Dabeaz

I spend a lot of time studying different aspects of Python, different implementation techniques, and so forth. As part of that, I often carry out little performance benchmarks. For small things, I'll often use the timeit module. For example:

>>> from timeit import timeit
>>> timeit("math.sin(2)","import math")
0.29826998710632324
>>> timeit("sin(2)","from math import sin")
0.21983098983764648
>>>

However, for larger blocks of code, I tend to just use the time module directly like this:

import time
start = time.time()
...
... some big calculation
...
end = time.time()
print("Whatever : %0.3f seconds" % (end-start))

Having typed that particular code template more often than I care to admit, it occurred to me that I really ought to just make a context manager for doing it. Something like this:

# benchmark.py
import time
class benchmark(object):
    def __init__(self,name):
        self.name = name
    def __enter__(self):
        self.start = time.time()
    def __exit__(self,ty,val,tb):
        end = time.time()
        print("%s : %0.3f seconds" % (self.name, end-self.start))
        return False

Now, I can just use that context manager whenever I want to do that kind of timing benchmark. For example:

# fileitertest.py
from benchmark import benchmark

with benchmark("iterate by lines (UTF-8)"):
     for line in open("biglog.txt",encoding='utf-8'):
          pass

with benchmark("iterate by lines (Latin-1)"):
     for line in open("biglog.txt",encoding='latin-1'):
         pass

with benchmark("iterate by lines (Binary)"):
     for line in open("biglog.txt","rb"):
         pass

If you run it, you might get output like this:

bash % python3 fileitertest.py
iterate by lines (UTF-8) : 3.903 seconds
iterate by lines (Latin-1) : 3.615 seconds
iterate by lines (Binary) : 1.886 seconds

Nice. I like it already!

posted by Dave  # 5:01 AM