memory dump using Python
I have small program written for me in Python to help me generate all combinations of passwords from a different sets of numbers and words i know for me to recover a password i forgot, as i know all different words and sets of numbers i used i just wanted to generate all possible combinations, the only problem is that the list seems to go on for hours and hours so eventually i run out of memory and it doesn't finish.
I got told it needs to dump my memory so it can carry on but i'm not sure if this is right. is there any way i can get round this problem?
this is the program i am running:
#!/usr/bin/python
import itertools
gfname = "name"
tendig = "1234567890"
sixteendig = "1111111111111111"
housenum = "99"
Characterset1 = "&&&&"
Characterset2 = "££££"
daughternam = "dname"
daughtyear = "1900"
phonenum1 = "055522233"
phonenum2 = "3333333"
mylist = [gfname, tendig, sixteendig, housenum, Characterset1,
Characterset2, daughternam, daughtyear, phonenum1, phonenum2]
for length in range(1, len(mylist)+1):
for item in itertools.permutations(mylist, length):
print "".join(item)
i have taken out a few sets and changed the numbers and word for obvious reasons but this is roughly the program.
another thing is i may be missing a particular word but didnt want to put it in the list because i know it might go before all the generated passwords, does anyone know how to add a prefix to my program.
sorry for the bad grammar and thanks for any help given.
Answer
I used guppy to understand the memory usage, I changed the OP code slightly (marked #!!!)
import itertools
gfname = "name"
tendig = "1234567890"
sixteendig = "1111111111111111"
housenum = "99"
Characterset1 = "&&&&"
Characterset2 = u"££££"
daughternam = "dname"
daughtyear = "1900"
phonenum1 = "055522233"
phonenum2 = "3333333"
from guppy import hpy # !!!
h=hpy() # !!!
mylist = [gfname, tendig, sixteendig, housenum, Characterset1,
Characterset2, daughternam, daughtyear, phonenum1, phonenum2]
for length in range(1, len(mylist)+1):
print h.heap() #!!!
for item in itertools.permutations(mylist, length):
print item # !!!
Guppy outputs something like this every time h.heap() is called.
Partition of a set of 25914 objects. Total size = 3370200 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 11748 45 985544 29 985544 29 str
1 5858 23 472376 14 1457920 43 tuple
2 323 1 253640 8 1711560 51 dict (no owner)
3 67 0 213064 6 1924624 57 dict of module
4 199 1 210856 6 2135480 63 dict of type
5 1630 6 208640 6 2344120 70 types.CodeType
6 1593 6 191160 6 2535280 75 function
7 199 1 177008 5 2712288 80 type
8 124 0 135328 4 2847616 84 dict of class
9 1045 4 83600 2 2931216 87 __builtin__.wrapper_descriptor
Running python code.py > code.log and the fgrep Partition code.log shows.
Partition of a set of 25914 objects. Total size = 3370200 bytes.
Partition of a set of 25924 objects. Total size = 3355832 bytes.
Partition of a set of 25924 objects. Total size = 3355728 bytes.
Partition of a set of 25924 objects. Total size = 3372568 bytes.
Partition of a set of 25924 objects. Total size = 3372736 bytes.
Partition of a set of 25924 objects. Total size = 3355752 bytes.
Partition of a set of 25924 objects. Total size = 3372592 bytes.
Partition of a set of 25924 objects. Total size = 3372760 bytes.
Partition of a set of 25924 objects. Total size = 3355776 bytes.
Partition of a set of 25924 objects. Total size = 3372616 bytes.
Which I believe shows that the memory footprint stays fairly consistent.
Granted I may be misinterpreting the results from guppy. Although during my tests I deliberately added a new string to a list to see if the object count increased and it did.
For those interested I had to install guppy like so on OSX - Mountain Lion
pip install https://guppy-pe.svn.sourceforge.net/svnroot/guppy-pe/trunk/guppy
In summary I don't think that it's a running out of memory issue although granted we're not using the full OP dataset.