TL;DR use buffers to not use tons of memory.
We get to the crux of your problem, I believe, when we consider the memory implications of working with very large files. We don’t want this bad boy to churn through 2 gigs of ram for a 2 gigabyte file so, as pasztorpisti points out, we gotta deal with those bigger files in chunks!
import sys
import hashlib
# BUF_SIZE is totally arbitrary, change for your app!
BUF_SIZE = 65536 # lets read stuff in 64kb chunks!
md5 = hashlib.md5()
sha1 = hashlib.sha1()
with open(sys.argv[1], 'rb') as f:
while True:
data = f.read(BUF_SIZE)
if not data:
break
md5.update(data)
sha1.update(data)
print("MD5: {0}".format(md5.hexdigest()))
print("SHA1: {0}".format(sha1.hexdigest()))
What we’ve done is we’re updating our hashes of this bad boy in 64kb chunks as we go along with hashlib’s handy dandy update method. This way we use a lot less memory than the 2gb it would take to hash the guy all at once!
You can test this with:
$ mkfile 2g bigfile
$ python hashes.py bigfile
MD5: a981130cf2b7e09f4686dc273cf7187e
SHA1: 91d50642dd930e9542c39d36f0516d45f4e1af0d
$ md5 bigfile
MD5 (bigfile) = a981130cf2b7e09f4686dc273cf7187e
$ shasum bigfile
91d50642dd930e9542c39d36f0516d45f4e1af0d bigfile
Hope that helps!
Also all of this is outlined in the linked question on the right hand side: Get MD5 hash of big files in Python
Addendum!
In general when writing python it helps to get into the habit of following pep-8. For example, in python variables are typically underscore separated not camelCased. But that’s just style and no one really cares about those things except people who have to read bad style… which might be you reading this code years from now.