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Python: Memory Conservation Tip: intern()

I'm working with a lot of data, and running out of memory is a problem. When I read a line of data, I've often seen the same data before. Rather than have two pointers that point to two separate copies of "foo", I'd prefer to have two pointers that point to the same copy of "foo". This makes a lot of sense in Python since strings are immutable anyway.

I knew that this was called the flyweight design pattern, but I didn't know if it was already implemented somewhere in Python. (Strictly speaking, I thought it was called the "flywheel" design pattern, and my buddy Drew Perttula corrected me.)

My first attempt was to write code like:
>>> s1 = "foo"
>>> s2 = ''.join(['f', 'o', 'o'])
>>> s1 == s2
True
>>> s1 is s2
False
>>> identity_cache = {}
>>> s1 = identity_cache.setdefault(s1, s1)
>>> s2 = identity_cache.setdefault(s2, s2)
>>> s1 == 'foo'
True
>>> s1 == s2
True
>>> s1 is s2
True
This code looks up the word "foo" by value and returns the same instance every time. Notice, it works.

However, Monte Davidoff pointed out that this is what the intern builtin is for. From the docs:
Enter string in the table of ``interned'' strings and return the interned string - which is string itself or a copy. Interning strings is useful to gain a little performance on dictionary lookup - if the keys in a dictionary are interned, and the lookup key is interned, the key comparisons (after hashing) can be done by a pointer compare instead of a string compare. Normally, the names used in Python programs are automatically interned, and the dictionaries used to hold module, class or instance attributes have interned keys. Changed in version 2.3: Interned strings are not immortal (like they used to be in Python 2.2 and before); you must keep a reference to the return value of intern() around to benefit from it.
Here it is in action:
>>> s1 = "foo"
>>> s2 = ''.join(['f', 'o', 'o'])
>>> s1 == s2
True
>>> s1 is s2
False
>>> s1 = intern(s1)
>>> s2 = intern(s2)
>>> s1 == 'foo'
True
>>> s1 == s2
True
>>> s1 is s2
True
Well did it work? My program still functions, but I didn't get a tremendous savings in memory. It turns out that I don't have enough dups, and that's not where I'm spending all my memory anyway. Oh well, at least I learned about the intern() function.

Comments

Alec said…
This is awesome jj - I didn't know about intern but I've really wanted it lately... I hope it's reasonably effective on jython cuz I'm in the same out of memory issue in our hadoop/jython cluster
Varikin said…
I don't have much deep python experience, I am coming from a Java background, but I really want to learn more about python. Do you know if it has any garbage collection (gc) logging like Java? For example, in Java, I can enable gc logging, take thread dumps, and then look for memory leaks or high consumption. I have done a fair bit of debugging/performance tuning in Java this way, but I am wondering if Python offers these options.
Doug Napoleone said…
@varikin

There is a garbage collection module (called 'gc') and gc.collect() can force (or at least strongly hint) to do a collection with circular issues.

From experience, this usually only postpones the problems a little.

For logging the best means is to just use the hotshot profiler.

If you have large arrays of primitive types I would recommend using the array package from SciPy (www.enthought.com). We were able to reduce the memory by over an order of magnitude just by having 5 arrays (3 int, 1 float, and 1 string) instead of a class with 5 attributes. 3Gig went to 250Meg.

There are some other tricks if you have many, many small objects. Read up on __slots__. (There are some nasty side effects however...)
Anonymous said…
this is useful and nice trick. Thanks so much :D, I think i will be using like so
Anonymous said…
So what's next for your program? I want to hear about cool compression schemes that let you work on your data while it's still compressed (like finding and factoring out string prefixes).
jjinux said…
I have one more that you might like, but it involves a longer blog post ;)
jjinux said…
Nice tips. Thanks, Doug.

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