Quick And Easy Execution Speed Testing

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There have been many times when I've been programming, encounter a problem that probably involves a loop of some sort, and I think of two or more possible ways to achieve the same end result. At this point, I usually think about which one will probably be the fastest solution (execution-wise) while still being readable/maintainable. A lot of the time, the essentials of the problem can be tested in a few short lines of code.

A while back, I was perusing some Stack Overflow questions for work, and I stumbled upon what I consider one of the many hidden jewels in Python: the timeit module. Given a bit of code, this little guy will handle executing it in several loops and giving you the best time out of three trials (you can ask it to do more than 3 runs if you want). Once it completes its test, it will offer some very clean and useful output.

For example, today I encountered a piece of code that was making a comma-separated list of an arbitrary number of "%s". The code I saw essentially looked like this:

",".join(["%s"] * 50000)

Even though this code required no optimization, I thought, "Hey, that's neat... I wonder if a list comprehension could possibly be any faster." Here's an example of the contender:

",".join(["%s" for i in xrange(50000)])

I had no idea which would be faster, so timeit to the rescue!! Open up a terminal, type a couple one-line Python commands, and enjoy the results!

$ python -mtimeit 'l = ",".join(["%s"] * 50000)'
1000 loops, best of 3: 1.15 msec per loop
$ python -mtimeit 'l = ",".join(["%s" for i in xrange(50000)])'
100 loops, best of 3: 3.23 msec per loop

Hah, the list comprehension is certainly slower.

Now, for other more in-depth tests of performance, you might consider using the cProfile module. As far as I can tell, simple one-liners can't be tested directly from the command line using cProfile--they apparently need to be in a script. You can use something like:

python -mcProfile script.py

...in such situations. Or you can wrap function calls using cProfile.run():

import cProfile

def function_a():
    # something you want to profile

def function_b():
    # an alternative version of function_a to profile

if __name__ == '__main__':

I've used this technique for tests that I'd like to have "hard evidence" for in the future. The output of such a cProfile test looks something like this:

3 function calls in 6.860 CPU seconds

Ordered by: standard name

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1    0.000    0.000    6.860    6.860 <string>:1(<module>)
     1    6.860    6.860    6.860    6.860 test_enumerate.py:5(test_enumerate)
     1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

This is useful when your code is calling other functions or methods and you want to find where your bottlenecks are. Hooray for Python!

What profiling techniques do you use?

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