Thats right, the dreaded two m’s. If you use C or C++, its quite dreaded. However, for about the first time ever, I ran into memory management issues in Python! Well, partially due to my own mistakes, and partially due to the way Python handles memory.

Memory handling schemes go awry

Well, not quite. More like, a leaky abstraction, compounded by my own idiot mistakes in the code. When memory is allocated in a loop, it is normally a smart optimization to hold onto that memory. What this means is that there are some cool things that you can do, like, refer to variables that were allocated inside a loop, and they will be in the local scope. They will just have the latest value thats been assigned to that variable.

However, in my case it backfired. The memory was held onto… and never reused. Essentially, I had a memory leak.

My own stupid mistakes

What happened, is that I was loading in a variable from a pickled file. However, what I had done was:

elements = pickle.load(open('file', 'rb'))

Normally, this would be okay, however, due to the size of the files, and the fact I was doing this in a loop, over different files, is that there was a now opened multi-megabyte file floating around, that has not been closed or reallocated. Actually, several. I came home after two hours to find that almost all of my RAM and 70% of my swap space had been used. Thats 70% of 3.8 GB. Yeah, great job Zeroth.

The lesson here is, that what works well for a small loop, or small files, does not neccesarilly work well for large files or large loops. Always make sure you close every file you open, and if worst comes to worst, allocate memory by using the del keyword in Python. I’m using this, and there is very moderate memory growth, as compared to the quite massive almost 6 GB of memory the code was taking up before.

So the proper way to do this is:

f=open('file', 'rb')
elements = pickle.load(f)
f.close()
del f

I do need to make this clear. This fault was not due to Python, or any problems or bugs in it, but rather with my own sloppy code. Even the best of us make mistakes, and I’m definitely not the best of us.

A new installment in my ever-popular series.

Todays installment will concern one of the best performance enhancing tricks in python: list comprehensions.

Most beginning pythonistas will produce code like this:

lst=[]
for i in range(0, 10):
     lst.append(i**2)

Read more

I’ve spent almost five years programming as a hobby, and my first language was Python. Its still one of my favourites for many reasons; simplicity, power, rapid development, and its fun.

Due to its nature, Python is very flexible and very versatile, and over the years, I’ve learned some very very cool and useful python idioms and tricks.

What I will show today is a Design Pattern that fits the ideology of Python well, and delivers a great amount of flexibility and usefulness. Its called the Borg Design Pattern. Read more