Python getting meaningful results from cProfile

Question

I have a Python script in a file which takes just over 30 seconds to run. I am trying to profile it as I would like to cut down this time dramatically.

I am trying to profile the script using cProfile, but essentially all it seems to be telling me is that yes, the main script took a long time to run, but doesn't give the kind of breakdown I was expecting. At the terminal, I type something like:

cat my_script_input.txt | python -m cProfile -s time my_script.py 

The results I get are:

<my_script_output>              683121 function calls (682169 primitive calls) in 32.133 seconds     Ordered by: internal time     ncalls  tottime  percall  cumtime  percall filename:lineno(function)         1   31.980   31.980   32.133   32.133 my_script.py:18(<module>)    121089    0.050    0.000    0.050    0.000 {method 'split' of 'str' objects}    121090    0.038    0.000    0.049    0.000 fileinput.py:243(next)         2    0.027    0.014    0.036    0.018 {method 'sort' of 'list' objects}    121089    0.009    0.000    0.009    0.000 {method 'strip' of 'str' objects}    201534    0.009    0.000    0.009    0.000 {method 'append' of 'list' objects}    100858    0.009    0.000    0.009    0.000 my_script.py:51(<lambda>)       952    0.008    0.000    0.008    0.000 {method 'readlines' of 'file' objects}  1904/952    0.003    0.000    0.011    0.000 fileinput.py:292(readline)     14412    0.001    0.000    0.001    0.000 {method 'add' of 'set' objects}       182    0.000    0.000    0.000    0.000 {method 'join' of 'str' objects}         1    0.000    0.000    0.000    0.000 fileinput.py:80(<module>)         1    0.000    0.000    0.000    0.000 fileinput.py:197(__init__)         1    0.000    0.000    0.000    0.000 fileinput.py:266(nextfile)         1    0.000    0.000    0.000    0.000 {isinstance}         1    0.000    0.000    0.000    0.000 fileinput.py:91(input)         1    0.000    0.000    0.000    0.000 fileinput.py:184(FileInput)         1    0.000    0.000    0.000    0.000 fileinput.py:240(__iter__)         1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects} 

This doesn't seem to be telling me anything useful. The vast majority of the time is simply listed as:

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)         1   31.980   31.980   32.133   32.133 my_script.py:18(<module>) 

In my_script.py, Line 18 is nothing more than the closing """ of the file's header block comment, so it's not that there is a whole load of work concentrated in Line 18. The script as a whole is mostly made up of line-based processing with mostly some string splitting, sorting and set work, so I was expecting to find the majority of time going to one or more of these activities. As it stands, seeing all the time grouped in cProfile's results as occurring on a comment line doesn't make any sense or at least does not shed any light on what is actually consuming all the time.

EDIT: I've constructed a minimum working example similar to my above case to demonstrate the same behavior:

mwe.py

import fileinput  for line in fileinput.input():     for i in range(10):         y = int(line.strip()) + int(line.strip()) 

And call it with:

perl -e 'for(1..1000000){print "$_\n"}' | python -m cProfile -s time mwe.py 

To get the result:

         22002536 function calls (22001694 primitive calls) in 9.433 seconds     Ordered by: internal time     ncalls  tottime  percall  cumtime  percall filename:lineno(function)         1    8.004    8.004    9.433    9.433 mwe.py:1(<module>)  20000000    1.021    0.000    1.021    0.000 {method 'strip' of 'str' objects}   1000001    0.270    0.000    0.301    0.000 fileinput.py:243(next)   1000000    0.107    0.000    0.107    0.000 {range}       842    0.024    0.000    0.024    0.000 {method 'readlines' of 'file' objects}  1684/842    0.007    0.000    0.032    0.000 fileinput.py:292(readline)         1    0.000    0.000    0.000    0.000 fileinput.py:80(<module>)         1    0.000    0.000    0.000    0.000 fileinput.py:91(input)         1    0.000    0.000    0.000    0.000 fileinput.py:197(__init__)         1    0.000    0.000    0.000    0.000 fileinput.py:184(FileInput)         1    0.000    0.000    0.000    0.000 fileinput.py:266(nextfile)         1    0.000    0.000    0.000    0.000 {isinstance}         1    0.000    0.000    0.000    0.000 fileinput.py:240(__iter__)         1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects} 

Am I using cProfile incorrectly somehow?

