NavigationThe Python Profilers¶Copyright © 1994, by InfoSeek Corporation, all rights reserved. Written by James Roskind. [1] Permission to use, copy, modify, and distribute this Python software and its associated documentation for any purpose (subject to the restriction in the following sentence) without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of InfoSeek not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. This permission is explicitly restricted to the copying and modification of the software to remain in Python, compiled Python, or other languages (such as C) wherein the modified or derived code is exclusively imported into a Python module. INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Introduction to the profilers¶A profiler is a program that describes the run time performance of a program, providing a variety of statistics. This documentation describes the profiler functionality provided in the modules cProfile, profile and pstats. This profiler provides deterministic profiling of Python programs. It also provides a series of report generation tools to allow users to rapidly examine the results of a profile operation. The Python standard library provides three different profilers:
The profile and cProfile modules export the same interface, so they are mostly interchangeable; cProfile has a much lower overhead but is newer and might not be available on all systems. cProfile is really a compatibility layer on top of the internal _lsprof module. The hotshot module is reserved for specialized usage. Instant User’s Manual¶This section is provided for users that “don’t want to read the manual.” It provides a very brief overview, and allows a user to rapidly perform profiling on an existing application. To profile an application with a main entry point of foo(), you would add the following to your module: import cProfile
cProfile.run('foo()')
(Use profile instead of cProfile if the latter is not available on your system.) The above action would cause foo() to be run, and a series of informative lines (the profile) to be printed. The above 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 file name as the second argument to the run() function: import cProfile
cProfile.run('foo()', 'fooprof')
The file cProfile.py can also be invoked as a script to profile another script. For example: python -m cProfile myscript.py cProfile.py accepts two optional arguments on the command line: cProfile.py [-o output_file] [-s sort_order] -s only applies to standard output (-o is not supplied). Look in the Stats documentation for valid sort values. When you wish to review the profile, you should use the methods in the pstats module. Typically you would load the statistics data as follows: import pstats
p = pstats.Stats('fooprof')
The class Stats (the above code just created an instance of this class) has a variety of methods for manipulating and printing the data that was just read into p. When you ran cProfile.run() above, what was printed was the result of three method calls: p.strip_dirs().sort_stats(-1).print_stats()
The first method removed the extraneous path from all the module names. The second method sorted all the entries according to the standard module/line/name string that is printed. The third method printed out all the statistics. You might try the following sort calls: p.sort_stats('name')
p.print_stats()
The first call will actually sort the list by function name, and the second call will print out the statistics. The following are some interesting calls to experiment with: p.sort_stats('cumulative').print_stats(10)
This sorts the profile by cumulative time in a function, and then only prints the ten most significant lines. If you want to understand what algorithms are taking time, the above line is what you would use. If you were looking to see what functions were looping a lot, and taking a lot of time, you would do: p.sort_stats('time').print_stats(10)
to sort according to time spent within each function, and then print the statistics for the top ten functions. You might also try: p.sort_stats('file').print_stats('__init__')
This will sort all the statistics by file name, and then print out statistics for only the class init methods (since they are spelled with __init__ in them). As one final example, you could try: p.sort_stats('time', 'cum').print_stats(.5, 'init')
This line sorts statistics with a primary key of time, and a secondary key of cumulative time, and then prints out some of the statistics. To be specific, the list is first culled down to 50% (re: .5) of its original size, then only lines containing init are maintained, and that sub-sub-list is printed. If you wondered what functions called the above functions, you could now (p is still sorted according to the last criteria) do: p.print_callers(.5, 'init')
and you would get a list of callers for each of the listed functions. If you want more functionality, you’re going to have to read the manual, or guess what the following functions do: p.print_callees()
p.add('fooprof')
Invoked as a script, the pstats module is a statistics browser for reading and examining profile dumps. It has a simple line-oriented interface (implemented using cmd) and interactive help. What Is Deterministic Profiling?¶Deterministic profiling is meant to reflect the fact that all function call, function return, and exception events are monitored, and precise timings are made for the intervals between these events (during which time the user’s code is executing). In contrast, statistical profiling (which is not done by this module) randomly samples the effective instruction pointer, and deduces where time is being spent. The latter technique traditionally involves less overhead (as the code does not need to be instrumented), but provides only relative indications of where time is being spent. In Python, since there is an interpreter active during execution, the presence of instrumented code is not required to do deterministic profiling. Python automatically provides a hook (optional callback) for each event. In addition, the interpreted nature of Python tends to add so much overhead to execution, that deterministic profiling tends to only add small processing overhead in typical applications. The result is that deterministic profiling is not that expensive, yet provides extensive run time statistics about the execution of a Python program. Call count statistics can be used to identify bugs in code (surprising counts), and to identify possible inline-expansion points (high call counts). Internal time statistics can be used to identify “hot loops” that should be carefully optimized. Cumulative time statistics should be used to identify high level errors in the selection of algorithms. Note that the unusual handling of cumulative times in this profiler allows statistics for recursive implementations of algorithms to be directly compared to iterative implementations. Reference Manual – profile and cProfile¶The primary entry point for the profiler is the global function profile.run() (resp. cProfile.run()). It is typically used to create any profile information. The reports are formatted and printed using methods of the class pstats.Stats. The following is a description of all of these standard entry points and functions. For a more in-depth view of some of the code, consider reading the later section on Profiler Extensions, which includes discussion of how to derive “better” profilers from the classes presented, or reading the source code for these modules.
