memory dump using Python

Question

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 1

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.

Answer 2

How about using IronPython and Visual Studio for its debug tools (which are pretty good)? You should be able to pause execution and look at the memory (essentially a memory dump).

Answer 3

To answer the above comment from @shaun if you want the file to output to notepad just run your file like so

Myfile.py >output.txt

If the text file doesn't exist it will be created.

EDIT:

Replace the line in your code at the bottom which reads:

print "" .join(item)

with this:

with open ("output.txt","a") as f:
    f.write('\n'.join(items))
f.close

which will produce a file called output.txt. Should work (haven't tested)

Answer 4

Your program will run pretty efficiently by itself, as you now know. But make sure you don't just run it in IDLE, for example; that will slow it down to a crawl as IDLE updates the screen with more and more lines. Save the output directly into a file.

Even better: Have you thought about what you'll do when you have the passwords? If you can log on to the lost account from the command line, try doing that immediately instead of storing all the passwords for later use:

for length in range(1, len(mylist)+1):
    for item in itertools.permutations(mylist, length):
        password = "".join(item)
        try_to_logon(command, password)