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Safari | Python Developer's Handbook -> Profiling Python





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Profiling Python

Profiling an application means to be able to sketch an image about what is going on behind the scenes when you execute a program.

The sys module is able to perform a very simple profiling task by telling you a little bit about what is going on after each function, method, or specific line gets executed.

sys.setprofiler(profiler_function) This function implements a source code profiler, which identifies a function that must be executed whenever a function or method is called.

sys.settrace(tracer_function) The functionality of this function is basically the same one of the setprofiler() function. However, this one is called whenever a new line is executed.

					
>>> import sys
>>> def profiler(frame, event, arguments):
...     print frame.f_code.co_name, frame.f_lineno, event, arguments
...
>>> sys.setprofile(profiler)
? 1 return None
>>> lst = ["Spam","Parrot","Knights"]
? 1 call None
? 1 return None
>>> def showlist(_lst):
...     for l in _lst:
...         print l
...     return _lst
...
? 1 call None
? 1 return None
>>> showlist(lst)
? 1 call None
showlist 1 call None
Spam
Parrot
Knights
showlist 4 return ['Spam', 'Parrot', 'Knights']
['Spam', 'Parrot', 'Knights']
? 1 return None
>>> sys.setprofile(None)

				

If you really want to perform a more complete and accurate study, you need to use the profiler module.

Python Profiler

The information provided here offers a brief overview about how to use the profile module to perform the analysis of the run time performance of a Python program. The original profile module was written by Sjoerd Mullender, and later Guido van Rossum applied some changes to it. All the original documentation is copyrighted by James Roskind (see copyright note in Appendix C, "Python Copyright Notices)" , and reproduced here with slight modifications.

Note

Check out the module's original documentation for more information about its "deterministic profiling" implementation.



You have two possible ways to use the profile module. The first option is to import it, and make it call a function on your program that you want to analyze, such as

						
import profile
def main():
    for n in xrange(100):
        print n,
profile.run("main()")

					

The run() function generates a profiling report that can be manipulated using the pstats module (the report generating functions are in the pstats module).

The second option is to invoke the profiler as a main program and pass the script that needs to be profiled as an argument.

						
python profile.py scriptfile [arg...]

					

Next, you have the static member functions that are available for the profiler class. Note that an instance of Profile() is not needed to call them.

To profile an application with a main entry point of foo(), you would add the following to your module:

						
import profile
profile.run("foo()")

					

The previous action would cause foo() to be run, and a series of informative lines (the profile) to be printed. This approach is most useful when working with the interpreter. If you would like to save the results of a profile into a file for later examination, you can supply a filename as the second argument to the run() function:

						
import profile
profile.run("foo()", 'fooprof')

					

The primary entry point for the profiler is the global function profile.run(). It is typically used to create any profile information. The reports are formatted and printed using methods for the class pstats.Stats. The following is a description of all these standard entry points and functions. For a more in-depth view of some of the code, consider reading the later section on "Extensions: Deriving Better Profilers," which includes a discussion of how to derive better profilers from the classes presented, or reading the source code for these modules.

FUNCTION profile.run(string, filename_opt)

This function takes a single argument that can be passed to the exec statement, and an optional filename. In all cases, this routine attempts to exec its first argument, and gathers profiling statistics from the execution. If no filename is present, this function automatically prints a simple profiling report, sorted by the standard name string (file/line/function-name) that is presented in each line. The following is a typical output from such a call:

						
main()
2706 function calls (2004 primitive calls) in 4.504 CPU seconds

Ordered by: standard name

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     2    0.006    0.003    0.953    0.477 pobject.py:75(save_objects)
  43/3    0.533    0.012    0.749    0.250 pobject.py:99(evaluate)
...

					

The first line indicates that this profile was generated by the call: profile.run('main()'), and hence the executed string is 'main()'. The second line indicates that 2706 calls were monitored. Of those calls, 2004 were primitive. We define primitive to mean that the call was not induced via recursion. The next line, Ordered by: standard name, indicates that the text string in the far right column was used to sort the output. The column headings include

ncalls stands for the number of calls.

tottime stands for the total time spent in the given function ... ... (Not finished, please download and read the complete file)

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