Answer 1

As I mentioned in a comment, when you can't get cProfile to work externally, you can often use it internally instead. It's not that hard.

For example, when I run with -m cProfile in my Python 2.7, I get effectively the same results you did. But when I manually instrument your example program:

import fileinput import cProfile  pr = cProfile.Profile() pr.enable() for line in fileinput.input():     for i in range(10):         y = int(line.strip()) + int(line.strip()) pr.disable() pr.print_stats(sort='time') 

… here's what I get:

         22002533 function calls (22001691 primitive calls) in 3.352 seconds     Ordered by: internal time     ncalls  tottime  percall  cumtime  percall filename:lineno(function)  20000000    2.326    0.000    2.326    0.000 {method 'strip' of 'str' objects}   1000001    0.646    0.000    0.700    0.000 fileinput.py:243(next)   1000000    0.325    0.000    0.325    0.000 {range}       842    0.042    0.000    0.042    0.000 {method 'readlines' of 'file' objects}  1684/842    0.013    0.000    0.055    0.000 fileinput.py:292(readline)         1    0.000    0.000    0.000    0.000 fileinput.py:197(__init__)         1    0.000    0.000    0.000    0.000 fileinput.py:91(input)         1    0.000    0.000    0.000    0.000 {isinstance}         1    0.000    0.000    0.000    0.000 fileinput.py:266(nextfile)         1    0.000    0.000    0.000    0.000 fileinput.py:240(__iter__)         1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects} 

That's a lot more useful: It tells you what you probably already expected, that more than half your time is spent calling str.strip().

---

Also, note that if you can't edit the file containing code you wish to profile (mwe.py), you can always do this:

import cProfile pr = cProfile.Profile() pr.enable() import mwe pr.disable() pr.print_stats(sort='time') 

---

Even that doesn't always work. If your program calls exit(), for example, you'll have to use a try:/finally: wrapper and/or an atexit. And it it calls os._exit(), or segfaults, you're probably completely hosed. But that isn't very common.

---

However, something I discovered later: If you move all code out of the global scope, -m cProfile seems to work, at least for this case. For example:

import fileinput  def f():     for line in fileinput.input():         for i in range(10):             y = int(line.strip()) + int(line.strip()) f() 

Now the output from -m cProfile includes, among other things:

2000000 4.819 0.000 4.819 0.000 :0(strip) 100001 0.288 0.000 0.295 0.000 fileinput.py:243(next)

I have no idea why this also made it twice as slow… or maybe that's just a cache effect; it's been a few minutes since I last ran it, and I've done lots of web browsing in between. But that's not important, what's important is that most of the time is getting charged to reasonable places.

But if I change this to move the outer loop to the global level, and only its body into a function, most of the time disappears again.

---

Another alternative, which I wouldn't suggest except as a last resort…

I notice that if I use profile instead of cProfile, it works both internally and externally, charging time to the right calls. However, those calls are also about 5x slower. And there seems to be an additional 10 seconds of constant overhead (which gets charged to import profile if used internally, whatever's on line 1 if used externally). So, to find out that split is using 70% of my time, instead of waiting 4 seconds and doing 2.326 / 3.352, I have to wait 27 seconds, and do 10.93 / (26.34 - 10.01). Not much fun…

---

One last thing: I get the same results with a CPython 3.4 dev build—correct results when used internally, everything charged to the first line of code when used externally. But PyPy 2.2/2.7.3 and PyPy3 2.1b1/3.2.3 both seem to give me correct results with -m cProfile. This may just mean that PyPy's cProfile is faked on top of profile because the pure-Python code is fast enough.

---

Anyway, if someone can figure out/explain why -m cProfile isn't working, that would be great… but otherwise, this is usually a perfectly good workaround.