private memory used
import xml.etree.cElementTree as ElementTree #3.4 Mb used
et=ElementTree.parse('otherdata.xml') #218.6 Mb used
del et #43.3 Mb used
et=ElementTree.parse('otherdata.xml') #218.6 Mb used
del et #60.6 Mb used
et=ElementTree.parse('otherdata.xml') #218.6 Mb used
del et #54.1 Mb used
et=ElementTree.parse('otherdata.xml') #218.6 Mb used
del et #54.1 Mb used
et=ElementTree.parse('otherdata.xml') #218.6 Mb used
del et #54.1 Mb used
Why does I have a such erratic memory freeing ?
I've tried the same test many time with a new interpreter and I've got
43.3 Mb after the first free and 54.1 Mb after the others.
If there is a memory pool limit in list ans dict, why can't I goes
back to 43.3 or 54.1 Mb all the times ?
I've tried using readlines():
When starting python : 2.1 Mb private memory used
f=open('otherdata.xml') #2.2 Mb used
data=f.readlines() #113 Mb used
del data #2.7 Mb used
f.seek(0) #2.7 Mb used
data=f.readlines() #113 Mb used
del data #2.7 Mb used
That time I have a good memory handling (for my definition of memory
handling)
So is there a problem with cElementTree ?
I've done a last test with ElementTree :
When starting python : 2.1 Mb private memory used
import xml.etree.ElementTree as ElementTree #3.2 Mb used
et=ElementTree.parse('otherdata.xml') #211.4 Mb used (but very
slow :p)
del et #21.4 Mb used
et=ElementTree.parse('otherdata.xml') #211.4 Mb used
del et #29.8 Mb used
So why does I have such differences in memory freeing ? Only due to
fragmentation ?
Anyway, python 2.5 has a better memory handling than 2.4, but still
not perfect for me.
I think I've not really understood the problem with the use of malloc
(fragmentation,...)
Thanks for your help
Regards,
FP
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