Migration¶

Migrating from profile to profiling.tracing is straightforward. The APIs are compatible:

# Old (deprecated)
import profile
profile.run('my_function()')

# New (recommended)
import profiling.tracing
profiling.tracing.run('my_function()')

For most code, replacing import profile with import profiling.tracing (and using profiling.tracing instead of profile throughout) provides a straightforward migration path.

profile and profiling.tracing module reference¶

Both the profile and profiling.tracing modules provide the following functions:

profile.run(command, filename=None, sort=-1)¶

This function takes a single argument that can be passed to the exec() function, and an optional file name. In all cases this routine executes:

exec(command, __main__.__dict__, __main__.__dict__)

and gathers profiling statistics from the execution. If no file name is present, then this function automatically creates a Stats instance and prints a simple profiling report. If the sort value is specified, it is passed to this Stats instance to control how the results are sorted.

profile.runctx(command, globals, locals, filename=None, sort=-1)¶

This function is similar to run(), with added arguments to supply the globals and locals mappings for the command string. This routine executes:

exec(command, globals, locals)

and gathers profiling statistics as in the run() function above.

class profile.Profile(timer=None, timeunit=0.0, subcalls=True, builtins=True)¶

This class is normally only used if more precise control over profiling is needed than what the profiling.tracing.run() function provides.

A custom timer can be supplied for measuring how long code takes to run via the timer argument. This must be a function that returns a single number representing the current time. If the number is an integer, the timeunit specifies a multiplier that specifies the duration of each unit of time. For example, if the timer returns times measured in thousandths of seconds, the time unit would be .001.

Directly using the Profile class allows formatting profile results without writing the profile data to a file:

import profiling.tracing
import pstats
import io
from pstats import SortKey

pr = profiling.tracing.Profile()
pr.enable()
# ... do something ...
pr.disable()
s = io.StringIO()
sortby = SortKey.CUMULATIVE
ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
print(s.getvalue())

The Profile class can also be used as a context manager (supported only in profiling.tracing, not in the deprecated profile module; see Context Manager Types):

import profiling.tracing

with profiling.tracing.Profile() as pr:
    # ... do something ...

    pr.print_stats()

Changed in version 3.8: Added context manager support.

enable()¶

Start collecting profiling data. Only in profiling.tracing.

disable()¶

Stop collecting profiling data. Only in profiling.tracing.

create_stats()¶

Stop collecting profiling data and record the results internally as the current profile.

print_stats(sort=-1)¶

Create a Stats object based on the current profile and print the results to stdout.

The sort parameter specifies the sorting order of the displayed statistics. It accepts a single key or a tuple of keys to enable multi-level sorting, as in pstats.Stats.sort_stats().

Added in version 3.13: print_stats() now accepts a tuple of keys.

dump_stats(filename)¶

Write the results of the current profile to filename.

run(cmd)¶

Profile the cmd via exec().

runctx(cmd, globals, locals)¶

Profile the cmd via exec() with the specified global and local environment.

runcall(func, /, *args, **kwargs)¶

Profile func(*args, **kwargs)

Note that profiling will only work if the called command/function actually returns. If the interpreter is terminated (e.g. via a sys.exit() call during the called command/function execution) no profiling results will be printed.

Differences from profiling.tracing¶

The profile module differs from profiling.tracing in several ways:

Higher overhead. The pure Python implementation is significantly slower than the C implementation, making it unsuitable for profiling long-running programs or performance-sensitive code.

Calibration support. The profile module supports calibration to compensate for profiling overhead. This is not needed in profiling.tracing because the C implementation has negligible overhead.

Custom timers. Both modules support custom timers, but profile accepts timer functions that return tuples (like os.times()), while profiling.tracing requires a function returning a single number.

Subclassing. The pure Python implementation is easier to subclass and extend for custom profiling behavior.

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 provided by the profiling.sampling module) periodically 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 in order 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.

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 the deprecated profile module than with the lower-overhead profiling.tracing. 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 Limitations).

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 a 1.8Ghz Intel Core i5 running macOS, and using Python’s time.process_time() as the timer, the magical number is about 4.04e-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:

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.

Using a custom timer¶

If you want to 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. Depending on whether you are using profile.Profile or profiling.tracing.Profile, your_time_func’s return value will be interpreted differently:

profile.Profile

your_time_func should return a single number, or a list of numbers whose sum is the current time (like what os.times() returns). If the function returns a single time number, or the list of returned numbers has length 2, then you will get an especially fast version of the dispatch routine.

Be warned that you should calibrate the profiler class for the timer function that you choose (see Calibration). For most machines, a timer that returns a lone integer value will provide the best results in terms of low overhead during profiling. (os.times() is pretty bad, as it returns a tuple of floating-point values). If you want to substitute a better timer in the cleanest fashion, derive a class and hardwire a replacement dispatch method that best handles your timer call, along with the appropriate calibration constant.

profiling.tracing.Profile

your_time_func should return a single number. If it returns integers, you can also invoke the class constructor with a second argument specifying the real duration of one unit of time. For example, if your_integer_time_func returns times measured in thousandths of seconds, you would construct the Profile instance as follows:

pr = profiling.tracing.Profile(your_integer_time_func, 0.001)

As the profiling.tracing.Profile class cannot be calibrated, custom timer functions should be used with care and should be as fast as possible. For the best results with a custom timer, it might be necessary to hard-code it in the C source of the internal _lsprof module.

Python 3.3 adds several new functions in time that can be used to make precise measurements of process or wall-clock time. For example, see time.perf_counter().