Analysis of the profiler data is done using the Stats class. Note The Stats class is defined in the pstats module.
The Stats Class¶Stats objects have the following methods:
Limitations¶One limitation has to do with accuracy of timing information. There is a fundamental problem with deterministic profilers involving accuracy. The most obvious restriction is that the underlying “clock” is only ticking at a rate (typically) of about .001 seconds. Hence no measurements will be more accurate than the underlying clock. If enough measurements are taken, then the “error” will tend to average out. Unfortunately, removing this first error induces a second source of error. The second problem is that it “takes a while” from when an event is dispatched until the profiler’s call to get the time actually gets the state of the clock. Similarly, there is a certain lag when exiting the profiler event handler from the time that the clock’s value was obtained (and then squirreled away), until the user’s code is once again executing. As a result, functions that are called many times, or call many functions, will typically accumulate this error. The error that accumulates in this fashion is typically less than the accuracy of the clock (less than one clock tick), but it can accumulate and become very significant. The problem is more important with profile than with the lower-overhead cProfile. For this reason, profile provides a means of calibrating itself for a given platform so that this error can be probabilistically (on the average) removed. After the profiler is calibrated, it will be more accurate (in a least square sense), but it will sometimes produce negative numbers (when call counts are exceptionally low, and the gods of probability work against you :-). ) Do not be alarmed by negative numbers in the profile. They should only appear if you have calibrated your profiler, and the results are actually better than without calibration. Calibration¶The profiler of the profile module subtracts a constant from each event handling time to compensate for the overhead of calling the time function, and socking away the results. By default, the constant is 0. The following procedure can be used to obtain a better constant for a given platform (see discussion in section Limitations above). import profile
pr = profile.Profile()
for i in range(5):
print pr.calibrate(10000)
The method executes the number of Python calls given by the argument, directly and again under the profiler, measuring the time for both. It then computes the hidden overhead per profiler event, and returns that as a float. For example, on an 800 MHz Pentium running Windows 2000, and using Python’s time.clock() as the timer, the magical number is about 12.5e-6. The object of this exercise is to get a fairly consistent result. If your computer is very fast, or your timer function has poor resolution, you might have to pass 100000, or even 1000000, to get consistent results. When you have a consistent answer, there are three ways you can use it: [2] import profile
# 1. Apply computed bias to all Profile instances created hereafter.
profile.Profile.bias = your_computed_bias
# 2. Apply computed bias to a specific Profile instance.
pr = profile.Profile()
pr.bias = your_computed_bias
# 3. Specify computed bias in instance constructor.
pr = profile.Profile(bias=your_computed_bias)
If you have a choice, you are better off choosing a smaller constant, and then your results will “less often” show up as negative in profile statistics. Extensions — Deriving Better Profilers¶The Profile class of both modules, profile and cProfile, were written so that derived classes could be developed to extend the profiler. The details are not described here, as doing this successfully requires an expert understanding of how the Profile class works internally. Study the source code of the module carefully if you want to pursue this. If all you want to do is change how current time is determined (for example, to force use of wall-clock time or elapsed process time), pass the timing function you want to the Profile class constructor: pr = profile.Profile(your_time_func)
The resulting profiler will then call your_time_func().
Footnotes
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